2024-08-06T20:17:36.5316237Z Current runner version: '2.318.0' 2024-08-06T20:17:36.5323485Z Runner name: 'i-03096d6fe1daee476' 2024-08-06T20:17:36.5324362Z Runner group name: 'Default' 2024-08-06T20:17:36.5325454Z Machine name: 'ip-10-0-6-243' 2024-08-06T20:17:36.5330002Z ##[group]GITHUB_TOKEN Permissions 2024-08-06T20:17:36.5332876Z Actions: read 2024-08-06T20:17:36.5333580Z Attestations: read 2024-08-06T20:17:36.5334249Z Checks: read 2024-08-06T20:17:36.5334825Z Contents: read 2024-08-06T20:17:36.5335452Z Deployments: read 2024-08-06T20:17:36.5336109Z Discussions: read 2024-08-06T20:17:36.5336684Z Issues: read 2024-08-06T20:17:36.5337297Z Metadata: read 2024-08-06T20:17:36.5337932Z Packages: read 2024-08-06T20:17:36.5338493Z Pages: read 2024-08-06T20:17:36.5339093Z PullRequests: read 2024-08-06T20:17:36.5340202Z RepositoryProjects: read 2024-08-06T20:17:36.5340870Z SecurityEvents: read 2024-08-06T20:17:36.5341524Z Statuses: read 2024-08-06T20:17:36.5342151Z ##[endgroup] 2024-08-06T20:17:36.5345679Z Secret source: Actions 2024-08-06T20:17:36.5346871Z Prepare workflow directory 2024-08-06T20:17:36.8451509Z Prepare all required actions 2024-08-06T20:17:36.8495873Z Getting action download info 2024-08-06T20:17:37.0013737Z Download action repository 'pytorch/test-infra@main' (SHA:a1f5a89251fc4258ab59806094fe3108f7d6741a) 2024-08-06T20:17:38.5907584Z Download action repository 'pytorch/pytorch@main' (SHA:de00c7958301ce81b9716bdef5731ed40d4d14ca) 2024-08-06T20:17:51.1582959Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-08-06T20:17:51.3749204Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-08-06T20:17:51.6633413Z Getting action download info 2024-08-06T20:17:51.7742828Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-08-06T20:17:52.0419776Z Getting action download info 2024-08-06T20:17:52.1361591Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-08-06T20:17:52.2991368Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/132710/merge (bf5bb5a1585a03379137fab341e87c02c77e76cd) 2024-08-06T20:17:52.2993457Z ##[group] Inputs 2024-08-06T20:17:52.2993937Z build-environment: linux-focal-py3.12-clang10-experimental-split-build 2024-08-06T20:17:52.2996032Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]} 2024-08-06T20:17:52.2998374Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:17:52.2999171Z sync-tag: 2024-08-06T20:17:52.2999959Z timeout-minutes: 600 2024-08-06T20:17:52.3000220Z use-gha: 2024-08-06T20:17:52.3000453Z dashboard-tag: 2024-08-06T20:17:52.3000713Z s3-bucket: gha-artifacts 2024-08-06T20:17:52.3000983Z aws-role-to-assume: 2024-08-06T20:17:52.3001250Z ##[endgroup] 2024-08-06T20:17:52.3001820Z Complete job name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, amz2023.linux.2xlarge) 2024-08-06T20:17:52.3444208Z A job started hook has been configured by the self-hosted runner administrator 2024-08-06T20:17:52.3548556Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2024-08-06T20:17:52.3557810Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:17:52.3558469Z ##[endgroup] 2024-08-06T20:17:54.0447656Z Runner Type: amz2023.linux.2xlarge 2024-08-06T20:17:54.0448102Z Instance Type: c5.2xlarge 2024-08-06T20:17:54.0448930Z AMI Name: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64 2024-08-06T20:17:54.0449363Z AMI ID: ami-06c68f701d8090592 2024-08-06T20:18:00.0332519Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2024-08-06T20:18:00.0332996Z with: 2024-08-06T20:18:00.0333795Z github-secret: *** 2024-08-06T20:18:00.0334546Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-08-06T20:18:00.0335345Z activate-with-label: false 2024-08-06T20:18:00.0335639Z label: with-ssh 2024-08-06T20:18:00.0335884Z remove-existing-keys: true 2024-08-06T20:18:00.0336172Z fail-silently: true 2024-08-06T20:18:00.0336409Z env: 2024-08-06T20:18:00.0336630Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:18:00.0336905Z ##[endgroup] 2024-08-06T20:18:00.1173073Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-08-06T20:18:00.4298965Z Grabbing public ssh keys from https://github.com/drisspg.keys 2024-08-06T20:18:00.5020102Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2024-08-06T20:18:00.5048029Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2024-08-06T20:18:00.5110646Z Login using: ssh ec2-user@ec2-54-81-62-230.compute-1.amazonaws.com 2024-08-06T20:18:00.5111335Z All testing is done inside the container, to start an interactive session run: 2024-08-06T20:18:00.5111910Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-08-06T20:18:00.5238328Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2024-08-06T20:18:00.5238823Z with: 2024-08-06T20:18:00.5239054Z submodules: recursive 2024-08-06T20:18:00.5239321Z fetch-depth: 0 2024-08-06T20:18:00.5239567Z env: 2024-08-06T20:18:00.5239775Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:18:00.5240048Z ##[endgroup] 2024-08-06T20:18:00.5332504Z ##[group]Run retry () { 2024-08-06T20:18:00.5332869Z retry () { 2024-08-06T20:18:00.5333212Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-08-06T20:18:00.5333620Z } 2024-08-06T20:18:00.5333890Z echo "${GITHUB_WORKSPACE}" 2024-08-06T20:18:00.5334250Z if [ -z "${NO_SUDO}" ]; then 2024-08-06T20:18:00.5334619Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-08-06T20:18:00.5334968Z else 2024-08-06T20:18:00.5335222Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-08-06T20:18:00.5335554Z fi 2024-08-06T20:18:00.5335850Z mkdir "${GITHUB_WORKSPACE}" 2024-08-06T20:18:00.5343683Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:18:00.5344093Z env: 2024-08-06T20:18:00.5344306Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:18:00.5344581Z NO_SUDO: 2024-08-06T20:18:00.5344803Z ##[endgroup] 2024-08-06T20:18:00.5372789Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-06T20:18:03.2487759Z ##[group]Run malfet/checkout@silent-checkout 2024-08-06T20:18:03.2488131Z with: 2024-08-06T20:18:03.2488385Z ref: b9d86fa89636e301796d4201f36d86c73f6e49bc 2024-08-06T20:18:03.2488710Z fetch-depth: 0 2024-08-06T20:18:03.2488960Z submodules: recursive 2024-08-06T20:18:03.2489227Z quiet-checkout: true 2024-08-06T20:18:03.2489487Z repository: pytorch/pytorch 2024-08-06T20:18:03.2489899Z token: *** 2024-08-06T20:18:03.2490128Z ssh-strict: true 2024-08-06T20:18:03.2490371Z persist-credentials: true 2024-08-06T20:18:03.2490648Z clean: true 2024-08-06T20:18:03.2491048Z sparse-checkout-cone-mode: true 2024-08-06T20:18:03.2491336Z lfs: false 2024-08-06T20:18:03.2491568Z set-safe-directory: true 2024-08-06T20:18:03.2491836Z env: 2024-08-06T20:18:03.2492039Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:18:03.2492302Z ##[endgroup] 2024-08-06T20:18:03.3552512Z Syncing repository: pytorch/pytorch 2024-08-06T20:18:03.3554713Z ##[group]Getting Git version info 2024-08-06T20:18:03.3555529Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-08-06T20:18:03.3557075Z [command]/usr/bin/git version 2024-08-06T20:18:03.3557541Z git version 2.40.1 2024-08-06T20:18:03.3560293Z ##[endgroup] 2024-08-06T20:18:03.3566097Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/5f3cd7d5-d45d-4a91-bb13-057a5e985b29' before making global git config changes 2024-08-06T20:18:03.3567842Z Adding repository directory to the temporary git global config as a safe directory 2024-08-06T20:18:03.3570844Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-06T20:18:03.3611768Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-08-06T20:18:03.3616339Z ##[group]Initializing the repository 2024-08-06T20:18:03.3619852Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-06T20:18:03.3764053Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-08-06T20:18:03.3764764Z hint: is subject to change. To configure the initial branch name to use in all 2024-08-06T20:18:03.3765358Z hint: of your new repositories, which will suppress this warning, call: 2024-08-06T20:18:03.3765813Z hint: 2024-08-06T20:18:03.3766126Z hint: git config --global init.defaultBranch 2024-08-06T20:18:03.3766517Z hint: 2024-08-06T20:18:03.3766941Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-08-06T20:18:03.3767882Z hint: 'development'. The just-created branch can be renamed via this command: 2024-08-06T20:18:03.3768989Z hint: 2024-08-06T20:18:03.3769384Z hint: git branch -m 2024-08-06T20:18:03.3770343Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-08-06T20:18:03.3777884Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-08-06T20:18:03.3818432Z ##[endgroup] 2024-08-06T20:18:03.3819159Z ##[group]Disabling automatic garbage collection 2024-08-06T20:18:03.3822405Z [command]/usr/bin/git config --local gc.auto 0 2024-08-06T20:18:03.3854122Z ##[endgroup] 2024-08-06T20:18:03.3854783Z ##[group]Setting up auth 2024-08-06T20:18:03.3861479Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-08-06T20:18:03.3894844Z [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' || :" 2024-08-06T20:18:03.4151375Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-08-06T20:18:03.4183822Z [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' || :" 2024-08-06T20:18:03.4443777Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-08-06T20:18:03.4493317Z ##[endgroup] 2024-08-06T20:18:03.4494158Z ##[group]Fetching the repository 2024-08-06T20:18:03.4500832Z [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/* 2024-08-06T20:18:06.0795875Z remote: Enumerating objects: 1008384 2024-08-06T20:18:06.0796338Z remote: Enumerating objects: 1008664, done. 2024-08-06T20:18:06.0797367Z remote: Counting objects: 0% (1/280) 2024-08-06T20:18:06.0797731Z remote: Counting objects: 1% (3/280) 2024-08-06T20:18:06.0798133Z remote: Counting objects: 2% (6/280) 2024-08-06T20:18:06.0798501Z remote: Counting objects: 3% (9/280) 2024-08-06T20:18:06.0798928Z remote: Counting objects: 4% (12/280) 2024-08-06T20:18:06.0799306Z remote: Counting objects: 5% (14/280) 2024-08-06T20:18:06.0799678Z remote: Counting objects: 6% (17/280) 2024-08-06T20:18:06.0800034Z remote: Counting objects: 7% (20/280) 2024-08-06T20:18:06.0800723Z remote: Counting objects: 8% (23/280) 2024-08-06T20:18:06.0801095Z remote: Counting objects: 9% (26/280) 2024-08-06T20:18:06.0801455Z remote: Counting objects: 10% (28/280) 2024-08-06T20:18:06.0801861Z remote: Counting objects: 11% (31/280) 2024-08-06T20:18:06.0802343Z remote: Counting objects: 12% (34/280) 2024-08-06T20:18:06.0802706Z remote: Counting objects: 13% (37/280) 2024-08-06T20:18:06.0803082Z remote: Counting objects: 14% (40/280) 2024-08-06T20:18:06.0803463Z 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remote: Counting objects: 73% (205/280) 2024-08-06T20:18:06.0827222Z remote: Counting objects: 74% (208/280) 2024-08-06T20:18:06.0827602Z remote: Counting objects: 75% (210/280) 2024-08-06T20:18:06.0827980Z remote: Counting objects: 76% (213/280) 2024-08-06T20:18:06.0828346Z remote: Counting objects: 77% (216/280) 2024-08-06T20:18:06.0828725Z remote: Counting objects: 78% (219/280) 2024-08-06T20:18:06.0829100Z remote: Counting objects: 79% (222/280) 2024-08-06T20:18:06.0829461Z remote: Counting objects: 80% (224/280) 2024-08-06T20:18:06.0830198Z remote: Counting objects: 81% (227/280) 2024-08-06T20:18:06.0830577Z remote: Counting objects: 82% (230/280) 2024-08-06T20:18:06.0830938Z remote: Counting objects: 83% (233/280) 2024-08-06T20:18:06.0831315Z remote: Counting objects: 84% (236/280) 2024-08-06T20:18:06.0831694Z remote: Counting objects: 85% (238/280) 2024-08-06T20:18:06.0832055Z remote: Counting objects: 86% (241/280) 2024-08-06T20:18:06.0832447Z remote: Counting objects: 87% 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2024-08-06T20:18:55.3924102Z ##[endgroup] 2024-08-06T20:18:55.3924573Z ##[group]Setting up auth for fetching submodules 2024-08-06T20:18:55.3927962Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-08-06T20:18:55.3976956Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-08-06T20:18:55.4007821Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-08-06T20:18:55.4037304Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-08-06T20:18:55.4063649Z ##[endgroup] 2024-08-06T20:18:55.4064054Z ##[group]Fetching submodules 2024-08-06T20:18:55.4067935Z [command]/usr/bin/git submodule sync --recursive 2024-08-06T20:18:55.4349094Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-08-06T20:18:55.4625867Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-08-06T20:18:55.4628106Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-08-06T20:18:55.4630389Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-08-06T20:18:55.4634068Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-08-06T20:18:55.4638309Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-08-06T20:18:55.4641221Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-08-06T20:18:55.4645496Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-08-06T20:18:55.4649463Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-08-06T20:18:55.4653751Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-08-06T20:18:55.4658231Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-08-06T20:18:55.4662323Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-08-06T20:18:55.4666410Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-08-06T20:18:55.4670711Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-08-06T20:18:55.4675365Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-08-06T20:18:55.4679731Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-08-06T20:18:55.4684317Z Submodule 'third_party/foxi' (https://github.com/houseroad/foxi.git) registered for path 'third_party/foxi' 2024-08-06T20:18:55.4690945Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-08-06T20:18:55.4695763Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-08-06T20:18:55.4700969Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-08-06T20:18:55.4705771Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-08-06T20:18:55.4711005Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-08-06T20:18:55.4716161Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-08-06T20:18:55.4721612Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-08-06T20:18:55.4727112Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-08-06T20:18:55.4732643Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-08-06T20:18:55.4738166Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-08-06T20:18:55.4745710Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-08-06T20:18:55.4750929Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-08-06T20:18:55.4757223Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-08-06T20:18:55.4763202Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-08-06T20:18:55.4769668Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-08-06T20:18:55.4775727Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-08-06T20:18:55.4782214Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-08-06T20:18:55.4790222Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-08-06T20:18:55.4796888Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-08-06T20:18:55.4825505Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-08-06T20:18:55.8117698Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-08-06T20:18:55.9941125Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-08-06T20:18:56.1777721Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-08-06T20:18:56.3967312Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-08-06T20:18:58.4657964Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-08-06T20:19:12.0073108Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-08-06T20:19:12.3963766Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-08-06T20:19:12.9448430Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-08-06T20:19:13.5303564Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-08-06T20:19:14.6880764Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-08-06T20:19:16.6186304Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-08-06T20:19:21.9221232Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-08-06T20:19:23.2160711Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-08-06T20:19:25.1625788Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-08-06T20:19:26.5806451Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/foxi'... 2024-08-06T20:19:26.7954327Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-08-06T20:19:27.3051432Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-08-06T20:19:27.6773768Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-08-06T20:19:28.8826110Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-08-06T20:19:29.2593866Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-08-06T20:19:29.5342650Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-08-06T20:19:31.0287103Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-08-06T20:19:31.7537464Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-08-06T20:19:32.4278596Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-08-06T20:19:38.6879006Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-08-06T20:19:40.8546555Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-08-06T20:19:46.6890135Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-08-06T20:19:46.9274953Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-08-06T20:19:56.2280802Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-08-06T20:19:56.4019116Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-08-06T20:19:56.5985863Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-08-06T20:19:57.4336465Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-08-06T20:19:57.7276368Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-08-06T20:19:58.4158078Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-08-06T20:19:58.7935621Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-08-06T20:19:58.8052221Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-08-06T20:19:58.8139723Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-08-06T20:19:58.8372138Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-08-06T20:19:58.8745370Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-08-06T20:19:59.7973762Z Submodule path 'third_party/XNNPACK': checked out 'fcbf55af6cf28a4627bcd1f703ab7ad843f0f3a2' 2024-08-06T20:19:59.8201073Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-08-06T20:19:59.8640263Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-08-06T20:19:59.9591199Z Submodule path 'third_party/cpuinfo': checked out '3c8b1533ac03dd6531ab6e7b9245d488f13a82a5' 2024-08-06T20:19:59.9919133Z Submodule path 'third_party/cudnn_frontend': checked out '98ca4e1941fe3263f128f74f10063a3ea35c7019' 2024-08-06T20:20:00.4800377Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-08-06T20:20:00.7271529Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-08-06T20:20:00.8054463Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-08-06T20:20:00.8072181Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-08-06T20:20:00.8074418Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-08-06T20:20:00.8076759Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-08-06T20:20:00.8079224Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-08-06T20:20:00.8082325Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-08-06T20:20:00.8108330Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-08-06T20:20:01.9351773Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-08-06T20:20:02.5380937Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-08-06T20:20:04.4769270Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-08-06T20:20:05.7045802Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-08-06T20:20:06.0202347Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-08-06T20:20:06.1135989Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-08-06T20:20:06.5014865Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-08-06T20:20:06.5625200Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-08-06T20:20:06.5747601Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-08-06T20:20:06.6888564Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-08-06T20:20:06.7294744Z Submodule path 'third_party/fmt': checked out '0c9fce2ffefecfdce794e1859584e25877b7b592' 2024-08-06T20:20:06.7390244Z Submodule path 'third_party/foxi': checked out 'c278588e34e535f0bb8f00df3880d26928038cad' 2024-08-06T20:20:06.7781226Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-08-06T20:20:06.8029639Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-08-06T20:20:06.8479829Z Submodule path 'third_party/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2024-08-06T20:20:06.8603748Z Submodule path 'third_party/ideep': checked out '55ca0191687aaf19aca5cdb7881c791e3bea442b' 2024-08-06T20:20:06.8618374Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-08-06T20:20:06.8642144Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-08-06T20:20:21.1308508Z Submodule path 'third_party/ideep/mkl-dnn': checked out '1137e04ec0b5251ca2b4400a4fd3c667ce843d67' 2024-08-06T20:20:21.1492626Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-08-06T20:20:21.2351157Z Submodule path 'third_party/kineto': checked out 'da2f2682cabaf95d601fa2a9b7e0979f84fe7667' 2024-08-06T20:20:21.2369573Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-06T20:20:21.2371679Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-08-06T20:20:21.2374100Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-08-06T20:20:21.2400518Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-08-06T20:20:21.8962874Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-08-06T20:20:23.1751668Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-08-06T20:20:24.4134234Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-08-06T20:20:24.4152969Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-06T20:20:24.4155322Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-06T20:20:24.4157658Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-06T20:20:24.4160194Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-06T20:20:24.4162770Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-06T20:20:24.4165693Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-06T20:20:24.4168853Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-06T20:20:24.4172032Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-06T20:20:24.4198346Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-08-06T20:20:25.2754668Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-08-06T20:20:25.6474898Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-08-06T20:20:26.9915893Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-08-06T20:20:27.3298255Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-08-06T20:20:27.8991322Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-08-06T20:20:29.0711573Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-08-06T20:20:36.9373782Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-08-06T20:20:37.3671661Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-08-06T20:20:37.3857237Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-08-06T20:20:37.4228128Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-08-06T20:20:37.4363824Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-08-06T20:20:37.4378588Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-06T20:20:37.4403758Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-08-06T20:20:37.8914591Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-08-06T20:20:37.9096295Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-08-06T20:20:37.9499900Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-08-06T20:20:38.0537804Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-08-06T20:20:38.0709722Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-08-06T20:20:38.1110437Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2024-08-06T20:20:38.1683611Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-08-06T20:20:38.2057779Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-08-06T20:20:38.2292995Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290' 2024-08-06T20:20:38.3318185Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-08-06T20:20:38.6961262Z Submodule path 'third_party/onnx': checked out '3bf92c03a9f27eba3bda1e5b9e63ea20ec213557' 2024-08-06T20:20:38.6995900Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/onnx/third_party/benchmark' 2024-08-06T20:20:38.6998048Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-08-06T20:20:38.7024608Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/benchmark'... 2024-08-06T20:20:39.1748939Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-08-06T20:20:40.1026541Z Submodule path 'third_party/onnx/third_party/benchmark': checked out '2dd015dfef425c866d9a43f2c67d8b52d709acb6' 2024-08-06T20:20:40.1357301Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '5b0a6fc2017fcc176545afe3e09c9f9885283242' 2024-08-06T20:20:40.2023244Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-08-06T20:20:40.2042971Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-06T20:20:40.2045517Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-06T20:20:40.2047866Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-06T20:20:40.2050322Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-06T20:20:40.2053097Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-06T20:20:40.2055558Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-06T20:20:40.2058230Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-06T20:20:40.2060875Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-06T20:20:40.2088071Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2024-08-06T20:20:40.6807994Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2024-08-06T20:20:41.8459200Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2024-08-06T20:20:42.1425868Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2024-08-06T20:20:48.5136393Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2024-08-06T20:22:19.1492226Z Entering 'third_party/ittapi' 2024-08-06T20:22:19.1538916Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2024-08-06T20:22:19.1555214Z Entering 'third_party/kineto' 2024-08-06T20:22:19.1601738Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config remote.origin.url 2024-08-06T20:22:19.1616755Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-06T20:22:19.1663604Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config remote.origin.url 2024-08-06T20:22:19.1677843Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-06T20:22:19.1724988Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/DCGM/config remote.origin.url 2024-08-06T20:22:19.1740112Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-06T20:22:19.1788719Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/cpr/config remote.origin.url 2024-08-06T20:22:19.1802680Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-06T20:22:19.1849104Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config remote.origin.url 2024-08-06T20:22:19.1863577Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-06T20:22:19.1912418Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config remote.origin.url 2024-08-06T20:22:19.2112104Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-06T20:22:19.2158660Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config remote.origin.url 2024-08-06T20:22:19.2174210Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-06T20:22:19.2220229Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/pfs/config remote.origin.url 2024-08-06T20:22:19.2237167Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-06T20:22:19.2284522Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config remote.origin.url 2024-08-06T20:22:19.2298878Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-06T20:22:19.2345147Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config remote.origin.url 2024-08-06T20:22:19.2361691Z Entering 'third_party/mimalloc' 2024-08-06T20:22:19.2407728Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2024-08-06T20:22:19.2422855Z Entering 'third_party/nccl/nccl' 2024-08-06T20:22:19.2469341Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config remote.origin.url 2024-08-06T20:22:19.2484522Z Entering 'third_party/nlohmann' 2024-08-06T20:22:19.2530398Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2024-08-06T20:22:19.2545945Z Entering 'third_party/onnx' 2024-08-06T20:22:19.2592746Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2024-08-06T20:22:19.2622725Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-06T20:22:19.2671856Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/benchmark/config remote.origin.url 2024-08-06T20:22:19.2686666Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-06T20:22:19.2732668Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config remote.origin.url 2024-08-06T20:22:19.2750403Z Entering 'third_party/opentelemetry-cpp' 2024-08-06T20:22:19.2798152Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config remote.origin.url 2024-08-06T20:22:19.2814349Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-06T20:22:19.2861669Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config remote.origin.url 2024-08-06T20:22:19.2876272Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-06T20:22:19.2922573Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config remote.origin.url 2024-08-06T20:22:19.2936891Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-06T20:22:19.2983428Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config remote.origin.url 2024-08-06T20:22:19.2998214Z Entering 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2024-08-06T20:22:19.3241805Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-06T20:22:19.3288977Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2024-08-06T20:22:19.3305857Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-06T20:22:19.3353873Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2024-08-06T20:22:19.3370345Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-06T20:22:19.3416958Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2024-08-06T20:22:19.3453718Z Entering 'third_party/pocketfft' 2024-08-06T20:22:19.3501759Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2024-08-06T20:22:19.3515967Z Entering 'third_party/protobuf' 2024-08-06T20:22:19.3563862Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2024-08-06T20:22:19.3582647Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-06T20:22:19.3629938Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2024-08-06T20:22:19.3646897Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-06T20:22:19.3694386Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2024-08-06T20:22:19.3711655Z Entering 'third_party/psimd' 2024-08-06T20:22:19.3758858Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2024-08-06T20:22:19.3773850Z Entering 'third_party/pthreadpool' 2024-08-06T20:22:19.3820693Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2024-08-06T20:22:19.3835757Z Entering 'third_party/pybind11' 2024-08-06T20:22:19.3881815Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2024-08-06T20:22:19.3897094Z Entering 'third_party/python-peachpy' 2024-08-06T20:22:19.3942738Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2024-08-06T20:22:19.3958061Z Entering 'third_party/sleef' 2024-08-06T20:22:19.4004248Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2024-08-06T20:22:19.4019817Z Entering 'third_party/tensorpipe' 2024-08-06T20:22:19.4066482Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2024-08-06T20:22:19.4080931Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-06T20:22:19.4126374Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2024-08-06T20:22:19.4141449Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-06T20:22:19.4188561Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2024-08-06T20:22:19.4202960Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-06T20:22:19.4249094Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2024-08-06T20:22:19.4264014Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-06T20:22:19.4310210Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2024-08-06T20:22:19.4323847Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-06T20:22:19.4371150Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2024-08-06T20:22:19.5261509Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2024-08-06T20:22:19.5544395Z Entering 'android/libs/fbjni' 2024-08-06T20:22:19.5584003Z Entering 'third_party/FP16' 2024-08-06T20:22:19.5623541Z Entering 'third_party/FXdiv' 2024-08-06T20:22:19.5661754Z Entering 'third_party/NNPACK' 2024-08-06T20:22:19.5701256Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-06T20:22:19.5739259Z Entering 'third_party/XNNPACK' 2024-08-06T20:22:19.5795888Z Entering 'third_party/benchmark' 2024-08-06T20:22:19.5834189Z Entering 'third_party/cpp-httplib' 2024-08-06T20:22:19.5872838Z Entering 'third_party/cpuinfo' 2024-08-06T20:22:19.5911597Z Entering 'third_party/cudnn_frontend' 2024-08-06T20:22:19.5950036Z Entering 'third_party/cutlass' 2024-08-06T20:22:19.5996525Z Entering 'third_party/eigen' 2024-08-06T20:22:19.6036822Z Entering 'third_party/fbgemm' 2024-08-06T20:22:19.6076332Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-06T20:22:19.6114315Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-06T20:22:19.6153629Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-06T20:22:19.6198442Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-06T20:22:19.6236315Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-06T20:22:19.6276837Z Entering 'third_party/flatbuffers' 2024-08-06T20:22:19.6319103Z Entering 'third_party/fmt' 2024-08-06T20:22:19.6357838Z Entering 'third_party/foxi' 2024-08-06T20:22:19.6396617Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-06T20:22:19.6435820Z Entering 'third_party/gloo' 2024-08-06T20:22:19.6476464Z Entering 'third_party/googletest' 2024-08-06T20:22:19.6515354Z Entering 'third_party/ideep' 2024-08-06T20:22:19.6552618Z Entering 'third_party/ideep/mkl-dnn' 2024-08-06T20:22:19.6597280Z Entering 'third_party/ittapi' 2024-08-06T20:22:19.6656211Z Entering 'third_party/kineto' 2024-08-06T20:22:19.6678300Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-06T20:22:19.6716526Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-06T20:22:19.6756732Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-06T20:22:19.6794781Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-06T20:22:19.6832065Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-06T20:22:19.6870062Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-06T20:22:19.6910740Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-06T20:22:19.6950757Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-06T20:22:19.6989341Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-06T20:22:19.7028961Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-06T20:22:19.7070419Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-06T20:22:19.7108771Z Entering 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2024-08-06T20:22:19.9455481Z Entering 'third_party/flatbuffers' 2024-08-06T20:22:19.9496743Z Entering 'third_party/fmt' 2024-08-06T20:22:19.9535209Z Entering 'third_party/foxi' 2024-08-06T20:22:19.9573903Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-06T20:22:19.9612768Z Entering 'third_party/gloo' 2024-08-06T20:22:19.9650872Z Entering 'third_party/googletest' 2024-08-06T20:22:19.9689940Z Entering 'third_party/ideep' 2024-08-06T20:22:19.9727369Z Entering 'third_party/ideep/mkl-dnn' 2024-08-06T20:22:19.9772652Z Entering 'third_party/ittapi' 2024-08-06T20:22:19.9811185Z Entering 'third_party/kineto' 2024-08-06T20:22:19.9849622Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-06T20:22:19.9887244Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-06T20:22:19.9926599Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-06T20:22:19.9967515Z Entering 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'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-06T20:22:20.0859984Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-06T20:22:20.0900317Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-06T20:22:20.0939724Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-06T20:22:20.0998873Z Entering 'third_party/pocketfft' 2024-08-06T20:22:20.1038099Z Entering 'third_party/protobuf' 2024-08-06T20:22:20.1080390Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-06T20:22:20.1118032Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-06T20:22:20.1157518Z Entering 'third_party/psimd' 2024-08-06T20:22:20.1196143Z Entering 'third_party/pthreadpool' 2024-08-06T20:22:20.1234219Z Entering 'third_party/pybind11' 2024-08-06T20:22:20.1272580Z Entering 'third_party/python-peachpy' 2024-08-06T20:22:20.1310421Z Entering 'third_party/sleef' 2024-08-06T20:22:20.1349888Z Entering 'third_party/tensorpipe' 2024-08-06T20:22:20.1388195Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-06T20:22:20.1425102Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-06T20:22:20.1462760Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-06T20:22:20.1500781Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-06T20:22:20.1536943Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-06T20:22:20.1588830Z ##[endgroup] 2024-08-06T20:22:20.1628097Z [command]/usr/bin/git log -1 --format='%H' 2024-08-06T20:22:20.1656597Z 'b9d86fa89636e301796d4201f36d86c73f6e49bc' 2024-08-06T20:22:20.1812779Z Prepare all required actions 2024-08-06T20:22:20.1813303Z Getting action download info 2024-08-06T20:22:20.3421360Z ##[group]Run ./.github/actions/setup-linux 2024-08-06T20:22:20.3421858Z env: 2024-08-06T20:22:20.3422077Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:20.3422352Z ##[endgroup] 2024-08-06T20:22:20.3471220Z ##[group]Run set -euo pipefail 2024-08-06T20:22:20.3471587Z set -euo pipefail 2024-08-06T20:22:20.3471884Z function get_ec2_metadata() { 2024-08-06T20:22:20.3472282Z  # Pulled from instance metadata endpoint for EC2 2024-08-06T20:22:20.3472936Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-08-06T20:22:20.3473505Z  category=$1 2024-08-06T20:22:20.3473879Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-08-06T20:22:20.3474330Z  runner_name_str=i-03096d6fe1daee476 2024-08-06T20:22:20.3474733Z  if [[ -f /.inarc ]]; then 2024-08-06T20:22:20.3475093Z  echo "ARC Runner, no info on ec2 metadata" 2024-08-06T20:22:20.3475484Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-08-06T20:22:20.3475982Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-08-06T20:22:20.3476430Z  else 2024-08-06T20:22:20.3476769Z  curl -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2024-08-06T20:22:20.3477188Z  fi 2024-08-06T20:22:20.3477410Z } 2024-08-06T20:22:20.3477669Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-08-06T20:22:20.3478104Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-08-06T20:22:20.3478595Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-08-06T20:22:20.3479010Z echo "system info $(uname -a)" 2024-08-06T20:22:20.3487505Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:20.3487909Z env: 2024-08-06T20:22:20.3488130Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:20.3488402Z ##[endgroup] 2024-08-06T20:22:20.3581185Z ami-id: ami-06c68f701d8090592 2024-08-06T20:22:20.3631484Z instance-id: i-03096d6fe1daee476 2024-08-06T20:22:20.3680414Z instance-type: c5.2xlarge 2024-08-06T20:22:20.3690513Z system info Linux ip-10-0-6-243.ec2.internal 6.1.94-99.176.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:57:56 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-08-06T20:22:20.3721681Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-08-06T20:22:20.3722357Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-08-06T20:22:20.3728324Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:20.3728718Z env: 2024-08-06T20:22:20.3728943Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:20.3729201Z ##[endgroup] 2024-08-06T20:22:20.3805377Z ##[group]Run if systemctl is-active --quiet docker; then 2024-08-06T20:22:20.3805866Z if systemctl is-active --quiet docker; then 2024-08-06T20:22:20.3806251Z  echo "Docker daemon is running..."; 2024-08-06T20:22:20.3806595Z else 2024-08-06T20:22:20.3806972Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-08-06T20:22:20.3807424Z fi 2024-08-06T20:22:20.3812849Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:20.3813232Z env: 2024-08-06T20:22:20.3813461Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:20.3813738Z ##[endgroup] 2024-08-06T20:22:20.3884445Z Docker daemon is running... 2024-08-06T20:22:20.3940972Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-08-06T20:22:20.3941401Z with: 2024-08-06T20:22:20.3941614Z shell: bash 2024-08-06T20:22:20.3941849Z timeout_minutes: 5 2024-08-06T20:22:20.3942086Z max_attempts: 3 2024-08-06T20:22:20.3942332Z retry_wait_seconds: 30 2024-08-06T20:22:20.3943907Z command: AWS_ACCOUNT_ID=$(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.$AWS_DEFAULT_REGION.amazonaws.com" 2024-08-06T20:22:20.3945155Z polling_interval_seconds: 1 2024-08-06T20:22:20.3945435Z warning_on_retry: true 2024-08-06T20:22:20.3945709Z continue_on_error: false 2024-08-06T20:22:20.3945982Z env: 2024-08-06T20:22:20.3946190Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:20.3946467Z AWS_RETRY_MODE: standard 2024-08-06T20:22:20.3946812Z AWS_MAX_ATTEMPTS: 5 2024-08-06T20:22:20.3947069Z AWS_DEFAULT_REGION: us-east-1 2024-08-06T20:22:20.3947356Z ##[endgroup] 2024-08-06T20:22:21.5381620Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-06T20:22:21.5382706Z Configure a credential helper to remove this warning. See 2024-08-06T20:22:21.5383638Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-06T20:22:21.5384172Z 2024-08-06T20:22:21.5384315Z Login Succeeded 2024-08-06T20:22:22.4503822Z Command completed after 1 attempt(s). 2024-08-06T20:22:22.4557969Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-06T20:22:22.4558539Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-06T20:22:22.4559021Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-06T20:22:22.4565315Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:22.4565700Z env: 2024-08-06T20:22:22.4565931Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:22.4566207Z ##[endgroup] 2024-08-06T20:22:22.4647599Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-08-06T20:22:22.4648201Z # ignore expansion of "docker ps -q" since it could be empty 2024-08-06T20:22:22.4648630Z # shellcheck disable=SC2046 2024-08-06T20:22:22.4648981Z docker stop $(docker ps -q) || true 2024-08-06T20:22:22.4649363Z # Prune all of the docker images 2024-08-06T20:22:22.4649688Z docker system prune -af 2024-08-06T20:22:22.4655215Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:22.4655632Z env: 2024-08-06T20:22:22.4655838Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:22.4656113Z ##[endgroup] 2024-08-06T20:22:22.4908314Z "docker stop" requires at least 1 argument. 2024-08-06T20:22:22.4908743Z See 'docker stop --help'. 2024-08-06T20:22:22.4908924Z 2024-08-06T20:22:22.4909093Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-08-06T20:22:22.4909384Z 2024-08-06T20:22:22.4909498Z Stop one or more running containers 2024-08-06T20:22:22.5050722Z Total reclaimed space: 0B 2024-08-06T20:22:22.5116299Z ##[group]Run set +e 2024-08-06T20:22:22.5116602Z set +e 2024-08-06T20:22:22.5116845Z set -x 2024-08-06T20:22:22.5117063Z  2024-08-06T20:22:22.5117317Z PT_DOMAIN=download.pytorch.org 2024-08-06T20:22:22.5117928Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-08-06T20:22:22.5118715Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-08-06T20:22:22.5119263Z # one is returned at random 2024-08-06T20:22:22.5119667Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-08-06T20:22:22.5120055Z  2024-08-06T20:22:22.5120280Z if [ -z "${RESOLVED_IP}" ]; then 2024-08-06T20:22:22.5120859Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-08-06T20:22:22.5121402Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-08-06T20:22:22.5121797Z  2024-08-06T20:22:22.5122041Z  if [ -z "${RESOLVED_IP}" ]; then 2024-08-06T20:22:22.5122439Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-08-06T20:22:22.5122803Z  exit 1 2024-08-06T20:22:22.5123045Z  fi 2024-08-06T20:22:22.5123431Z fi 2024-08-06T20:22:22.5123637Z  2024-08-06T20:22:22.5123904Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-08-06T20:22:22.5124284Z  # Clean up any old records first 2024-08-06T20:22:22.5124755Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-08-06T20:22:22.5125094Z fi 2024-08-06T20:22:22.5125311Z  2024-08-06T20:22:22.5125618Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-08-06T20:22:22.5126032Z cat /etc/hosts 2024-08-06T20:22:22.5131878Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:22.5132257Z env: 2024-08-06T20:22:22.5132486Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:22.5132753Z ##[endgroup] 2024-08-06T20:22:22.5155577Z + PT_DOMAIN=download.pytorch.org 2024-08-06T20:22:22.5161117Z ++ dig -4 +short download.pytorch.org 2024-08-06T20:22:22.5161772Z ++ tail -n1 2024-08-06T20:22:22.5519660Z + RESOLVED_IP=18.160.10.28 2024-08-06T20:22:22.5520025Z + '[' -z 18.160.10.28 ']' 2024-08-06T20:22:22.5520329Z + grep -r download.pytorch.org /etc/hosts 2024-08-06T20:22:22.5532016Z 18.160.10.22 download.pytorch.org 2024-08-06T20:22:22.5533508Z + sudo sed -i /download.pytorch.org/d /etc/hosts 2024-08-06T20:22:22.8331132Z + echo '18.160.10.28 download.pytorch.org' 2024-08-06T20:22:22.8331887Z + sudo tee -a /etc/hosts 2024-08-06T20:22:22.8739286Z 18.160.10.28 download.pytorch.org 2024-08-06T20:22:22.8754605Z + cat /etc/hosts 2024-08-06T20:22:22.8763627Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-08-06T20:22:22.8769414Z ::1 localhost6 localhost6.localdomain6 2024-08-06T20:22:22.8769811Z 18.160.10.28 download.pytorch.org 2024-08-06T20:22:22.8911789Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2024-08-06T20:22:22.8912284Z with: 2024-08-06T20:22:22.8912975Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:22.8913785Z docker-build-dir: .ci/docker 2024-08-06T20:22:22.8914083Z working-directory: . 2024-08-06T20:22:22.8914442Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:22.8914858Z force-push: false 2024-08-06T20:22:22.8915095Z env: 2024-08-06T20:22:22.8915314Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:22.8915564Z ##[endgroup] 2024-08-06T20:22:22.8939390Z ##[group]Run set -ex 2024-08-06T20:22:22.8939706Z set -ex 2024-08-06T20:22:22.8939944Z  2024-08-06T20:22:22.8940359Z # If the docker build directory or the build script doesn't exist, the action will 2024-08-06T20:22:22.8941097Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-08-06T20:22:22.8941673Z # job could then download the pre-built image as usual 2024-08-06T20:22:22.8942203Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-08-06T20:22:22.8943683Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.8944150Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.8944563Z  2024-08-06T20:22:22.8944942Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-08-06T20:22:22.8945402Z  exit 0 2024-08-06T20:22:22.8945624Z else 2024-08-06T20:22:22.8945899Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.8946347Z fi 2024-08-06T20:22:22.8946555Z  2024-08-06T20:22:22.8946980Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-08-06T20:22:22.8947680Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-08-06T20:22:22.8948455Z  # use it as it is, but first let's extract the tag 2024-08-06T20:22:22.8948958Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-08-06T20:22:22.8949488Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.8949986Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.8950573Z else 2024-08-06T20:22:22.8951039Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-08-06T20:22:22.8951506Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.8952169Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.8952751Z fi 2024-08-06T20:22:22.8961647Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:22.8962036Z env: 2024-08-06T20:22:22.8962263Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:22.8962531Z REPO_NAME: pytorch 2024-08-06T20:22:22.8963264Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:22.8964057Z DOCKER_BUILD_DIR: .ci/docker 2024-08-06T20:22:22.8964436Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:22.8964836Z ##[endgroup] 2024-08-06T20:22:22.8990051Z + [[ ! -d .ci/docker ]] 2024-08-06T20:22:22.8990341Z + [[ ! -f .ci/docker/build.sh ]] 2024-08-06T20:22:22.8990622Z + echo skip=false 2024-08-06T20:22:22.8991956Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 == *\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* ]] 2024-08-06T20:22:22.8997537Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:22.8998333Z ++ awk -F '[:,]' '{print $2}' 2024-08-06T20:22:22.9049790Z + DOCKER_TAG=02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:22.9050269Z + echo docker-tag=02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:22.9051171Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:22.9084477Z ##[group]Run set +e 2024-08-06T20:22:22.9084798Z set +e 2024-08-06T20:22:22.9085023Z set -x 2024-08-06T20:22:22.9085259Z  2024-08-06T20:22:22.9085481Z login() { 2024-08-06T20:22:22.9085963Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-08-06T20:22:22.9086509Z } 2024-08-06T20:22:22.9086726Z  2024-08-06T20:22:22.9086931Z retry () { 2024-08-06T20:22:22.9087219Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-08-06T20:22:22.9087543Z } 2024-08-06T20:22:22.9087744Z  2024-08-06T20:22:22.9087981Z retry login "${DOCKER_REGISTRY}" 2024-08-06T20:22:22.9088285Z  2024-08-06T20:22:22.9088623Z # Check if image already exists, if it does then skip building it 2024-08-06T20:22:22.9089132Z if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-08-06T20:22:22.9089508Z  exit 0 2024-08-06T20:22:22.9089732Z fi 2024-08-06T20:22:22.9089946Z  2024-08-06T20:22:22.9090290Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-08-06T20:22:22.9090880Z # be empty. The default action would be to continue rebuild the image 2024-08-06T20:22:22.9091407Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-08-06T20:22:22.9091872Z  # if we're on the base branch then use the parent commit 2024-08-06T20:22:22.9092276Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-08-06T20:22:22.9092604Z else 2024-08-06T20:22:22.9092938Z  # otherwise we're on a PR, so use the most recent base commit 2024-08-06T20:22:22.9093412Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-08-06T20:22:22.9093790Z fi 2024-08-06T20:22:22.9094005Z  2024-08-06T20:22:22.9094228Z if [[ -z "${MERGE_BASE}" ]]; then 2024-08-06T20:22:22.9094594Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.9095069Z  2024-08-06T20:22:22.9095534Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-08-06T20:22:22.9096103Z  exit 0 2024-08-06T20:22:22.9096338Z fi 2024-08-06T20:22:22.9096542Z  2024-08-06T20:22:22.9096859Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-08-06T20:22:22.9097569Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-08-06T20:22:22.9098164Z  exit 1 2024-08-06T20:22:22.9098394Z fi 2024-08-06T20:22:22.9098613Z  2024-08-06T20:22:22.9098971Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-08-06T20:22:22.9099647Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-08-06T20:22:22.9100254Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-08-06T20:22:22.9100952Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-08-06T20:22:22.9101725Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-08-06T20:22:22.9102195Z fi 2024-08-06T20:22:22.9102411Z  2024-08-06T20:22:22.9102767Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-08-06T20:22:22.9108361Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:22.9108750Z env: 2024-08-06T20:22:22.9108957Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:22.9109244Z DOCKER_BUILD_DIR: .ci/docker 2024-08-06T20:22:22.9109596Z BASE_REVISION: 1736af7cf736184c356be1bb00f59fb2feea6d7d 2024-08-06T20:22:22.9110417Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:22.9111246Z DOCKER_TAG: 02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:22.9111714Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:22.9112099Z ##[endgroup] 2024-08-06T20:22:22.9134362Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:22.9134821Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:22.9136886Z + aws ecr get-login-password --region us-east-1 2024-08-06T20:22:22.9138029Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:23.4643023Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-06T20:22:23.4643686Z Configure a credential helper to remove this warning. See 2024-08-06T20:22:23.4644348Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-06T20:22:23.4644908Z 2024-08-06T20:22:23.4645086Z Login Succeeded 2024-08-06T20:22:23.4658573Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:23.6722620Z { 2024-08-06T20:22:23.6723086Z "schemaVersion": 2, 2024-08-06T20:22:23.6723780Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-08-06T20:22:23.6724437Z "config": { 2024-08-06T20:22:23.6724932Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-08-06T20:22:23.6725517Z "size": 43414, 2024-08-06T20:22:23.6726173Z "digest": "sha256:7572c787a966437be85abe02d1578158976ce242058ff35a46097c5cce747b65" 2024-08-06T20:22:23.6727018Z }, 2024-08-06T20:22:23.6727341Z "layers": [ 2024-08-06T20:22:23.6727701Z { 2024-08-06T20:22:23.6728290Z "mediaType": 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"sha256:a1f65f5ee1b9e2a0b50e542115a4fb4c40d74af180e677b7564364af570e3bb6" 2024-08-06T20:22:23.6908345Z }, 2024-08-06T20:22:23.6908530Z { 2024-08-06T20:22:23.6908858Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6909289Z "size": 32, 2024-08-06T20:22:23.6909698Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-06T20:22:23.6910184Z }, 2024-08-06T20:22:23.6910381Z { 2024-08-06T20:22:23.6910700Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6911126Z "size": 104, 2024-08-06T20:22:23.6911541Z "digest": "sha256:b612a56e3264a78b894344fe08c85d8ca5def27c59682d0a8a05280f03ac03c1" 2024-08-06T20:22:23.6912006Z }, 2024-08-06T20:22:23.6912271Z { 2024-08-06T20:22:23.6912607Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6913022Z "size": 1841, 2024-08-06T20:22:23.6913436Z "digest": "sha256:7586204541024fd269c979c13158bd840eecbb24c8200c03a33d1c4112dc079e" 2024-08-06T20:22:23.6913918Z }, 2024-08-06T20:22:23.6914107Z { 2024-08-06T20:22:23.6914441Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6914870Z "size": 7529636, 2024-08-06T20:22:23.6915290Z "digest": "sha256:53bc8a6759fcb58e6e15b36e1d754da0417d69a313cfe53f0d498923fa31c06d" 2024-08-06T20:22:23.6915774Z }, 2024-08-06T20:22:23.6915959Z { 2024-08-06T20:22:23.6916293Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6916717Z "size": 106, 2024-08-06T20:22:23.6917117Z "digest": "sha256:df484cabb091817a184cbff78cd30b37308f0676f888a1058591103661359092" 2024-08-06T20:22:23.6917593Z }, 2024-08-06T20:22:23.6917790Z { 2024-08-06T20:22:23.6918112Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6918539Z "size": 164, 2024-08-06T20:22:23.6918959Z "digest": "sha256:c6845d12fa9cf399f5183a45e75edde15eb9d4adde3920eb32e6c330e912fb0b" 2024-08-06T20:22:23.6919432Z }, 2024-08-06T20:22:23.6919631Z { 2024-08-06T20:22:23.6919964Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6920379Z "size": 7944, 2024-08-06T20:22:23.6920803Z "digest": "sha256:753c44cee6e1f1bf167d235a71aeade5a6762991dc8eb28f5c8c48460a2f30e9" 2024-08-06T20:22:23.6921293Z }, 2024-08-06T20:22:23.6921477Z { 2024-08-06T20:22:23.6921808Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6922235Z "size": 8067, 2024-08-06T20:22:23.6922640Z "digest": "sha256:44405015d60832892b4846efcae8ac22b0e45dee5522ce2f259dfb2cb07b219a" 2024-08-06T20:22:23.6923117Z }, 2024-08-06T20:22:23.6923312Z { 2024-08-06T20:22:23.6923635Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6924058Z "size": 301, 2024-08-06T20:22:23.6924472Z "digest": "sha256:c4f46162314640b03c3abb7bb1eac99fd16bb002668abb80824661675e2ee79e" 2024-08-06T20:22:23.6924941Z }, 2024-08-06T20:22:23.6925137Z { 2024-08-06T20:22:23.6925468Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6925884Z "size": 32, 2024-08-06T20:22:23.6926303Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-06T20:22:23.6926860Z }, 2024-08-06T20:22:23.6927044Z { 2024-08-06T20:22:23.6927378Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6927807Z "size": 108, 2024-08-06T20:22:23.6928203Z "digest": "sha256:6acbdc2527c9c427993060927e64e711d53a60280d67883952e995257a31abe9" 2024-08-06T20:22:23.6928674Z }, 2024-08-06T20:22:23.6928869Z { 2024-08-06T20:22:23.6929187Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6929616Z "size": 54145774, 2024-08-06T20:22:23.6930039Z "digest": "sha256:82264f8de02acc48e8335869bcd920e2327c637515a5a11dad8da6d2cb6c9294" 2024-08-06T20:22:23.6930520Z }, 2024-08-06T20:22:23.6930718Z { 2024-08-06T20:22:23.6931037Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-06T20:22:23.6931459Z "size": 32, 2024-08-06T20:22:23.6931875Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-06T20:22:23.6932353Z } 2024-08-06T20:22:23.6932555Z ] 2024-08-06T20:22:23.6932750Z } 2024-08-06T20:22:23.6932960Z + exit 0 2024-08-06T20:22:23.6997720Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-08-06T20:22:23.6998125Z tag=${ECR_DOCKER_IMAGE##*/} 2024-08-06T20:22:23.6998544Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-08-06T20:22:23.7004345Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:23.7004731Z env: 2024-08-06T20:22:23.7004961Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:23.7005705Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:23.7006482Z ##[endgroup] 2024-08-06T20:22:23.7031719Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.12-clang10-02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:23.7081606Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2024-08-06T20:22:23.7082077Z with: 2024-08-06T20:22:23.7082756Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:23.7083629Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:23.7084029Z env: 2024-08-06T20:22:23.7084234Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:23.7084502Z ##[endgroup] 2024-08-06T20:22:23.7104677Z ##[group]Run set -x 2024-08-06T20:22:23.7104974Z set -x 2024-08-06T20:22:23.7105213Z set +e 2024-08-06T20:22:23.7105432Z  2024-08-06T20:22:23.7105655Z login() { 2024-08-06T20:22:23.7106149Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-08-06T20:22:23.7106678Z } 2024-08-06T20:22:23.7106985Z  2024-08-06T20:22:23.7107264Z retry () { 2024-08-06T20:22:23.7107530Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-08-06T20:22:23.7107861Z } 2024-08-06T20:22:23.7108088Z  2024-08-06T20:22:23.7108315Z retry login "${DOCKER_REGISTRY}" 2024-08-06T20:22:23.7108628Z  2024-08-06T20:22:23.7108826Z set -e 2024-08-06T20:22:23.7109172Z # ignore output since only exit code is used for conditional 2024-08-06T20:22:23.7109676Z # only pull docker image if it's not available locally 2024-08-06T20:22:23.7110235Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-08-06T20:22:23.7110739Z  retry docker pull "${DOCKER_IMAGE}" 2024-08-06T20:22:23.7111066Z fi 2024-08-06T20:22:23.7116485Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:22:23.7116864Z env: 2024-08-06T20:22:23.7117090Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:22:23.7117825Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:23.7118679Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:23.7119220Z ##[endgroup] 2024-08-06T20:22:23.7141977Z + set +e 2024-08-06T20:22:23.7142865Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:23.7143344Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:23.7145886Z + aws ecr get-login-password --region us-east-1 2024-08-06T20:22:23.7146935Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-06T20:22:24.2584455Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-06T20:22:24.2585109Z Configure a credential helper to remove this warning. See 2024-08-06T20:22:24.2585827Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-06T20:22:24.2586229Z 2024-08-06T20:22:24.2586341Z Login Succeeded 2024-08-06T20:22:24.2599389Z + set -e 2024-08-06T20:22:24.2600161Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:24.2726709Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:24.2727974Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:22:24.5011431Z 02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9: Pulling from pytorch/pytorch-linux-focal-py3.12-clang10 2024-08-06T20:22:24.5015066Z 560c024910be: Pulling fs layer 2024-08-06T20:22:24.5017008Z 5536cf60f335: Pulling fs layer 2024-08-06T20:22:24.5017648Z 2d2a994403d3: Pulling fs layer 2024-08-06T20:22:24.5018290Z 8bb2baf0f937: Pulling fs layer 2024-08-06T20:22:24.5018771Z 2a29c47c7450: Pulling fs layer 2024-08-06T20:22:24.5019691Z 60ae3239e75d: Pulling fs layer 2024-08-06T20:22:24.5020205Z 0e2a419f6d38: Pulling fs layer 2024-08-06T20:22:24.5020688Z 2f7615e08b26: Pulling fs layer 2024-08-06T20:22:24.5021221Z 5231e76a47c7: Pulling fs layer 2024-08-06T20:22:24.5021726Z 32d63b5acf81: Pulling fs layer 2024-08-06T20:22:24.5022257Z e9f6f1ca5fb0: Pulling fs layer 2024-08-06T20:22:24.5022771Z 5715243311bd: Pulling fs layer 2024-08-06T20:22:24.5023231Z 2ac7d65122e5: Pulling fs layer 2024-08-06T20:22:24.5023724Z 4f4fb700ef54: Pulling fs layer 2024-08-06T20:22:24.5024251Z 6b61f599dafa: Pulling fs layer 2024-08-06T20:22:24.5024728Z 5231e76a47c7: Waiting 2024-08-06T20:22:24.5025186Z 1f11bb841d2a: Pulling fs layer 2024-08-06T20:22:24.5025685Z 32d63b5acf81: Waiting 2024-08-06T20:22:24.5026126Z fab16dc9bed3: Pulling fs layer 2024-08-06T20:22:24.5026621Z 2a29c47c7450: Waiting 2024-08-06T20:22:24.5027126Z 0e2a419f6d38: Waiting 2024-08-06T20:22:24.5027569Z 60ae3239e75d: Waiting 2024-08-06T20:22:24.5028016Z 4702c9016e32: Pulling fs layer 2024-08-06T20:22:24.5028583Z 2f7615e08b26: Waiting 2024-08-06T20:22:24.5029038Z 4578fa00ba8a: Pulling fs layer 2024-08-06T20:22:24.5029577Z e9f6f1ca5fb0: Waiting 2024-08-06T20:22:24.5030023Z 5715243311bd: Waiting 2024-08-06T20:22:24.5030313Z 5e0b7a7387b1: Pulling fs layer 2024-08-06T20:22:24.5030609Z 3b12a15a2dd8: Pulling fs layer 2024-08-06T20:22:24.5030887Z 2ac7d65122e5: Waiting 2024-08-06T20:22:24.5031133Z efc9e54882ba: Pulling fs layer 2024-08-06T20:22:24.5031430Z bbcce21dddee: Pulling fs layer 2024-08-06T20:22:24.5031715Z 4f4fb700ef54: Waiting 2024-08-06T20:22:24.5031974Z 0ffc6f4509fe: Pulling fs layer 2024-08-06T20:22:24.5032266Z c43dca7e37c0: Pulling fs layer 2024-08-06T20:22:24.5032549Z 6b61f599dafa: Waiting 2024-08-06T20:22:24.5032796Z 380952bfb408: Pulling fs layer 2024-08-06T20:22:24.5033079Z 1f11bb841d2a: Waiting 2024-08-06T20:22:24.5033329Z ceb2379cb498: Pulling fs layer 2024-08-06T20:22:24.5033624Z 62a07c6fa5fc: Pulling fs layer 2024-08-06T20:22:24.5033918Z 8b5570c513a9: Pulling fs layer 2024-08-06T20:22:24.5034202Z 88d03497075e: Pulling fs layer 2024-08-06T20:22:24.5034498Z 39d053a85f7c: Pulling fs layer 2024-08-06T20:22:24.5034792Z 04f7d0566845: Pulling fs layer 2024-08-06T20:22:24.5035243Z 87202a98d131: Pulling fs layer 2024-08-06T20:22:24.5035532Z 3d149566d7ff: Pulling fs layer 2024-08-06T20:22:24.5035811Z 4702c9016e32: Waiting 2024-08-06T20:22:24.5036048Z fab16dc9bed3: Waiting 2024-08-06T20:22:24.5036315Z 18983ac58952: Pulling fs layer 2024-08-06T20:22:24.5036595Z 4578fa00ba8a: Waiting 2024-08-06T20:22:24.5036831Z 8bb2baf0f937: Waiting 2024-08-06T20:22:24.5037094Z d1f3066e929d: Pulling fs layer 2024-08-06T20:22:24.5037375Z 5e0b7a7387b1: Waiting 2024-08-06T20:22:24.5037608Z 3b12a15a2dd8: Waiting 2024-08-06T20:22:24.5037865Z a3cbced61652: Pulling fs layer 2024-08-06T20:22:24.5038145Z efc9e54882ba: Waiting 2024-08-06T20:22:24.5038387Z e5ca241cc120: Pulling fs layer 2024-08-06T20:22:24.5038667Z 04f7d0566845: Waiting 2024-08-06T20:22:24.5038923Z 2a68b738ffe1: Pulling fs layer 2024-08-06T20:22:24.5039190Z 87202a98d131: Waiting 2024-08-06T20:22:24.5039431Z 0ffc6f4509fe: Waiting 2024-08-06T20:22:24.5039668Z bbcce21dddee: Waiting 2024-08-06T20:22:24.5039922Z c43dca7e37c0: Waiting 2024-08-06T20:22:24.5040165Z 3d149566d7ff: Waiting 2024-08-06T20:22:24.5040393Z ceb2379cb498: Waiting 2024-08-06T20:22:24.5040636Z 380952bfb408: Waiting 2024-08-06T20:22:24.5040878Z a3cbced61652: Waiting 2024-08-06T20:22:24.5041123Z 3767fa07553a: Pulling fs layer 2024-08-06T20:22:24.5041403Z 18983ac58952: Waiting 2024-08-06T20:22:24.5041649Z 62a07c6fa5fc: Waiting 2024-08-06T20:22:24.5041883Z d1f3066e929d: Waiting 2024-08-06T20:22:24.5042130Z e5ca241cc120: Waiting 2024-08-06T20:22:24.5042373Z 8b5570c513a9: Waiting 2024-08-06T20:22:24.5042819Z 88d03497075e: Waiting 2024-08-06T20:22:24.5043062Z 39d053a85f7c: Waiting 2024-08-06T20:22:24.5043318Z 1367c503435c: Pulling fs layer 2024-08-06T20:22:24.5043585Z 3767fa07553a: Waiting 2024-08-06T20:22:24.5043827Z 2a68b738ffe1: Waiting 2024-08-06T20:22:24.5044082Z 3106fe392226: Pulling fs layer 2024-08-06T20:22:24.5044517Z e673200c085d: Pulling fs layer 2024-08-06T20:22:24.5044815Z 734e3d036d23: Pulling fs layer 2024-08-06T20:22:24.5045104Z d7ff8fb3636b: Pulling fs layer 2024-08-06T20:22:24.5045404Z 1cce4640fddc: Pulling fs layer 2024-08-06T20:22:24.5045699Z 427d59d82d28: Pulling fs layer 2024-08-06T20:22:24.5045974Z 411f34d6d581: Pulling fs layer 2024-08-06T20:22:24.5046266Z 4d13e386b5bf: Pulling fs layer 2024-08-06T20:22:24.5046541Z 3106fe392226: Waiting 2024-08-06T20:22:24.5046788Z 0f9cdfa3c55d: Pulling fs layer 2024-08-06T20:22:24.5047082Z cfbf800d7f89: Pulling fs layer 2024-08-06T20:22:24.5047358Z e673200c085d: Waiting 2024-08-06T20:22:24.5047600Z 31934c84f92c: Pulling fs layer 2024-08-06T20:22:24.5047877Z 734e3d036d23: Waiting 2024-08-06T20:22:24.5048120Z d7ff8fb3636b: Waiting 2024-08-06T20:22:24.5048355Z 0f9cdfa3c55d: Waiting 2024-08-06T20:22:24.5048611Z 91254fa13f25: Pulling fs layer 2024-08-06T20:22:24.5048889Z 1cce4640fddc: Waiting 2024-08-06T20:22:24.5049136Z 13d99bf6f074: Pulling fs layer 2024-08-06T20:22:24.5049431Z aa216cf24a5c: Pulling fs layer 2024-08-06T20:22:24.5049708Z 427d59d82d28: Waiting 2024-08-06T20:22:24.5049942Z cfbf800d7f89: Waiting 2024-08-06T20:22:24.5050197Z 31934c84f92c: Waiting 2024-08-06T20:22:24.5050426Z 411f34d6d581: Waiting 2024-08-06T20:22:24.5050679Z 8a75636074ed: Pulling fs layer 2024-08-06T20:22:24.5050961Z aa216cf24a5c: Waiting 2024-08-06T20:22:24.5051207Z bc60d0ee2efa: Pulling fs layer 2024-08-06T20:22:24.5051486Z 13d99bf6f074: Waiting 2024-08-06T20:22:24.5051729Z 91254fa13f25: Waiting 2024-08-06T20:22:24.5051974Z d7b6bfdc4ac4: Pulling fs layer 2024-08-06T20:22:24.5052267Z 362577cee720: Pulling fs layer 2024-08-06T20:22:24.5052540Z 8a75636074ed: Waiting 2024-08-06T20:22:24.5052786Z dd997f1a9a7a: Pulling fs layer 2024-08-06T20:22:24.5053068Z d7b6bfdc4ac4: Waiting 2024-08-06T20:22:24.5053317Z dd997f1a9a7a: Waiting 2024-08-06T20:22:24.5053549Z 362577cee720: Waiting 2024-08-06T20:22:24.5053804Z e1b7d62c8da3: Pulling fs layer 2024-08-06T20:22:24.5054099Z 5b5ac0963a87: Pulling fs layer 2024-08-06T20:22:24.5054363Z 4d13e386b5bf: Waiting 2024-08-06T20:22:24.5054621Z 0926e4f103f3: Pulling fs layer 2024-08-06T20:22:24.5055016Z 5b5ac0963a87: Waiting 2024-08-06T20:22:24.5055265Z 0926e4f103f3: Waiting 2024-08-06T20:22:24.5055525Z f29ef8d2e14d: Pulling fs layer 2024-08-06T20:22:24.5055807Z e1b7d62c8da3: Waiting 2024-08-06T20:22:24.5056057Z a1f65f5ee1b9: Pulling fs layer 2024-08-06T20:22:24.5056345Z b612a56e3264: Pulling fs layer 2024-08-06T20:22:24.5056629Z 758620454102: Pulling fs layer 2024-08-06T20:22:24.5056888Z b612a56e3264: Waiting 2024-08-06T20:22:24.5057143Z 53bc8a6759fc: Pulling fs layer 2024-08-06T20:22:24.5057433Z df484cabb091: Pulling fs layer 2024-08-06T20:22:24.5057713Z c6845d12fa9c: Pulling fs layer 2024-08-06T20:22:24.5057992Z 53bc8a6759fc: Waiting 2024-08-06T20:22:24.5058237Z 753c44cee6e1: Pulling fs layer 2024-08-06T20:22:24.5058524Z 44405015d608: Pulling fs layer 2024-08-06T20:22:24.5058820Z c4f461623146: Pulling fs layer 2024-08-06T20:22:24.5059091Z 44405015d608: Waiting 2024-08-06T20:22:24.5059338Z f29ef8d2e14d: Waiting 2024-08-06T20:22:24.5059604Z 6acbdc2527c9: Pulling fs layer 2024-08-06T20:22:24.5059890Z 82264f8de02a: Pulling fs layer 2024-08-06T20:22:24.5060172Z 6acbdc2527c9: Waiting 2024-08-06T20:22:24.5060423Z c4f461623146: Waiting 2024-08-06T20:22:24.5060661Z 82264f8de02a: Waiting 2024-08-06T20:22:24.5060909Z 758620454102: Waiting 2024-08-06T20:22:24.5856070Z 5536cf60f335: Verifying Checksum 2024-08-06T20:22:24.5856529Z 5536cf60f335: Download complete 2024-08-06T20:22:24.6522693Z 8bb2baf0f937: Verifying Checksum 2024-08-06T20:22:24.6523094Z 8bb2baf0f937: Download complete 2024-08-06T20:22:24.8400149Z 560c024910be: Verifying Checksum 2024-08-06T20:22:24.8400561Z 560c024910be: Download complete 2024-08-06T20:22:24.9263250Z 60ae3239e75d: Download complete 2024-08-06T20:22:25.0023276Z 0e2a419f6d38: Verifying Checksum 2024-08-06T20:22:25.0023864Z 0e2a419f6d38: Download complete 2024-08-06T20:22:25.0752025Z 2f7615e08b26: Verifying Checksum 2024-08-06T20:22:25.0752953Z 2f7615e08b26: Download complete 2024-08-06T20:22:25.1679480Z 5231e76a47c7: Verifying Checksum 2024-08-06T20:22:25.1680115Z 5231e76a47c7: Download complete 2024-08-06T20:22:25.2417033Z 32d63b5acf81: Download complete 2024-08-06T20:22:25.3116342Z e9f6f1ca5fb0: Verifying Checksum 2024-08-06T20:22:25.3116982Z e9f6f1ca5fb0: Download complete 2024-08-06T20:22:25.3921092Z 5715243311bd: Verifying Checksum 2024-08-06T20:22:25.3921541Z 5715243311bd: Download complete 2024-08-06T20:22:25.4963873Z 2a29c47c7450: Verifying Checksum 2024-08-06T20:22:25.4964426Z 2a29c47c7450: Download complete 2024-08-06T20:22:25.5069907Z 4f4fb700ef54: Verifying Checksum 2024-08-06T20:22:25.5070481Z 4f4fb700ef54: Download complete 2024-08-06T20:22:25.6033986Z 6b61f599dafa: Verifying Checksum 2024-08-06T20:22:25.6034594Z 6b61f599dafa: Download complete 2024-08-06T20:22:25.6821265Z 1f11bb841d2a: Download complete 2024-08-06T20:22:25.7213865Z 560c024910be: Pull complete 2024-08-06T20:22:25.7398211Z 5536cf60f335: Pull complete 2024-08-06T20:22:25.7522566Z fab16dc9bed3: Verifying Checksum 2024-08-06T20:22:25.7523131Z fab16dc9bed3: Download complete 2024-08-06T20:22:25.8467339Z 4702c9016e32: Download complete 2024-08-06T20:22:25.9402335Z 4578fa00ba8a: Verifying Checksum 2024-08-06T20:22:25.9402884Z 4578fa00ba8a: Download complete 2024-08-06T20:22:26.0147538Z 5e0b7a7387b1: Verifying Checksum 2024-08-06T20:22:26.0147952Z 5e0b7a7387b1: Download complete 2024-08-06T20:22:26.0873878Z 3b12a15a2dd8: Download complete 2024-08-06T20:22:26.1604810Z efc9e54882ba: Verifying Checksum 2024-08-06T20:22:26.1605224Z efc9e54882ba: Download complete 2024-08-06T20:22:26.2480050Z bbcce21dddee: Verifying Checksum 2024-08-06T20:22:26.2480659Z bbcce21dddee: Download complete 2024-08-06T20:22:27.5442102Z 0ffc6f4509fe: Verifying Checksum 2024-08-06T20:22:27.5442659Z 0ffc6f4509fe: Download complete 2024-08-06T20:22:27.6273267Z c43dca7e37c0: Download complete 2024-08-06T20:22:27.6766119Z 2d2a994403d3: Verifying Checksum 2024-08-06T20:22:27.6766713Z 2d2a994403d3: Download complete 2024-08-06T20:22:27.6908680Z 380952bfb408: Verifying Checksum 2024-08-06T20:22:27.6909206Z 380952bfb408: Download complete 2024-08-06T20:22:27.7596509Z 62a07c6fa5fc: Download complete 2024-08-06T20:22:27.7624559Z ceb2379cb498: Verifying Checksum 2024-08-06T20:22:27.7625035Z ceb2379cb498: Download complete 2024-08-06T20:22:27.8532319Z 8b5570c513a9: Download complete 2024-08-06T20:22:27.9719484Z 39d053a85f7c: Download complete 2024-08-06T20:22:28.0350506Z 04f7d0566845: Download complete 2024-08-06T20:22:28.1163335Z 87202a98d131: Verifying Checksum 2024-08-06T20:22:28.1163902Z 87202a98d131: Download complete 2024-08-06T20:22:28.1901605Z 3d149566d7ff: Verifying Checksum 2024-08-06T20:22:28.1902235Z 3d149566d7ff: Download complete 2024-08-06T20:22:28.2751885Z 18983ac58952: Verifying Checksum 2024-08-06T20:22:28.2752393Z 18983ac58952: Download complete 2024-08-06T20:22:28.3481083Z d1f3066e929d: Download complete 2024-08-06T20:22:28.4212566Z a3cbced61652: Verifying Checksum 2024-08-06T20:22:28.4213205Z a3cbced61652: Download complete 2024-08-06T20:22:28.4948904Z e5ca241cc120: Verifying Checksum 2024-08-06T20:22:28.4949443Z e5ca241cc120: Download complete 2024-08-06T20:22:28.5729777Z 2a68b738ffe1: Verifying Checksum 2024-08-06T20:22:28.5730200Z 2a68b738ffe1: Download complete 2024-08-06T20:22:28.6490797Z 3767fa07553a: Download complete 2024-08-06T20:22:28.7265060Z 1367c503435c: Download complete 2024-08-06T20:22:28.7935522Z 3106fe392226: Verifying Checksum 2024-08-06T20:22:28.7936151Z 3106fe392226: Download complete 2024-08-06T20:22:29.3166318Z e673200c085d: Verifying Checksum 2024-08-06T20:22:29.3166786Z e673200c085d: Download complete 2024-08-06T20:22:29.3857520Z 734e3d036d23: Download complete 2024-08-06T20:22:29.4690716Z d7ff8fb3636b: Download complete 2024-08-06T20:22:29.5414269Z 1cce4640fddc: Download complete 2024-08-06T20:22:29.6241033Z 427d59d82d28: Verifying Checksum 2024-08-06T20:22:29.6241674Z 427d59d82d28: Download complete 2024-08-06T20:22:29.8844676Z 411f34d6d581: Verifying Checksum 2024-08-06T20:22:29.8845502Z 411f34d6d581: Download complete 2024-08-06T20:22:29.9574947Z 4d13e386b5bf: Download complete 2024-08-06T20:22:30.0205308Z 0f9cdfa3c55d: Download complete 2024-08-06T20:22:30.1167619Z cfbf800d7f89: Download complete 2024-08-06T20:22:30.1951885Z 31934c84f92c: Verifying Checksum 2024-08-06T20:22:30.1952398Z 31934c84f92c: Download complete 2024-08-06T20:22:30.2730010Z 91254fa13f25: Verifying Checksum 2024-08-06T20:22:30.2730535Z 91254fa13f25: Download complete 2024-08-06T20:22:30.3436150Z 13d99bf6f074: Verifying Checksum 2024-08-06T20:22:30.3436725Z 13d99bf6f074: Download complete 2024-08-06T20:22:30.4137631Z aa216cf24a5c: Verifying Checksum 2024-08-06T20:22:30.4138210Z aa216cf24a5c: Download complete 2024-08-06T20:22:30.4919043Z 8a75636074ed: Verifying Checksum 2024-08-06T20:22:30.4919584Z 8a75636074ed: Download complete 2024-08-06T20:22:30.5601027Z bc60d0ee2efa: Verifying Checksum 2024-08-06T20:22:30.5601514Z bc60d0ee2efa: Download complete 2024-08-06T20:22:30.6325794Z d7b6bfdc4ac4: Verifying Checksum 2024-08-06T20:22:30.6326384Z d7b6bfdc4ac4: Download complete 2024-08-06T20:22:30.7088617Z 362577cee720: Download complete 2024-08-06T20:22:30.7848548Z dd997f1a9a7a: Download complete 2024-08-06T20:22:30.8631497Z e1b7d62c8da3: Download complete 2024-08-06T20:22:30.9459568Z 5b5ac0963a87: Verifying Checksum 2024-08-06T20:22:30.9460109Z 5b5ac0963a87: Download complete 2024-08-06T20:22:31.0238893Z 0926e4f103f3: Verifying Checksum 2024-08-06T20:22:31.0239510Z 0926e4f103f3: Download complete 2024-08-06T20:22:31.1013934Z f29ef8d2e14d: Verifying Checksum 2024-08-06T20:22:31.1014366Z f29ef8d2e14d: Download complete 2024-08-06T20:22:31.1830676Z a1f65f5ee1b9: Verifying Checksum 2024-08-06T20:22:31.1831330Z a1f65f5ee1b9: Download complete 2024-08-06T20:22:31.2552823Z b612a56e3264: Download complete 2024-08-06T20:22:31.3217473Z 758620454102: Verifying Checksum 2024-08-06T20:22:31.3218051Z 758620454102: Download complete 2024-08-06T20:22:31.4695029Z 53bc8a6759fc: Download complete 2024-08-06T20:22:31.5497159Z df484cabb091: Download complete 2024-08-06T20:22:31.6230337Z c6845d12fa9c: Verifying Checksum 2024-08-06T20:22:31.6230843Z c6845d12fa9c: Download complete 2024-08-06T20:22:31.6929740Z 753c44cee6e1: Download complete 2024-08-06T20:22:31.7786986Z 44405015d608: Download complete 2024-08-06T20:22:31.8505767Z c4f461623146: Download complete 2024-08-06T20:22:31.9275058Z 6acbdc2527c9: Verifying Checksum 2024-08-06T20:22:31.9277991Z 6acbdc2527c9: Download complete 2024-08-06T20:22:32.5202708Z 82264f8de02a: Verifying Checksum 2024-08-06T20:22:32.5203133Z 82264f8de02a: Download complete 2024-08-06T20:22:32.5721420Z 88d03497075e: Verifying Checksum 2024-08-06T20:22:32.5722045Z 88d03497075e: Download complete 2024-08-06T20:22:36.0350411Z 2d2a994403d3: Pull complete 2024-08-06T20:22:36.1887153Z 8bb2baf0f937: Pull complete 2024-08-06T20:22:37.8079575Z 2a29c47c7450: Pull complete 2024-08-06T20:22:37.9822420Z 60ae3239e75d: Pull complete 2024-08-06T20:22:38.1915624Z 0e2a419f6d38: Pull complete 2024-08-06T20:22:38.3190717Z 2f7615e08b26: Pull complete 2024-08-06T20:22:38.4864408Z 5231e76a47c7: Pull complete 2024-08-06T20:22:38.6577028Z 32d63b5acf81: Pull complete 2024-08-06T20:22:38.8780195Z e9f6f1ca5fb0: Pull complete 2024-08-06T20:22:39.0481039Z 5715243311bd: Pull complete 2024-08-06T20:22:51.2957508Z 2ac7d65122e5: Verifying Checksum 2024-08-06T20:22:51.2957917Z 2ac7d65122e5: Download complete 2024-08-06T20:23:32.3983757Z 2ac7d65122e5: Pull complete 2024-08-06T20:23:32.4168021Z 4f4fb700ef54: Pull complete 2024-08-06T20:23:32.4351230Z 6b61f599dafa: Pull complete 2024-08-06T20:23:32.4527393Z 1f11bb841d2a: Pull complete 2024-08-06T20:23:32.4689061Z fab16dc9bed3: Pull complete 2024-08-06T20:23:32.5238186Z 4702c9016e32: Pull complete 2024-08-06T20:23:32.5414687Z 4578fa00ba8a: Pull complete 2024-08-06T20:23:32.5587585Z 5e0b7a7387b1: Pull complete 2024-08-06T20:23:32.5791828Z 3b12a15a2dd8: Pull complete 2024-08-06T20:23:32.6177445Z efc9e54882ba: Pull complete 2024-08-06T20:23:32.6365644Z bbcce21dddee: Pull complete 2024-08-06T20:23:35.4485600Z 0ffc6f4509fe: Pull complete 2024-08-06T20:23:35.6684612Z c43dca7e37c0: Pull complete 2024-08-06T20:23:35.8908729Z 380952bfb408: Pull complete 2024-08-06T20:23:36.0659163Z ceb2379cb498: Pull complete 2024-08-06T20:23:36.2852357Z 62a07c6fa5fc: Pull complete 2024-08-06T20:23:36.4552842Z 8b5570c513a9: Pull complete 2024-08-06T20:23:43.9895629Z 88d03497075e: Pull complete 2024-08-06T20:23:44.0061785Z 39d053a85f7c: Pull complete 2024-08-06T20:23:44.0237086Z 04f7d0566845: Pull complete 2024-08-06T20:23:44.0386752Z 87202a98d131: Pull complete 2024-08-06T20:23:44.0545648Z 3d149566d7ff: Pull complete 2024-08-06T20:23:44.0875245Z 18983ac58952: Pull complete 2024-08-06T20:23:44.1032419Z d1f3066e929d: Pull complete 2024-08-06T20:23:44.1198574Z a3cbced61652: Pull complete 2024-08-06T20:23:44.1352010Z e5ca241cc120: Pull complete 2024-08-06T20:23:44.1664408Z 2a68b738ffe1: Pull complete 2024-08-06T20:23:44.1831946Z 3767fa07553a: Pull complete 2024-08-06T20:23:44.2142985Z 1367c503435c: Pull complete 2024-08-06T20:23:44.2300519Z 3106fe392226: Pull complete 2024-08-06T20:23:45.7372654Z e673200c085d: Pull complete 2024-08-06T20:23:45.7529610Z 734e3d036d23: Pull complete 2024-08-06T20:23:45.7677676Z 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308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:23:53.6682962Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:23:53.6727487Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-08-06T20:23:53.6728209Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-08-06T20:23:53.6736172Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:23:53.6736566Z env: 2024-08-06T20:23:53.6736780Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:23:53.6737050Z ##[endgroup] 2024-08-06T20:23:53.6815993Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-08-06T20:23:53.6816587Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-08-06T20:23:53.6817107Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-08-06T20:23:53.6817598Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-08-06T20:23:53.6823167Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:23:53.6823556Z env: 2024-08-06T20:23:53.6823774Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:23:53.6824028Z ##[endgroup] 2024-08-06T20:23:54.1325295Z Defaulting to user installation because normal site-packages is not writeable 2024-08-06T20:23:54.1570840Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.9/site-packages (5.9.1) 2024-08-06T20:23:54.1576685Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.9/site-packages (11.525.84) 2024-08-06T20:23:54.3129756Z Prepare all required actions 2024-08-06T20:23:54.3130563Z Getting action download info 2024-08-06T20:23:54.4513244Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-08-06T20:23:54.7183397Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a) 2024-08-06T20:23:54.8717161Z ##[group]Run ./.github/actions/download-build-artifacts 2024-08-06T20:23:54.8717551Z with: 2024-08-06T20:23:54.8717871Z name: linux-focal-py3.12-clang10-experimental-split-build 2024-08-06T20:23:54.8718280Z s3-bucket: gha-artifacts 2024-08-06T20:23:54.8718551Z env: 2024-08-06T20:23:54.8718772Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:23:54.8719023Z ##[endgroup] 2024-08-06T20:23:54.8756382Z ##[group]Run seemethere/download-artifact-s3@v4 2024-08-06T20:23:54.8756740Z with: 2024-08-06T20:23:54.8757045Z name: linux-focal-py3.12-clang10-experimental-split-build 2024-08-06T20:23:54.8757523Z s3-bucket: gha-artifacts 2024-08-06T20:23:54.8757787Z region: us-east-1 2024-08-06T20:23:54.8758016Z env: 2024-08-06T20:23:54.8758236Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:23:54.8758491Z ##[endgroup] 2024-08-06T20:23:55.3557687Z (node:273585) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-08-06T20:23:55.3558210Z 2024-08-06T20:23:55.3558630Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-08-06T20:23:55.3559177Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-08-06T20:23:55.3560028Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-08-06T20:23:55.4251889Z Found 1 objects with prefix pytorch/pytorch/10273124344/linux-focal-py3.12-clang10-experimental-split-build/ 2024-08-06T20:23:55.4252785Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-08-06T20:24:03.7452633Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-08-06T20:24:03.7460175Z Artifact download has finished successfully 2024-08-06T20:24:03.7618220Z ##[group]Run unzip -o artifacts.zip 2024-08-06T20:24:03.7618579Z unzip -o artifacts.zip 2024-08-06T20:24:03.7624221Z shell: 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2024-08-06T20:24:09.0537461Z creating: build/lib/torch/ao/quantization/pt2e/ 2024-08-06T20:24:09.0538314Z extracting: build/lib/torch/ao/quantization/pt2e/__init__.py 2024-08-06T20:24:09.0539355Z inflating: build/lib/torch/ao/quantization/pt2e/_numeric_debugger.py 2024-08-06T20:24:09.0540270Z inflating: build/lib/torch/ao/quantization/pt2e/duplicate_dq_pass.py 2024-08-06T20:24:09.0541240Z inflating: build/lib/torch/ao/quantization/pt2e/export_utils.py 2024-08-06T20:24:09.0541945Z inflating: build/lib/torch/ao/quantization/pt2e/graph_utils.py 2024-08-06T20:24:09.0543071Z inflating: build/lib/torch/ao/quantization/pt2e/port_metadata_pass.py 2024-08-06T20:24:09.0544580Z inflating: build/lib/torch/ao/quantization/pt2e/prepare.py 2024-08-06T20:24:09.0547689Z inflating: build/lib/torch/ao/quantization/pt2e/qat_utils.py 2024-08-06T20:24:09.0549809Z inflating: build/lib/torch/ao/quantization/pt2e/utils.py 2024-08-06T20:24:09.0550824Z creating: 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inflating: build/lib/torch/include/ATen/jiterator_macros.h 2024-08-06T20:24:10.2739548Z inflating: build/lib/torch/include/ATen/record_function.h 2024-08-06T20:24:10.2740468Z inflating: build/lib/torch/include/ATen/CPUFunctions.h 2024-08-06T20:24:10.2741995Z inflating: build/lib/torch/include/ATen/CPUFunctions_inl.h 2024-08-06T20:24:10.2743189Z inflating: build/lib/torch/include/ATen/CompositeExplicitAutogradFunctions.h 2024-08-06T20:24:10.2745235Z inflating: build/lib/torch/include/ATen/CompositeExplicitAutogradFunctions_inl.h 2024-08-06T20:24:10.2746420Z inflating: build/lib/torch/include/ATen/CompositeExplicitAutogradNonFunctionalFunctions.h 2024-08-06T20:24:10.2748168Z inflating: build/lib/torch/include/ATen/CompositeExplicitAutogradNonFunctionalFunctions_inl.h 2024-08-06T20:24:10.2749014Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradFunctions.h 2024-08-06T20:24:10.2750086Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradFunctions_inl.h 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build/lib/torch/include/ATen/RegistrationDeclarations.h 2024-08-06T20:24:10.2961686Z inflating: build/lib/torch/include/ATen/VmapGeneratedPlumbing.h 2024-08-06T20:24:10.2962671Z inflating: build/lib/torch/include/ATen/CUDAFunctions.h 2024-08-06T20:24:10.2964391Z inflating: build/lib/torch/include/ATen/CUDAFunctions_inl.h 2024-08-06T20:24:10.2965266Z creating: build/lib/torch/include/ATen/cpu/ 2024-08-06T20:24:10.2966082Z inflating: build/lib/torch/include/ATen/cpu/FlushDenormal.h 2024-08-06T20:24:10.2966816Z inflating: build/lib/torch/include/ATen/cpu/Utils.h 2024-08-06T20:24:10.2967619Z inflating: build/lib/torch/include/ATen/cpu/vml.h 2024-08-06T20:24:10.2968071Z creating: build/lib/torch/include/ATen/cpu/vec/ 2024-08-06T20:24:10.2968871Z creating: build/lib/torch/include/ATen/cpu/vec/vec256/ 2024-08-06T20:24:10.2969568Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/missing_vld1_neon.h 2024-08-06T20:24:10.2970606Z inflating: 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inflating: build/lib/torch/include/ATen/cpu/vec/vec_n.h 2024-08-06T20:24:10.3051613Z creating: build/lib/torch/include/ATen/core/ 2024-08-06T20:24:10.3052426Z inflating: build/lib/torch/include/ATen/core/ATenGeneral.h 2024-08-06T20:24:10.3053328Z inflating: build/lib/torch/include/ATen/core/ATenOpList.h 2024-08-06T20:24:10.3054248Z inflating: build/lib/torch/include/ATen/core/ATen_fwd.h 2024-08-06T20:24:10.3054971Z inflating: build/lib/torch/include/ATen/core/ATen_pch.h 2024-08-06T20:24:10.3055424Z inflating: build/lib/torch/include/ATen/core/Array.h 2024-08-06T20:24:10.3056209Z inflating: build/lib/torch/include/ATen/core/Backtrace.h 2024-08-06T20:24:10.3057379Z inflating: build/lib/torch/include/ATen/core/CachingHostAllocator.h 2024-08-06T20:24:10.3058343Z inflating: build/lib/torch/include/ATen/core/CheckMemoryFormat.h 2024-08-06T20:24:10.3059016Z inflating: build/lib/torch/include/ATen/core/DeprecatedTypeProperties.h 2024-08-06T20:24:10.3060249Z inflating: build/lib/torch/include/ATen/core/DeprecatedTypePropertiesRegistry.h 2024-08-06T20:24:10.3061387Z inflating: build/lib/torch/include/ATen/core/Dict.h 2024-08-06T20:24:10.3061925Z inflating: build/lib/torch/include/ATen/core/Dict_inl.h 2024-08-06T20:24:10.3062750Z inflating: build/lib/torch/include/ATen/core/DimVector.h 2024-08-06T20:24:10.3063448Z inflating: build/lib/torch/include/ATen/core/Dimname.h 2024-08-06T20:24:10.3064424Z inflating: build/lib/torch/include/ATen/core/DistributionsHelper.h 2024-08-06T20:24:10.3065130Z inflating: build/lib/torch/include/ATen/core/Formatting.h 2024-08-06T20:24:10.3065618Z inflating: build/lib/torch/include/ATen/core/Generator.h 2024-08-06T20:24:10.3066395Z inflating: build/lib/torch/include/ATen/core/GeneratorForPrivateuseone.h 2024-08-06T20:24:10.3067562Z inflating: build/lib/torch/include/ATen/core/IListRef.h 2024-08-06T20:24:10.3068426Z inflating: build/lib/torch/include/ATen/core/IListRef_inl.h 2024-08-06T20:24:10.3069021Z inflating: build/lib/torch/include/ATen/core/LegacyTypeDispatch.h 2024-08-06T20:24:10.3069776Z inflating: build/lib/torch/include/ATen/core/List.h 2024-08-06T20:24:10.3071240Z inflating: build/lib/torch/include/ATen/core/List_inl.h 2024-08-06T20:24:10.3072324Z inflating: build/lib/torch/include/ATen/core/MT19937RNGEngine.h 2024-08-06T20:24:10.3073375Z inflating: build/lib/torch/include/ATen/core/NamedTensor.h 2024-08-06T20:24:10.3074330Z inflating: build/lib/torch/include/ATen/core/NestedIntSymNodeImpl.h 2024-08-06T20:24:10.3075332Z inflating: build/lib/torch/include/ATen/core/PhiloxRNGEngine.h 2024-08-06T20:24:10.3076475Z inflating: build/lib/torch/include/ATen/core/PythonFallbackKernel.h 2024-08-06T20:24:10.3077762Z inflating: build/lib/torch/include/ATen/core/PythonOpRegistrationTrampoline.h 2024-08-06T20:24:10.3078972Z inflating: build/lib/torch/include/ATen/core/QuantizerBase.h 2024-08-06T20:24:10.3079818Z inflating: build/lib/torch/include/ATen/core/Range.h 2024-08-06T20:24:10.3080634Z inflating: build/lib/torch/include/ATen/core/Reduction.h 2024-08-06T20:24:10.3081232Z extracting: build/lib/torch/include/ATen/core/Scalar.h 2024-08-06T20:24:10.3081727Z extracting: build/lib/torch/include/ATen/core/ScalarType.h 2024-08-06T20:24:10.3082217Z inflating: build/lib/torch/include/ATen/core/Tensor.h 2024-08-06T20:24:10.3082700Z inflating: build/lib/torch/include/ATen/core/TensorAccessor.h 2024-08-06T20:24:10.3083708Z inflating: build/lib/torch/include/ATen/core/TensorBase.h 2024-08-06T20:24:10.3084687Z inflating: build/lib/torch/include/ATen/core/TorchDispatchUtils.h 2024-08-06T20:24:10.3085599Z inflating: build/lib/torch/include/ATen/core/TransformationHelper.h 2024-08-06T20:24:10.3086712Z extracting: build/lib/torch/include/ATen/core/UndefinedTensorImpl.h 2024-08-06T20:24:10.3087762Z inflating: build/lib/torch/include/ATen/core/UnsafeFromTH.h 2024-08-06T20:24:10.3088864Z inflating: build/lib/torch/include/ATen/core/VariableHooksInterface.h 2024-08-06T20:24:10.3089802Z 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build/lib/torch/include/ATen/cuda/Exceptions.h 2024-08-06T20:24:10.3199374Z inflating: build/lib/torch/include/ATen/cuda/PeerToPeerAccess.h 2024-08-06T20:24:10.3200184Z inflating: build/lib/torch/include/ATen/cuda/PhiloxCudaState.h 2024-08-06T20:24:10.3201316Z inflating: build/lib/torch/include/ATen/cuda/PinnedMemoryAllocator.h 2024-08-06T20:24:10.3202241Z inflating: build/lib/torch/include/ATen/cuda/Sleep.h 2024-08-06T20:24:10.3203156Z inflating: build/lib/torch/include/ATen/cuda/ThrustAllocator.h 2024-08-06T20:24:10.3204108Z inflating: build/lib/torch/include/ATen/cuda/cub.h 2024-08-06T20:24:10.3204948Z inflating: build/lib/torch/include/ATen/cuda/jiterator.h 2024-08-06T20:24:10.3206103Z inflating: build/lib/torch/include/ATen/cuda/jiterator_impl.h 2024-08-06T20:24:10.3206709Z inflating: build/lib/torch/include/ATen/cuda/llvm_jit_strings.h 2024-08-06T20:24:10.3207482Z creating: build/lib/torch/include/ATen/cuda/detail/ 2024-08-06T20:24:10.3208439Z inflating: build/lib/torch/include/ATen/cuda/detail/IndexUtils.cuh 2024-08-06T20:24:10.3209349Z inflating: build/lib/torch/include/ATen/cuda/detail/IntegerDivider.cuh 2024-08-06T20:24:10.3210347Z inflating: build/lib/torch/include/ATen/cuda/detail/OffsetCalculator.cuh 2024-08-06T20:24:10.3211305Z inflating: build/lib/torch/include/ATen/cuda/detail/PhiloxCudaStateRaw.cuh 2024-08-06T20:24:10.3212292Z inflating: build/lib/torch/include/ATen/cuda/detail/TensorInfo.cuh 2024-08-06T20:24:10.3213198Z inflating: build/lib/torch/include/ATen/cuda/detail/UnpackRaw.cuh 2024-08-06T20:24:10.3214152Z inflating: build/lib/torch/include/ATen/cuda/detail/CUDAHooks.h 2024-08-06T20:24:10.3215152Z inflating: build/lib/torch/include/ATen/cuda/detail/DeviceThreadHandles.h 2024-08-06T20:24:10.3215982Z inflating: build/lib/torch/include/ATen/cuda/detail/KernelUtils.h 2024-08-06T20:24:10.3216810Z inflating: build/lib/torch/include/ATen/cuda/detail/LazyNVRTC.h 2024-08-06T20:24:10.3217318Z creating: 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build/bin/c10_accumulate_test 2024-08-06T20:24:14.4221913Z inflating: build/bin/c10_bit_cast_test 2024-08-06T20:24:14.4273297Z inflating: build/bin/c10_string_view_test 2024-08-06T20:24:14.4321774Z inflating: build/bin/c10_bfloat16_test 2024-08-06T20:24:14.4367447Z inflating: build/bin/c10_irange_test 2024-08-06T20:24:14.4414075Z inflating: build/bin/c10_exception_test 2024-08-06T20:24:14.4463025Z inflating: build/bin/c10_complex_test 2024-08-06T20:24:14.4509073Z inflating: build/bin/c10_flags_test 2024-08-06T20:24:14.4553669Z inflating: build/bin/c10_generic_math_test 2024-08-06T20:24:14.4682397Z inflating: build/bin/c10_intrusive_ptr_test 2024-08-06T20:24:14.4731989Z inflating: build/bin/c10_logging_test 2024-08-06T20:24:14.4783473Z inflating: build/bin/c10_complex_math_test 2024-08-06T20:24:14.4852873Z inflating: build/bin/c10_optional_test 2024-08-06T20:24:14.4900901Z inflating: build/bin/c10_registry_test 2024-08-06T20:24:14.4947851Z inflating: build/bin/c10_lazy_test 2024-08-06T20:24:14.4995463Z inflating: build/bin/c10_Metaprogramming_test 2024-08-06T20:24:14.5040872Z inflating: build/bin/c10_string_util_test 2024-08-06T20:24:14.5170107Z inflating: build/bin/c10_small_vector_test 2024-08-06T20:24:14.5174029Z inflating: build/bin/torch_shm_manager 2024-08-06T20:24:14.5174380Z creating: .additional_ci_files/ 2024-08-06T20:24:14.5229745Z inflating: .additional_ci_files/test-times.json 2024-08-06T20:24:14.5459137Z inflating: .additional_ci_files/test-class-times.json 2024-08-06T20:24:14.5511555Z ##[group]Run rm artifacts.zip 2024-08-06T20:24:14.5511898Z rm artifacts.zip 2024-08-06T20:24:14.5521181Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:24:14.5521568Z env: 2024-08-06T20:24:14.5521799Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:14.5522075Z ##[endgroup] 2024-08-06T20:24:14.6113503Z ##[group]Run df -H 2024-08-06T20:24:14.6113788Z df -H 2024-08-06T20:24:14.6119624Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:24:14.6120014Z env: 2024-08-06T20:24:14.6120235Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:14.6120493Z ##[endgroup] 2024-08-06T20:24:14.6348067Z Filesystem Size Used Avail Use% Mounted on 2024-08-06T20:24:14.6348658Z devtmpfs 4.2M 0 4.2M 0% /dev 2024-08-06T20:24:14.6349011Z tmpfs 8.2G 0 8.2G 0% /dev/shm 2024-08-06T20:24:14.6349349Z tmpfs 3.3G 488k 3.3G 1% /run 2024-08-06T20:24:14.6349685Z /dev/nvme0n1p1 161G 22G 140G 14% / 2024-08-06T20:24:14.6350200Z tmpfs 8.2G 13k 8.2G 1% /tmp 2024-08-06T20:24:14.6350549Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2024-08-06T20:24:14.6431029Z Prepare all required actions 2024-08-06T20:24:14.6431408Z Getting action download info 2024-08-06T20:24:14.7769816Z ##[group]Run ./.github/actions/download-td-artifacts 2024-08-06T20:24:14.7770194Z with: 2024-08-06T20:24:14.7770418Z env: 2024-08-06T20:24:14.7770638Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:14.7770909Z ##[endgroup] 2024-08-06T20:24:14.7849066Z ##[group]Run seemethere/download-artifact-s3@v4 2024-08-06T20:24:14.7849423Z with: 2024-08-06T20:24:14.7849640Z name: td_results 2024-08-06T20:24:14.7849874Z s3-bucket: gha-artifacts 2024-08-06T20:24:14.7850151Z region: us-east-1 2024-08-06T20:24:14.7850385Z env: 2024-08-06T20:24:14.7850592Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:14.7850861Z ##[endgroup] 2024-08-06T20:24:15.2643113Z (node:273603) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-08-06T20:24:15.2643635Z 2024-08-06T20:24:15.2643925Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-08-06T20:24:15.2644476Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-08-06T20:24:15.2645054Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-08-06T20:24:15.3472006Z Found 1 objects with prefix pytorch/pytorch/10273124344/td_results/ 2024-08-06T20:24:15.3473225Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-08-06T20:24:15.4899211Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-08-06T20:24:15.4905212Z Artifact download has finished successfully 2024-08-06T20:24:15.5114179Z ##[group]Run mkdir -p .additional_ci_files 2024-08-06T20:24:15.5114584Z mkdir -p .additional_ci_files 2024-08-06T20:24:15.5114993Z mv td_results.json .additional_ci_files/td_results.json 2024-08-06T20:24:15.5120825Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:24:15.5121216Z env: 2024-08-06T20:24:15.5121443Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:15.5121709Z ##[endgroup] 2024-08-06T20:24:15.5482621Z ##[group]Run .github/scripts/parse_ref.py 2024-08-06T20:24:15.5483029Z .github/scripts/parse_ref.py 2024-08-06T20:24:15.5488642Z shell: /usr/bin/bash -e {0} 2024-08-06T20:24:15.5488931Z env: 2024-08-06T20:24:15.5489158Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:15.5489425Z ##[endgroup] 2024-08-06T20:24:15.5969689Z Prepare all required actions 2024-08-06T20:24:15.6061512Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-08-06T20:24:15.6061898Z with: 2024-08-06T20:24:15.6062317Z github-token: *** 2024-08-06T20:24:15.6062558Z env: 2024-08-06T20:24:15.6062781Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:15.6063040Z ##[endgroup] 2024-08-06T20:24:15.6107769Z ##[group]Run set -eux 2024-08-06T20:24:15.6108060Z set -eux 2024-08-06T20:24:15.6108506Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-08-06T20:24:15.6114425Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:24:15.6114814Z env: 2024-08-06T20:24:15.6115028Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:15.6115564Z GITHUB_TOKEN: *** 2024-08-06T20:24:15.6115812Z ##[endgroup] 2024-08-06T20:24:15.6139042Z + python3 .github/scripts/get_workflow_job_id.py 10273124344 i-03096d6fe1daee476 2024-08-06T20:24:20.0759156Z setting job-id=28427567353 2024-08-06T20:24:20.0759947Z setting job-name=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, amz2023.linux.2xlarge) 2024-08-06T20:24:20.1040548Z Prepare all required actions 2024-08-06T20:24:20.1041506Z Getting action download info 2024-08-06T20:24:20.2590085Z ##[group]Run ./.github/actions/filter-test-configs 2024-08-06T20:24:20.2590467Z with: 2024-08-06T20:24:20.2590907Z github-token: *** 2024-08-06T20:24:20.2592692Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]} 2024-08-06T20:24:20.2594880Z job-name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, amz2023.linux.2xlarge) 2024-08-06T20:24:20.2595539Z env: 2024-08-06T20:24:20.2595785Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:20.2596059Z ##[endgroup] 2024-08-06T20:24:20.2637773Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-08-06T20:24:20.2638201Z with: 2024-08-06T20:24:20.2638420Z shell: bash 2024-08-06T20:24:20.2638658Z timeout_minutes: 10 2024-08-06T20:24:20.2638900Z max_attempts: 5 2024-08-06T20:24:20.2639149Z retry_wait_seconds: 30 2024-08-06T20:24:20.2639960Z 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 2024-08-06T20:24:20.2640816Z polling_interval_seconds: 1 2024-08-06T20:24:20.2641093Z warning_on_retry: true 2024-08-06T20:24:20.2641383Z continue_on_error: false 2024-08-06T20:24:20.2641635Z env: 2024-08-06T20:24:20.2641852Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:20.2642606Z GITHUB_TOKEN: *** 2024-08-06T20:24:20.2642868Z ##[endgroup] 2024-08-06T20:24:20.3329658Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-08-06T20:24:20.5781550Z Defaulting to user installation because normal site-packages is not writeable 2024-08-06T20:24:20.5941845Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1) 2024-08-06T20:24:20.5945992Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1) 2024-08-06T20:24:20.6063090Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2024-08-06T20:24:20.6073553Z 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) 2024-08-06T20:24:20.6077545Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2024.7.4) 2024-08-06T20:24:20.6087744Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2024-08-06T20:24:21.3154660Z Command completed after 1 attempt(s). 2024-08-06T20:24:21.3204212Z ##[group]Run set -x 2024-08-06T20:24:21.3204500Z set -x 2024-08-06T20:24:21.3204722Z  2024-08-06T20:24:21.3205118Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-08-06T20:24:21.3205617Z # in runner workspace 2024-08-06T20:24:21.3206000Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-08-06T20:24:21.3211919Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:24:21.3212320Z env: 2024-08-06T20:24:21.3212539Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:21.3212811Z ##[endgroup] 2024-08-06T20:24:21.3237590Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-08-06T20:24:21.3480957Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-08-06T20:24:21.3481587Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-08-06T20:24:21.3481927Z echo "Job name: ${JOB_NAME}" 2024-08-06T20:24:21.3482232Z  2024-08-06T20:24:21.3482624Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-08-06T20:24:21.3483107Z # in runner workspace 2024-08-06T20:24:21.3483544Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-08-06T20:24:21.3484043Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-08-06T20:24:21.3484374Z  --job-name "${JOB_NAME}" \ 2024-08-06T20:24:21.3486258Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]}" \ 2024-08-06T20:24:21.3488174Z  --selected-test-configs "" \ 2024-08-06T20:24:21.3488524Z  --pr-number "${PR_NUMBER}" \ 2024-08-06T20:24:21.3488849Z  --tag "${TAG}" \ 2024-08-06T20:24:21.3489137Z  --event-name "${EVENT_NAME}" \ 2024-08-06T20:24:21.3489471Z  --schedule "${SCHEDULE}" \ 2024-08-06T20:24:21.3489797Z  --branch "${HEAD_BRANCH}" 2024-08-06T20:24:21.3495414Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:24:21.3495814Z env: 2024-08-06T20:24:21.3496040Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:21.3496525Z GITHUB_TOKEN: *** 2024-08-06T20:24:21.3497080Z JOB_NAME: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, amz2023.linux.2xlarge) 2024-08-06T20:24:21.3497700Z PR_NUMBER: 132710 2024-08-06T20:24:21.3497934Z TAG: 2024-08-06T20:24:21.3498157Z EVENT_NAME: pull_request 2024-08-06T20:24:21.3498429Z SCHEDULE: 2024-08-06T20:24:21.3498647Z HEAD_BRANCH: 2024-08-06T20:24:21.3498883Z ##[endgroup] 2024-08-06T20:24:21.3521832Z Workflow: pull 2024-08-06T20:24:21.3522406Z Job name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, amz2023.linux.2xlarge) 2024-08-06T20:24:21.6114540Z INFO:root:Found no test-config label on the PR, so all test configs are included 2024-08-06T20:24:21.7991496Z ##[group]Run echo "Filtered matrix:" 2024-08-06T20:24:21.7991861Z echo "Filtered matrix:" 2024-08-06T20:24:21.7993718Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]}" 2024-08-06T20:24:21.7995579Z  2024-08-06T20:24:21.7995797Z echo 2024-08-06T20:24:21.7996084Z echo "Is the current job unstable? False" 2024-08-06T20:24:21.7996425Z  2024-08-06T20:24:21.7996643Z echo 2024-08-06T20:24:21.7996901Z echo "Is keep-going label set? False" 2024-08-06T20:24:21.7997238Z  2024-08-06T20:24:21.7997455Z echo 2024-08-06T20:24:21.7997686Z echo "Renabled issues? " 2024-08-06T20:24:21.8003380Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:24:21.8003779Z env: 2024-08-06T20:24:21.8004146Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:21.8004423Z ##[endgroup] 2024-08-06T20:24:21.8027687Z Filtered matrix: 2024-08-06T20:24:21.8030624Z {include: [{config: default, shard: 1, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: default, shard: 2, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: default, shard: 3, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 1, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 2, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 3, num_shards: 3, runner: amz2023.linux.2xlarge}]} 2024-08-06T20:24:21.8032353Z 2024-08-06T20:24:21.8032474Z Is the current job unstable? False 2024-08-06T20:24:21.8032695Z 2024-08-06T20:24:21.8032806Z Is keep-going label set? False 2024-08-06T20:24:21.8032994Z 2024-08-06T20:24:21.8033103Z Renabled issues? 2024-08-06T20:24:21.8079857Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-08-06T20:24:21.8080406Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-08-06T20:24:21.8085908Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T20:24:21.8086300Z env: 2024-08-06T20:24:21.8086514Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:21.8086784Z JOB_TIMEOUT: 600 2024-08-06T20:24:21.8087020Z ##[endgroup] 2024-08-06T20:24:21.8159457Z ##[group]Run set -x 2024-08-06T20:24:21.8159798Z set -x 2024-08-06T20:24:21.8160020Z  2024-08-06T20:24:21.8160282Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-08-06T20:24:21.8160691Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-08-06T20:24:21.8161091Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-08-06T20:24:21.8161468Z  TEST_COMMAND=.ci/onnx/test.sh 2024-08-06T20:24:21.8161785Z else 2024-08-06T20:24:21.8162035Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-08-06T20:24:21.8162363Z fi 2024-08-06T20:24:21.8162581Z  2024-08-06T20:24:21.8162931Z # detached container should get cleaned up by teardown_ec2_linux 2024-08-06T20:24:21.8163488Z # TODO: Stop building test binaries as part of the build phase 2024-08-06T20:24:21.8163974Z # Used for GPU_FLAG since that doesn't play nice 2024-08-06T20:24:21.8164408Z # shellcheck disable=SC2086,SC2090 2024-08-06T20:24:21.8164745Z container_name=$(docker run \ 2024-08-06T20:24:21.8165066Z  ${GPU_FLAG:-} \ 2024-08-06T20:24:21.8165356Z  -e BUILD_ENVIRONMENT \ 2024-08-06T20:24:21.8165655Z  -e PR_NUMBER \ 2024-08-06T20:24:21.8165946Z  -e GITHUB_ACTIONS \ 2024-08-06T20:24:21.8166252Z  -e GITHUB_REPOSITORY \ 2024-08-06T20:24:21.8166555Z  -e GITHUB_WORKFLOW \ 2024-08-06T20:24:21.8166852Z  -e GITHUB_JOB \ 2024-08-06T20:24:21.8167133Z  -e GITHUB_RUN_ID \ 2024-08-06T20:24:21.8167409Z  -e GITHUB_RUN_NUMBER \ 2024-08-06T20:24:21.8178555Z  -e GITHUB_RUN_ATTEMPT \ 2024-08-06T20:24:21.8178956Z  -e JOB_ID \ 2024-08-06T20:24:21.8179218Z  -e JOB_NAME \ 2024-08-06T20:24:21.8179479Z  -e BASE_SHA \ 2024-08-06T20:24:21.8179721Z  -e BRANCH \ 2024-08-06T20:24:21.8179963Z  -e SHA1 \ 2024-08-06T20:24:21.8180217Z  -e AWS_DEFAULT_REGION \ 2024-08-06T20:24:21.8180517Z  -e IN_WHEEL_TEST \ 2024-08-06T20:24:21.8180793Z  -e SHARD_NUMBER \ 2024-08-06T20:24:21.8181057Z  -e TEST_CONFIG \ 2024-08-06T20:24:21.8181328Z  -e NUM_TEST_SHARDS \ 2024-08-06T20:24:21.8181619Z  -e REENABLED_ISSUES \ 2024-08-06T20:24:21.8181915Z  -e CONTINUE_THROUGH_ERROR \ 2024-08-06T20:24:21.8182235Z  -e VERBOSE_TEST_LOGS \ 2024-08-06T20:24:21.8182536Z  -e TEST_SHOWLOCALS \ 2024-08-06T20:24:21.8182822Z  -e NO_TEST_TIMEOUT \ 2024-08-06T20:24:21.8183105Z  -e NO_TD \ 2024-08-06T20:24:21.8183361Z  -e TD_DISTRIBUTED \ 2024-08-06T20:24:21.8183827Z  -e PR_LABELS \ 2024-08-06T20:24:21.8184128Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-08-06T20:24:21.8184467Z  -e SCCACHE_BUCKET \ 2024-08-06T20:24:21.8184752Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-08-06T20:24:21.8185057Z  -e XLA_CUDA \ 2024-08-06T20:24:21.8185355Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-08-06T20:24:21.8185716Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-08-06T20:24:21.8186088Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-08-06T20:24:21.8186458Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-08-06T20:24:21.8186887Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-08-06T20:24:21.8187189Z  -e DASHBOARD_TAG \ 2024-08-06T20:24:21.8187527Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-08-06T20:24:21.8187928Z  --security-opt seccomp=unconfined \ 2024-08-06T20:24:21.8188264Z  --cap-add=SYS_PTRACE \ 2024-08-06T20:24:21.8188560Z  --ipc=host \ 2024-08-06T20:24:21.8188820Z  --shm-size="${SHM_SIZE}" \ 2024-08-06T20:24:21.8189101Z  --tty \ 2024-08-06T20:24:21.8189331Z  --detach \ 2024-08-06T20:24:21.8189587Z  --name="${container_name}" \ 2024-08-06T20:24:21.8189995Z  --user jenkins \ 2024-08-06T20:24:21.8190338Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-08-06T20:24:21.8190733Z  -w /var/lib/jenkins/workspace \ 2024-08-06T20:24:21.8191039Z  "${DOCKER_IMAGE}" 2024-08-06T20:24:21.8191296Z ) 2024-08-06T20:24:21.8191588Z # Propagate download.pytorch.org IP to container 2024-08-06T20:24:21.8192255Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-08-06T20:24:21.8192981Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-08-06T20:24:21.8193650Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-08-06T20:24:21.8199290Z shell: /usr/bin/bash -e {0} 2024-08-06T20:24:21.8199546Z env: 2024-08-06T20:24:21.8199745Z GIT_DEFAULT_BRANCH: main 2024-08-06T20:24:21.8200157Z BUILD_ENVIRONMENT: linux-focal-py3.12-clang10-experimental-split-build 2024-08-06T20:24:21.8200624Z PR_NUMBER: 132710 2024-08-06T20:24:21.8200875Z GITHUB_REPOSITORY: pytorch/pytorch 2024-08-06T20:24:21.8201166Z GITHUB_WORKFLOW: pull 2024-08-06T20:24:21.8201411Z GITHUB_JOB: test 2024-08-06T20:24:21.8201638Z GITHUB_RUN_ID: 10273124344 2024-08-06T20:24:21.8201895Z GITHUB_RUN_NUMBER: 233985 2024-08-06T20:24:21.8202157Z GITHUB_RUN_ATTEMPT: 1 2024-08-06T20:24:21.8202395Z JOB_ID: 28427567353 2024-08-06T20:24:21.8202921Z JOB_NAME: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, amz2023.linux.2xlarge) 2024-08-06T20:24:21.8203530Z BRANCH: pull/132710 2024-08-06T20:24:21.8203800Z SHA1: b9d86fa89636e301796d4201f36d86c73f6e49bc 2024-08-06T20:24:21.8204171Z BASE_SHA: 1736af7cf736184c356be1bb00f59fb2feea6d7d 2024-08-06T20:24:21.8204512Z TEST_CONFIG: dynamo 2024-08-06T20:24:21.8204742Z SHARD_NUMBER: 1 2024-08-06T20:24:21.8204957Z NUM_TEST_SHARDS: 3 2024-08-06T20:24:21.8205194Z REENABLED_ISSUES: 2024-08-06T20:24:21.8205450Z CONTINUE_THROUGH_ERROR: False 2024-08-06T20:24:21.8205723Z VERBOSE_TEST_LOGS: False 2024-08-06T20:24:21.8205989Z TEST_SHOWLOCALS: False 2024-08-06T20:24:21.8206253Z NO_TEST_TIMEOUT: False 2024-08-06T20:24:21.8206488Z NO_TD: False 2024-08-06T20:24:21.8206712Z TD_DISTRIBUTED: False 2024-08-06T20:24:21.8207021Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-08-06T20:24:21.8207374Z SCCACHE_S3_KEY_PREFIX: pull 2024-08-06T20:24:21.8207641Z SHM_SIZE: 1g 2024-08-06T20:24:21.8208311Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:24:21.8209047Z XLA_CUDA: 2024-08-06T20:24:21.8209500Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-08-06T20:24:21.8209955Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-08-06T20:24:21.8210269Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-08-06T20:24:21.8210574Z DASHBOARD_TAG: 2024-08-06T20:24:21.8210809Z HUGGING_FACE_HUB_TOKEN: 2024-08-06T20:24:21.8211059Z ##[endgroup] 2024-08-06T20:24:21.8234481Z + [[ dynamo == \m\u\l\t\i\g\p\u ]] 2024-08-06T20:24:21.8234956Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *onnx* ]] 2024-08-06T20:24:21.8235437Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-08-06T20:24:21.8242614Z +++ nproc --ignore=2 2024-08-06T20:24:21.8262951Z ++ 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_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 DASHBOARD_TAG --env-file=/tmp/github_env_10273124344 --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-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T20:24:26.1355996Z + container_name=b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T20:24:26.1357599Z + grep download.pytorch.org /etc/hosts 2024-08-06T20:24:26.1359176Z + docker exec -i b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 sudo bash -c '/bin/cat >> /etc/hosts' 2024-08-06T20:24:26.3418496Z + echo DOCKER_CONTAINER_ID=b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T20:24:26.3422017Z ++ echo dist/torch-2.5.0a0+gitb9d86fa-cp312-cp312-linux_x86_64.whl dist/torch_no_python-2.5.0a0+gitb9d86fa-py3-none-any.whl 2024-08-06T20:24:26.3424954Z + docker exec -t b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 sh -c 'pip install dist/torch-2.5.0a0+gitb9d86fa-cp312-cp312-linux_x86_64.whl dist/torch_no_python-2.5.0a0+gitb9d86fa-py3-none-any.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-08-06T20:24:27.2354973Z Processing ./dist/torch-2.5.0a0+gitb9d86fa-cp312-cp312-linux_x86_64.whl 2024-08-06T20:24:27.4568226Z Processing ./dist/torch_no_python-2.5.0a0+gitb9d86fa-py3-none-any.whl (from torch-no-python==2.5.0a0+gitb9d86fa) 2024-08-06T20:24:27.8168029Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb9d86fa) (3.13.1) 2024-08-06T20:24:27.8177441Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb9d86fa) (4.12.2) 2024-08-06T20:24:27.8185348Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb9d86fa) (2.8.8) 2024-08-06T20:24:27.8191752Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb9d86fa) (3.1.4) 2024-08-06T20:24:27.8205446Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb9d86fa) (2024.6.1) 2024-08-06T20:24:27.8214363Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb9d86fa) (69.5.1) 2024-08-06T20:24:27.8232266Z Requirement already satisfied: sympy>=1.13.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb9d86fa) (1.13.1) 2024-08-06T20:24:27.8378933Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch-no-python==2.5.0a0+gitb9d86fa->torch-no-python==2.5.0a0+gitb9d86fa) (3.3.0) 2024-08-06T20:24:27.8430987Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from opt-einsum>=3.3->torch-no-python==2.5.0a0+gitb9d86fa->torch-no-python==2.5.0a0+gitb9d86fa) (1.26.0) 2024-08-06T20:24:27.8468420Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy>=1.13.0->torch==2.5.0a0+gitb9d86fa) (1.3.0) 2024-08-06T20:24:27.9194842Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.5.0a0+gitb9d86fa) (2.1.5) 2024-08-06T20:24:28.6688365Z Installing collected packages: torch-no-python, torch 2024-08-06T20:24:39.9287580Z Successfully installed torch-2.5.0a0+gitb9d86fa torch-no-python-2.5.0a0+gitb9d86fa 2024-08-06T20:24:40.0572604Z ++ dirname .ci/pytorch/test.sh 2024-08-06T20:24:40.0602534Z + source .ci/pytorch/common.sh 2024-08-06T20:24:40.0608934Z +++ dirname .ci/pytorch/common.sh 2024-08-06T20:24:40.0615022Z ++ source .ci/pytorch/common_utils.sh 2024-08-06T20:24:40.0620359Z +++ declare -f -t trap_add 2024-08-06T20:24:40.0626997Z ++ set -ex 2024-08-06T20:24:40.0627696Z ++ [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-08-06T20:24:40.0628446Z ++ BUILD_TEST_LIBTORCH=0 2024-08-06T20:24:40.0628852Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *rocm* ]] 2024-08-06T20:24:40.0630761Z ++ stat -c %u /var/lib/jenkins/workspace 2024-08-06T20:24:40.0657750Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-08-06T20:24:40.0658329Z + trap_add cleanup_workspace EXIT 2024-08-06T20:24:40.0658676Z + trap_add_cmd=cleanup_workspace 2024-08-06T20:24:40.0659187Z + shift 2024-08-06T20:24:40.0659596Z + for trap_add_name in "$@" 2024-08-06T20:24:40.0664550Z +++ trap -p EXIT 2024-08-06T20:24:40.0667101Z ++ eval 'extract_trap_cmd ' 2024-08-06T20:24:40.0667626Z +++ extract_trap_cmd 2024-08-06T20:24:40.0668078Z +++ printf '%s\n' '' 2024-08-06T20:24:40.0668496Z ++ printf '%s\n' cleanup_workspace 2024-08-06T20:24:40.0670246Z + trap -- ' 2024-08-06T20:24:40.0670661Z cleanup_workspace' EXIT 2024-08-06T20:24:40.0671241Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-08-06T20:24:40.5781525Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-08-06T20:24:40.5796464Z + echo 'Environment variables:' 2024-08-06T20:24:40.5796850Z Environment variables: 2024-08-06T20:24:40.5797107Z + env 2024-08-06T20:24:40.5827255Z INSTALLED_DB=yes 2024-08-06T20:24:40.5827880Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-06T20:24:40.5828637Z CONTINUE_THROUGH_ERROR=False 2024-08-06T20:24:40.5829344Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10-experimental-split-build 2024-08-06T20:24:40.5829993Z HOSTNAME=b394f538c1f1 2024-08-06T20:24:40.5830900Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.5831794Z GITHUB_ACTION=__self 2024-08-06T20:24:40.5832070Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-08-06T20:24:40.5832397Z GITHUB_RUN_NUMBER=233985 2024-08-06T20:24:40.5832688Z TEST_CONFIG=dynamo 2024-08-06T20:24:40.5832969Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-08-06T20:24:40.5833381Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-08-06T20:24:40.5833708Z GITHUB_TRIGGERING_ACTOR=drisspg 2024-08-06T20:24:40.5834003Z GITHUB_REF_TYPE=branch 2024-08-06T20:24:40.5834271Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-08-06T20:24:40.5834585Z BASE_SHA=1736af7cf736184c356be1bb00f59fb2feea6d7d 2024-08-06T20:24:40.5834927Z XLA_CUDA= 2024-08-06T20:24:40.5835153Z HUGGING_FACE_HUB_TOKEN= 2024-08-06T20:24:40.5835597Z *** 2024-08-06T20:24:40.5835829Z GITHUB_REPOSITORY_ID=65600975 2024-08-06T20:24:40.5836122Z GITHUB_ACTIONS=true 2024-08-06T20:24:40.5836430Z SHA1=b9d86fa89636e301796d4201f36d86c73f6e49bc 2024-08-06T20:24:40.5837041Z GITHUB_SHA=bf5bb5a1585a03379137fab341e87c02c77e76cd 2024-08-06T20:24:40.5837602Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/132710/merge 2024-08-06T20:24:40.5838116Z UCC_HOME=/usr 2024-08-06T20:24:40.5838336Z VERBOSE_TEST_LOGS=False 2024-08-06T20:24:40.5838615Z GITHUB_REF=refs/pull/132710/merge 2024-08-06T20:24:40.5838907Z SHARD_NUMBER=1 2024-08-06T20:24:40.5839136Z GITHUB_REF_PROTECTED=false 2024-08-06T20:24:40.5839412Z HOME=/var/lib/jenkins 2024-08-06T20:24:40.5839698Z GITHUB_API_URL=https://api.github.com 2024-08-06T20:24:40.5840023Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-08-06T20:24:40.5840324Z UCX_COMMIT= 2024-08-06T20:24:40.5840554Z SCCACHE_S3_KEY_PREFIX=pull 2024-08-06T20:24:40.5840810Z NUM_TEST_SHARDS=3 2024-08-06T20:24:40.5841041Z UCX_HOME=/usr 2024-08-06T20:24:40.5841614Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.5842873Z JOB_NAME=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, amz2023.linux.2xlarge) 2024-08-06T20:24:40.5844222Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.5845225Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-08-06T20:24:40.5845746Z GITHUB_EVENT_NAME=pull_request 2024-08-06T20:24:40.5846037Z DASHBOARD_TAG= 2024-08-06T20:24:40.5846276Z GITHUB_RUN_ID=10273124344 2024-08-06T20:24:40.5846922Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.5847623Z GITHUB_ACTOR=drisspg 2024-08-06T20:24:40.5847876Z PR_NUMBER=132710 2024-08-06T20:24:40.5848101Z DESIRED_CUDA= 2024-08-06T20:24:40.5848342Z GITHUB_RUN_ATTEMPT=1 2024-08-06T20:24:40.5848610Z ANACONDA_PYTHON_VERSION=3.12 2024-08-06T20:24:40.5848946Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-08-06T20:24:40.5849312Z TERM=xterm 2024-08-06T20:24:40.5849545Z INSTALLED_VISION=yes 2024-08-06T20:24:40.5849785Z BRANCH=pull/132710 2024-08-06T20:24:40.5850040Z OPENSSL_ROOT_DIR=/opt/openssl 2024-08-06T20:24:40.5850316Z CUDA_PATH=/usr/local/cuda 2024-08-06T20:24:40.5850857Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-08-06T20:24:40.5851461Z GITHUB_SERVER_URL=https://github.com 2024-08-06T20:24:40.5851759Z UCC_COMMIT= 2024-08-06T20:24:40.5851980Z REENABLED_ISSUES= 2024-08-06T20:24:40.5852212Z DOCS= 2024-08-06T20:24:40.5852413Z INSTALLED_ANDROID= 2024-08-06T20:24:40.5852651Z SHLVL=1 2024-08-06T20:24:40.5852862Z MAX_JOBS=6 2024-08-06T20:24:40.5853079Z GITHUB_ACTOR_ID=32754868 2024-08-06T20:24:40.5853438Z GITHUB_WORKFLOW_SHA=bf5bb5a1585a03379137fab341e87c02c77e76cd 2024-08-06T20:24:40.5853845Z GITHUB_REF_NAME=132710/merge 2024-08-06T20:24:40.5854255Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-08-06T20:24:40.5854705Z GITHUB_JOB=test 2024-08-06T20:24:40.5854957Z NO_TEST_TIMEOUT=False 2024-08-06T20:24:40.5855209Z TD_DISTRIBUTED=False 2024-08-06T20:24:40.5855489Z GITHUB_REPOSITORY=pytorch/pytorch 2024-08-06T20:24:40.5855806Z GITHUB_RETENTION_DAYS=90 2024-08-06T20:24:40.5856073Z OPENSSL_DIR=/opt/openssl 2024-08-06T20:24:40.5856351Z GITHUB_ACTION_REPOSITORY= 2024-08-06T20:24:40.5857174Z 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 2024-08-06T20:24:40.5858020Z GITHUB_BASE_REF=gh/drisspg/23/base 2024-08-06T20:24:40.5858332Z INSTALLED_ACL= 2024-08-06T20:24:40.5858563Z CI=true 2024-08-06T20:24:40.5858782Z GITHUB_REPOSITORY_OWNER=pytorch 2024-08-06T20:24:40.5859077Z JOB_ID=28427567353 2024-08-06T20:24:40.5859326Z INSTALLED_PROTOBUF=yes 2024-08-06T20:24:40.5859587Z GITHUB_HEAD_REF=gh/drisspg/23/head 2024-08-06T20:24:40.5859887Z GITHUB_ACTION_REF= 2024-08-06T20:24:40.5860173Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-08-06T20:24:40.5860661Z TEST_SHOWLOCALS=False 2024-08-06T20:24:40.5860924Z GITHUB_WORKFLOW=pull 2024-08-06T20:24:40.5861177Z DEBIAN_FRONTEND=noninteractive 2024-08-06T20:24:40.5861823Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.5862491Z NO_TD=False 2024-08-06T20:24:40.5862716Z SKIP_SCCACHE_INITIALIZATION=1 2024-08-06T20:24:40.5863006Z _=/usr/bin/env 2024-08-06T20:24:40.5863328Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-08-06T20:24:40.6002145Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2024-08-06T20:24:40.6003006Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-06T20:24:40.6003685Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2024-08-06T20:24:40.6004444Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2024-08-06T20:24:40.6004899Z + BUILD_DIR=build 2024-08-06T20:24:40.6005169Z + BUILD_RENAMED_DIR=build_renamed 2024-08-06T20:24:40.6005474Z + BUILD_BIN_DIR=build/bin 2024-08-06T20:24:40.6005737Z + SHARD_NUMBER=1 2024-08-06T20:24:40.6005973Z + NUM_TEST_SHARDS=3 2024-08-06T20:24:40.6006222Z + export VALGRIND=ON 2024-08-06T20:24:40.6006459Z + VALGRIND=ON 2024-08-06T20:24:40.6007041Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *clang9* ]] 2024-08-06T20:24:40.6007521Z + [[ 0 == \1 ]] 2024-08-06T20:24:40.6007761Z + [[ False == \1 ]] 2024-08-06T20:24:40.6008187Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *bazel* ]] 2024-08-06T20:24:40.6008650Z ++ realpath build/custom_test_artifacts 2024-08-06T20:24:40.6033854Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-08-06T20:24:40.6034373Z + [[ -n '' ]] 2024-08-06T20:24:40.6034619Z + echo 'Environment variables' 2024-08-06T20:24:40.6034905Z Environment variables 2024-08-06T20:24:40.6035156Z + env 2024-08-06T20:24:40.6040957Z INSTALLED_DB=yes 2024-08-06T20:24:40.6041702Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-06T20:24:40.6042334Z CONTINUE_THROUGH_ERROR=False 2024-08-06T20:24:40.6043015Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10-experimental-split-build 2024-08-06T20:24:40.6043623Z HOSTNAME=b394f538c1f1 2024-08-06T20:24:40.6044273Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.6044940Z GITHUB_ACTION=__self 2024-08-06T20:24:40.6045202Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-08-06T20:24:40.6045511Z GITHUB_RUN_NUMBER=233985 2024-08-06T20:24:40.6045774Z TEST_CONFIG=dynamo 2024-08-06T20:24:40.6046021Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-08-06T20:24:40.6046361Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-08-06T20:24:40.6046688Z GITHUB_TRIGGERING_ACTOR=drisspg 2024-08-06T20:24:40.6046985Z GITHUB_REF_TYPE=branch 2024-08-06T20:24:40.6047252Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-08-06T20:24:40.6047618Z BASE_SHA=1736af7cf736184c356be1bb00f59fb2feea6d7d 2024-08-06T20:24:40.6047954Z XLA_CUDA= 2024-08-06T20:24:40.6048182Z HUGGING_FACE_HUB_TOKEN= 2024-08-06T20:24:40.6048605Z *** 2024-08-06T20:24:40.6048815Z GITHUB_REPOSITORY_ID=65600975 2024-08-06T20:24:40.6049110Z GITHUB_ACTIONS=true 2024-08-06T20:24:40.6049389Z SHA1=b9d86fa89636e301796d4201f36d86c73f6e49bc 2024-08-06T20:24:40.6049765Z GITHUB_SHA=bf5bb5a1585a03379137fab341e87c02c77e76cd 2024-08-06T20:24:40.6050325Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/132710/merge 2024-08-06T20:24:40.6050840Z UCC_HOME=/usr 2024-08-06T20:24:40.6051065Z VERBOSE_TEST_LOGS=False 2024-08-06T20:24:40.6051345Z GITHUB_REF=refs/pull/132710/merge 2024-08-06T20:24:40.6051645Z SHARD_NUMBER=1 2024-08-06T20:24:40.6051878Z GITHUB_REF_PROTECTED=false 2024-08-06T20:24:40.6052157Z HOME=/var/lib/jenkins 2024-08-06T20:24:40.6052446Z GITHUB_API_URL=https://api.github.com 2024-08-06T20:24:40.6052784Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-08-06T20:24:40.6053094Z UCX_COMMIT= 2024-08-06T20:24:40.6053540Z SCCACHE_S3_KEY_PREFIX=pull 2024-08-06T20:24:40.6053800Z NUM_TEST_SHARDS=3 2024-08-06T20:24:40.6054037Z UCX_HOME=/usr 2024-08-06T20:24:40.6054630Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.6055596Z JOB_NAME=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, amz2023.linux.2xlarge) 2024-08-06T20:24:40.6056533Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.6057381Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-08-06T20:24:40.6057898Z GITHUB_EVENT_NAME=pull_request 2024-08-06T20:24:40.6058199Z DASHBOARD_TAG= 2024-08-06T20:24:40.6058439Z GITHUB_RUN_ID=10273124344 2024-08-06T20:24:40.6059089Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.6059810Z GITHUB_ACTOR=drisspg 2024-08-06T20:24:40.6060078Z PR_NUMBER=132710 2024-08-06T20:24:40.6060304Z DESIRED_CUDA= 2024-08-06T20:24:40.6060545Z GITHUB_RUN_ATTEMPT=1 2024-08-06T20:24:40.6060802Z VALGRIND=ON 2024-08-06T20:24:40.6061034Z ANACONDA_PYTHON_VERSION=3.12 2024-08-06T20:24:40.6061492Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-08-06T20:24:40.6061865Z TERM=xterm 2024-08-06T20:24:40.6062085Z INSTALLED_VISION=yes 2024-08-06T20:24:40.6062354Z BRANCH=pull/132710 2024-08-06T20:24:40.6062618Z OPENSSL_ROOT_DIR=/opt/openssl 2024-08-06T20:24:40.6062906Z CUDA_PATH=/usr/local/cuda 2024-08-06T20:24:40.6063463Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-08-06T20:24:40.6064071Z GITHUB_SERVER_URL=https://github.com 2024-08-06T20:24:40.6064375Z UCC_COMMIT= 2024-08-06T20:24:40.6064606Z REENABLED_ISSUES= 2024-08-06T20:24:40.6064832Z DOCS= 2024-08-06T20:24:40.6065052Z INSTALLED_ANDROID= 2024-08-06T20:24:40.6065290Z SHLVL=1 2024-08-06T20:24:40.6065492Z MAX_JOBS=6 2024-08-06T20:24:40.6065715Z GITHUB_ACTOR_ID=32754868 2024-08-06T20:24:40.6066076Z GITHUB_WORKFLOW_SHA=bf5bb5a1585a03379137fab341e87c02c77e76cd 2024-08-06T20:24:40.6066469Z GITHUB_REF_NAME=132710/merge 2024-08-06T20:24:40.6066978Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-08-06T20:24:40.6067433Z GITHUB_JOB=test 2024-08-06T20:24:40.6067658Z NO_TEST_TIMEOUT=False 2024-08-06T20:24:40.6067915Z TD_DISTRIBUTED=False 2024-08-06T20:24:40.6068186Z GITHUB_REPOSITORY=pytorch/pytorch 2024-08-06T20:24:40.6068483Z GITHUB_RETENTION_DAYS=90 2024-08-06T20:24:40.6068756Z OPENSSL_DIR=/opt/openssl 2024-08-06T20:24:40.6069035Z GITHUB_ACTION_REPOSITORY= 2024-08-06T20:24:40.6069836Z 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 2024-08-06T20:24:40.6070690Z GITHUB_BASE_REF=gh/drisspg/23/base 2024-08-06T20:24:40.6071003Z INSTALLED_ACL= 2024-08-06T20:24:40.6071224Z CI=true 2024-08-06T20:24:40.6071454Z GITHUB_REPOSITORY_OWNER=pytorch 2024-08-06T20:24:40.6071742Z JOB_ID=28427567353 2024-08-06T20:24:40.6071973Z INSTALLED_PROTOBUF=yes 2024-08-06T20:24:40.6072253Z GITHUB_HEAD_REF=gh/drisspg/23/head 2024-08-06T20:24:40.6072562Z GITHUB_ACTION_REF= 2024-08-06T20:24:40.6072855Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-08-06T20:24:40.6073220Z TEST_SHOWLOCALS=False 2024-08-06T20:24:40.6073483Z GITHUB_WORKFLOW=pull 2024-08-06T20:24:40.6073737Z DEBIAN_FRONTEND=noninteractive 2024-08-06T20:24:40.6074382Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_f9ee520e-ee61-4546-93d9-ada22f3efab6 2024-08-06T20:24:40.6075049Z NO_TD=False 2024-08-06T20:24:40.6075277Z SKIP_SCCACHE_INITIALIZATION=1 2024-08-06T20:24:40.6075565Z _=/usr/bin/env 2024-08-06T20:24:40.6075789Z + echo 'Testing pytorch' 2024-08-06T20:24:40.6076055Z Testing pytorch 2024-08-06T20:24:40.6076323Z + export LANG=C.UTF-8 2024-08-06T20:24:40.6076559Z + LANG=C.UTF-8 2024-08-06T20:24:40.6127988Z + PR_NUMBER=132710 2024-08-06T20:24:40.6128471Z + [[ dynamo == \d\e\f\a\u\l\t ]] 2024-08-06T20:24:40.6128838Z + [[ dynamo == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-08-06T20:24:40.6129312Z + [[ dynamo == \s\l\o\w ]] 2024-08-06T20:24:40.6129957Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *slow-gradcheck* ]] 2024-08-06T20:24:40.6130882Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *cuda* ]] 2024-08-06T20:24:40.6131483Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-08-06T20:24:40.6132135Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *xpu* ]] 2024-08-06T20:24:40.6132566Z + [[ dynamo == *crossref* ]] 2024-08-06T20:24:40.6132990Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-08-06T20:24:40.6133557Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *xpu* ]] 2024-08-06T20:24:40.6136758Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *-bazel-* ]] 2024-08-06T20:24:40.6137256Z + pip_install --user ninja==1.10.2 2024-08-06T20:24:40.6137650Z + pip install --progress-bar off --user ninja==1.10.2 2024-08-06T20:24:41.1680195Z Collecting ninja==1.10.2 2024-08-06T20:24:41.1853955Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-08-06T20:24:41.2001859Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-08-06T20:24:41.8233148Z Installing collected packages: ninja 2024-08-06T20:24:41.8314055Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-08-06T20:24:41.8315073Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-08-06T20:24:41.8372227Z Successfully installed ninja-1.10.2 2024-08-06T20:24:41.9613176Z + 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 2024-08-06T20:24:41.9614863Z + 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 2024-08-06T20:24:41.9615981Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *aarch64* ]] 2024-08-06T20:24:41.9616442Z + install_tlparse 2024-08-06T20:24:41.9616699Z + pip_install --user tlparse==0.3.7 2024-08-06T20:24:41.9617083Z + pip install --progress-bar off --user tlparse==0.3.7 2024-08-06T20:24:42.4189799Z Collecting tlparse==0.3.7 2024-08-06T20:24:42.4354795Z Downloading tlparse-0.3.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (346 bytes) 2024-08-06T20:24:42.4473130Z Downloading tlparse-0.3.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-08-06T20:24:43.1039296Z Installing collected packages: tlparse 2024-08-06T20:24:43.1450620Z Successfully installed tlparse-0.3.7 2024-08-06T20:24:43.2710986Z ++ python -m site --user-base 2024-08-06T20:24:43.2888889Z + 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 2024-08-06T20:24:43.2890581Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *asan* ]] 2024-08-06T20:24:43.2891391Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *-debug* ]] 2024-08-06T20:24:43.2891979Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *-bazel-* ]] 2024-08-06T20:24:43.2892754Z + echo 'We are not in debug mode: linux-focal-py3.12-clang10-experimental-split-build. Expect the assertion to pass' 2024-08-06T20:24:43.2893747Z We are not in debug mode: linux-focal-py3.12-clang10-experimental-split-build. Expect the assertion to pass 2024-08-06T20:24:43.2894786Z + cd test 2024-08-06T20:24:43.2895575Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-08-06T20:24:45.1233078Z + [[ dynamo == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-08-06T20:24:45.1233483Z + [[ dynamo == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-08-06T20:24:45.1236964Z + DYNAMO_BENCHMARK_FLAGS=() 2024-08-06T20:24:45.1237378Z + [[ dynamo == *dynamo_eager* ]] 2024-08-06T20:24:45.1237727Z + [[ dynamo == *aot_eager* ]] 2024-08-06T20:24:45.1238051Z + [[ dynamo == *aot_inductor* ]] 2024-08-06T20:24:45.1238337Z + [[ dynamo == *inductor* ]] 2024-08-06T20:24:45.1238626Z + [[ dynamo == *dynamic* ]] 2024-08-06T20:24:45.1238901Z + [[ dynamo == *cpu* ]] 2024-08-06T20:24:45.1239187Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-08-06T20:24:45.1270130Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *libtorch* ]] 2024-08-06T20:24:45.1270768Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *-bazel-* ]] 2024-08-06T20:24:45.1273531Z + cd test 2024-08-06T20:24:45.1273972Z + python -c 'import torch; print(torch.__config__.show())' 2024-08-06T20:24:46.4512335Z PyTorch built with: 2024-08-06T20:24:46.4512795Z - GCC 4.2 2024-08-06T20:24:46.4513029Z - C++ Version: 201703 2024-08-06T20:24:46.4513278Z - clang 10.0.0 2024-08-06T20:24:46.4513847Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-08-06T20:24:46.4514908Z - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-08-06T20:24:46.4515374Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-08-06T20:24:46.4515717Z - LAPACK is enabled (usually provided by MKL) 2024-08-06T20:24:46.4516069Z - NNPACK is enabled 2024-08-06T20:24:46.4516345Z - CPU capability usage: AVX512 2024-08-06T20:24:46.4522004Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, 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 -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=pedantic -Wno-error=old-style-cast -Wconstant-conversion -Wno-invalid-partial-specialization -Wno-missing-braces -Qunused-arguments -fcolor-diagnostics -faligned-new -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.5.0, 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, 2024-08-06T20:24:46.4527797Z 2024-08-06T20:24:46.7821381Z + cd test 2024-08-06T20:24:46.7821888Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-08-06T20:24:48.0694808Z ATen/Parallel: 2024-08-06T20:24:48.0695167Z at::get_num_threads() : 4 2024-08-06T20:24:48.0695466Z at::get_num_interop_threads() : 4 2024-08-06T20:24:48.0695787Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-08-06T20:24:48.0696121Z omp_get_max_threads() : 4 2024-08-06T20:24:48.0696692Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-08-06T20:24:48.0697319Z mkl_get_max_threads() : 4 2024-08-06T20:24:48.0697710Z Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-08-06T20:24:48.0698154Z std::thread::hardware_concurrency() : 8 2024-08-06T20:24:48.0698482Z Environment variables: 2024-08-06T20:24:48.0698749Z OMP_NUM_THREADS : [not set] 2024-08-06T20:24:48.0699022Z MKL_NUM_THREADS : [not set] 2024-08-06T20:24:48.0699312Z ATen parallel backend: OpenMP 2024-08-06T20:24:48.0699500Z 2024-08-06T20:24:48.4070543Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *aarch64* ]] 2024-08-06T20:24:48.4071212Z + [[ dynamo == *backward* ]] 2024-08-06T20:24:48.4071600Z + [[ dynamo == *xla* ]] 2024-08-06T20:24:48.4071918Z + [[ dynamo == *executorch* ]] 2024-08-06T20:24:48.4072258Z + [[ dynamo == \j\i\t\_\l\e\g\a\c\y ]] 2024-08-06T20:24:48.4072787Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *libtorch* ]] 2024-08-06T20:24:48.4073261Z + [[ dynamo == distributed ]] 2024-08-06T20:24:48.4073610Z + [[ dynamo == *inductor_distributed* ]] 2024-08-06T20:24:48.4074036Z + [[ dynamo == *inductor-halide* ]] 2024-08-06T20:24:48.4074427Z + [[ dynamo == *inductor-micro-benchmark* ]] 2024-08-06T20:24:48.4074833Z + [[ dynamo == *huggingface* ]] 2024-08-06T20:24:48.4075132Z + [[ dynamo == *timm* ]] 2024-08-06T20:24:48.4075403Z + [[ dynamo == *torchbench* ]] 2024-08-06T20:24:48.4075742Z + [[ dynamo == *inductor_cpp_wrapper_abi_compatible* ]] 2024-08-06T20:24:48.4076096Z + [[ dynamo == *inductor* ]] 2024-08-06T20:24:48.4076392Z + [[ dynamo == *dynamo* ]] 2024-08-06T20:24:48.4076650Z + install_torchvision 2024-08-06T20:24:48.4076910Z + local orig_preload 2024-08-06T20:24:48.4077158Z + local commit 2024-08-06T20:24:48.4077389Z ++ get_pinned_commit vision 2024-08-06T20:24:48.4077691Z ++ cat .github/ci_commit_pins/vision.txt 2024-08-06T20:24:48.4094190Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-08-06T20:24:48.4094552Z + orig_preload= 2024-08-06T20:24:48.4094796Z + '[' -n '' ']' 2024-08-06T20:24:48.4095375Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-06T20:24:48.4096407Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-06T20:24:48.8480742Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-06T20:24:48.8486743Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-ljafny4t 2024-08-06T20:24:48.8507973Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-ljafny4t 2024-08-06T20:24:50.2404275Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-08-06T20:24:50.2424442Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-08-06T20:24:51.5704051Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-08-06T20:24:51.8549891Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-08-06T20:24:54.1319942Z Preparing metadata (setup.py) ... [?25l- \ done 2024-08-06T20:24:54.1376837Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.26.0) 2024-08-06T20:24:54.1391426Z Requirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.5.0a0+gitb9d86fa) 2024-08-06T20:24:54.1403798Z 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) (10.3.0) 2024-08-06T20:24:54.1794430Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.13.1) 2024-08-06T20:24:54.1805941Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2024-08-06T20:24:54.1816119Z 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) 2024-08-06T20:24:54.1823807Z 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) 2024-08-06T20:24:54.1839543Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.6.1) 2024-08-06T20:24:54.1850488Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (69.5.1) 2024-08-06T20:24:54.1867634Z Requirement already satisfied: torch-no-python==2.5.0a0+gitb9d86fa in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.5.0a0+gitb9d86fa) 2024-08-06T20:24:54.1886461Z Requirement already satisfied: sympy>=1.13.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.1) 2024-08-06T20:24:54.2003647Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy>=1.13.0->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2024-08-06T20:24:54.2612379Z 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) (2.1.5) 2024-08-06T20:24:54.3069027Z Building wheels for collected packages: torchvision 2024-08-06T20:26:01.4896913Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done 2024-08-06T20:26:01.4932636Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp312-cp312-linux_x86_64.whl size=1116550 sha256=d8305d60ef79b6380d01c15a17d8a766d109d0df3047f13ee396d852c1fbee80 2024-08-06T20:26:01.4935191Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/b9/aa/81/39d3509ec629531316195ffac7a7b05ff7603f393064d63ec9 2024-08-06T20:26:01.4971034Z Successfully built torchvision 2024-08-06T20:26:02.1085412Z Installing collected packages: torchvision 2024-08-06T20:26:02.5602737Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-08-06T20:26:02.7163800Z + '[' -n '' ']' 2024-08-06T20:26:02.7164093Z + test_dynamo_shard 1 2024-08-06T20:26:02.7164342Z + [[ -z 3 ]] 2024-08-06T20:26:02.7164604Z + python tools/dynamo/verify_dynamo.py 2024-08-06T20:26:03.9388027Z Python version: 3.12.4 2024-08-06T20:26:03.9388418Z `torch` version: 2.5.0a0+gitb9d86fa 2024-08-06T20:26:03.9388736Z CUDA version: None 2024-08-06T20:26:03.9388967Z ROCM version: None 2024-08-06T20:26:03.9389123Z 2024-08-06T20:26:04.8182571Z CUDA not available -- skipping CUDA check on eager backend 2024-08-06T20:26:04.8182905Z 2024-08-06T20:26:05.8834244Z CUDA not available -- skipping CUDA check on aot_eager backend 2024-08-06T20:26:05.8834595Z 2024-08-06T20:26:14.0205688Z CUDA not available -- skipping CUDA check on inductor backend 2024-08-06T20:26:14.0206054Z 2024-08-06T20:26:14.0206167Z All required checks passed 2024-08-06T20:26:14.7392997Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --shard 1 3 --verbose 2024-08-06T20:26:14.8453037Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-08-06T20:26:14.8454735Z import pkg_resources 2024-08-06T20:26:16.8999354Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T20:26:17.8953314Z Downloading https://ossci-metrics.s3.amazonaws.com/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json 2024-08-06T20:26:17.9487153Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-06T20:26:17.9953445Z Ignoring disabled issues: [''] 2024-08-06T20:26:18.0064495Z Found test times from artifacts 2024-08-06T20:26:18.0489171Z Found test times from artifacts 2024-08-06T20:26:18.0503646Z Running all tests 2024-08-06T20:26:18.0607681Z Running parallel tests on 3 processes 2024-08-06T20:26:18.0610369Z Name: tests to run (est. time: 55.48min) 2024-08-06T20:26:18.0610796Z Serial tests (26): 2024-08-06T20:26:18.0611058Z test_native_mha 1/1 2024-08-06T20:26:18.0611299Z test_nn 2/2 2024-08-06T20:26:18.0611546Z test_cpp_api_parity 1/1 2024-08-06T20:26:18.0611832Z test_torch 1/1 2024-08-06T20:26:18.0612084Z test_ci_sanity_check_fail 1/1 2024-08-06T20:26:18.0612394Z test_jit_disabled 1/1 2024-08-06T20:26:18.0612652Z test_tensorexpr 1/1 2024-08-06T20:26:18.0612911Z test_autocast 1/1 2024-08-06T20:26:18.0613177Z test_cpp_extensions_jit 1/1 2024-08-06T20:26:18.0613487Z test_sort_and_select 1/1 2024-08-06T20:26:18.0613759Z test_reductions 1/1 2024-08-06T20:26:18.0614025Z test_cuda_primary_ctx 1/1 2024-08-06T20:26:18.0614329Z test_cuda_nvml_based_avail 1/1 2024-08-06T20:26:18.0614626Z nn/test_convolution 1/1 2024-08-06T20:26:18.0614903Z nn/test_pooling 1/1 2024-08-06T20:26:18.0615160Z test_multiprocessing 1/1 2024-08-06T20:26:18.0615470Z test_mobile_optimizer 1/1 2024-08-06T20:26:18.0615774Z test_multiprocessing_spawn 1/1 2024-08-06T20:26:18.0616073Z test_spectral_ops 1/1 2024-08-06T20:26:18.0616370Z distributions/test_distributions 1/2 2024-08-06T20:26:18.0616962Z distributions/test_distributions 2/2 2024-08-06T20:26:18.0617283Z doctests 1/1 2024-08-06T20:26:18.0617548Z test_cpp_extensions_aot_no_ninja 1/1 2024-08-06T20:26:18.0617878Z test_autoload_enable 1/1 2024-08-06T20:26:18.0618157Z test_autoload_disable 1/1 2024-08-06T20:26:18.0618468Z test_cpp_extensions_aot_ninja 1/1 2024-08-06T20:26:18.0618786Z Parallel tests (27): 2024-08-06T20:26:18.0619049Z dynamo/test_dynamic_shapes 1/1 2024-08-06T20:26:18.0619360Z dynamo/test_frame_init 1/1 2024-08-06T20:26:18.0619652Z dynamo/test_interop 1/1 2024-08-06T20:26:18.0619919Z test_matmul_cuda 1/1 2024-08-06T20:26:18.0620192Z dynamo/test_global 1/1 2024-08-06T20:26:18.0620474Z dynamo/test_exceptions 1/1 2024-08-06T20:26:18.0620766Z dynamo/test_subgraphs 1/1 2024-08-06T20:26:18.0621051Z dynamo/test_modes 1/1 2024-08-06T20:26:18.0621333Z dynamo/test_higher_order_ops 1/1 2024-08-06T20:26:18.0621635Z dynamo/test_functions 1/1 2024-08-06T20:26:18.0621920Z dynamo/test_modules 1/1 2024-08-06T20:26:18.0622210Z dynamo/test_model_output 1/1 2024-08-06T20:26:18.0622494Z dynamo/test_export 1/1 2024-08-06T20:26:18.0622772Z dynamo/test_ctx_manager 1/1 2024-08-06T20:26:18.0623049Z functorch/test_ac 1/1 2024-08-06T20:26:18.0623328Z dynamo/test_profiler 1/1 2024-08-06T20:26:18.0623641Z dynamo/test_activation_checkpointing 1/1 2024-08-06T20:26:18.0623971Z dynamo/test_trace_rules 1/1 2024-08-06T20:26:18.0624264Z dynamo/test_unspec 1/1 2024-08-06T20:26:18.0624554Z dynamo/test_input_attr_tracking 1/1 2024-08-06T20:26:18.0624874Z dynamo/test_recompile_ux 1/1 2024-08-06T20:26:18.0625182Z dynamo/test_structured_trace 1/1 2024-08-06T20:26:18.0625501Z test_cuda_sanitizer 1/1 2024-08-06T20:26:18.0625759Z test_cuda 1/1 2024-08-06T20:26:18.0626004Z test_cuda_multigpu 1/1 2024-08-06T20:26:18.0626282Z test_quantization 3/6 2024-08-06T20:26:18.0626551Z optim/test_lrscheduler 1/1 2024-08-06T20:26:18.0626928Z Name: excluded (est. time: 0.0min) 2024-08-06T20:26:18.0627241Z Serial tests (0): 2024-08-06T20:26:18.0627476Z Parallel tests (0): 2024-08-06T20:26:18.0627862Z Starting test batch 'tests to run' 0.0 seconds after initiating testing 2024-08-06T20:26:18.0728189Z Running test_native_mha 1/1 ... [2024-08-06 20:26:18.072458] 2024-08-06T20:26:18.0733048Z 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'] ... [2024-08-06 20:26:18.072929] 2024-08-06T20:26:20.1876133Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T20:26:20.2161054Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T20:26:20.2162531Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T20:26:46.7808570Z 2024-08-06T20:26:46.7809920Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_6db589fc60910015_.log 2024-08-06T20:26:46.7837578Z 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 2024-08-06T20:26:46.7858766Z 2024-08-06T20:26:46.7858946Z Running test_nn 2/2 ... [2024-08-06 20:26:46.781218] 2024-08-06T20:26:46.7859999Z 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'] ... [2024-08-06 20:26:46.781573] 2024-08-06T20:32:29.1028600Z 2024-08-06T20:32:29.1045583Z test_nn 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_2.2_88d5c9391779ebb5_.log 2024-08-06T20:32:29.1667479Z Running 1127 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_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, 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, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_sum_cpu, 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 2024-08-06T20:32:29.2268884Z 2024-08-06T20:32:29.2269112Z Running test_cpp_api_parity 1/1 ... [2024-08-06 20:32:29.104962] 2024-08-06T20:32:29.2270272Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 20:32:29.105308] 2024-08-06T20:35:05.0766550Z 2024-08-06T20:35:05.0770121Z test_cpp_api_parity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_api_parity_1.1_12e804e602416c72_.log 2024-08-06T20:35:05.0962641Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda 2024-08-06T20:35:05.1152648Z 2024-08-06T20:35:05.1152831Z Running test_torch 1/1 ... [2024-08-06 20:35:05.077479] 2024-08-06T20:35:05.1154195Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 20:35:05.077802] 2024-08-06T20:40:53.3283652Z 2024-08-06T20:40:53.3284705Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_7c04fa92e64f1aa5_.log 2024-08-06T20:40:53.3606553Z Running 1021 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensoriterator_output_setup, test/test_torch.py::TestTorch::test_terminate_handler_on_crash, test/test_torch.py::TestTorch::test_to, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, test/test_torch.py::TestTorch::test_torch_from_file, test/test_torch.py::TestTorch::test_transpose_neg_dim, test/test_torch.py::TestTorch::test_type, test/test_torch.py::TestTorch::test_type_alias, test/test_torch.py::TestTorch::test_type_conversion_via_dtype_name, test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unbind_neg_dim, test/test_torch.py::TestTorch::test_unflatten, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_unsqueeze_neg_dim, test/test_torch.py::TestTorch::test_upsample_nearest1d_meta, test/test_torch.py::TestTorch::test_upsample_nearest2d_meta, test/test_torch.py::TestTorch::test_var_neg_dim, test/test_torch.py::TestTorch::test_warn_types, test/test_torch.py::TestTorch::test_wildcard_import, test/test_torch.py::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_assertRaisesRegex_ignore_msg_non_native_device_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_fmod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ge_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lerp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_fill_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_select_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_min_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_mul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ne_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_pow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_sub_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_cuda_backward_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_euclidean_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_replication_pad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_device_guard_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_dim_function_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_discontiguous_out_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dtypetensor_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_errors_index_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_deterministic_path_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_one_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scale_will_not_overflow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_deprecated_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_pass_itself_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_accumulation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_separate_unscale_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_multiple_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_penalty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_state_dict_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_sparse_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_update_scale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_hook_remove_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_put_non_accumulate_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_uint8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_clone_cpu, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_FractionalMaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_NLLLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad1d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad1d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_bincount_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_histc_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_bicubic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_linear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_trilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_kthvalue_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_median_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_put_accumulate_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_put_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint2x4, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nullary_op_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pairwise_distance_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_pdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pdist_norm_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pickle_gradscaler_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pin_memory_from_constructor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_repeat_interleave_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scalar_check_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_bool_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_non_unique_index_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_one_dim_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_to_large_input_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_bool_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_to_large_input_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_zero_size_index_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_serialization_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_default_tensor_type_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_shift_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_skip_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_unfold_all_devices_and_dtypes_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_unfold_scalars_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu 2024-08-06T20:40:53.3919219Z 2024-08-06T20:40:53.3919464Z Running test_ci_sanity_check_fail 1/1 ... [2024-08-06 20:40:53.330328] 2024-08-06T20:40:53.3920667Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 20:40:53.330631] 2024-08-06T20:41:01.7310872Z Running test_jit_disabled 1/1 ... [2024-08-06 20:41:01.730723] 2024-08-06T20:41:01.7312569Z 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'] ... [2024-08-06 20:41:01.731032] 2024-08-06T20:41:04.6995308Z 2024-08-06T20:41:04.6996177Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_a5038321842e9a4f_.log 2024-08-06T20:41:04.6997935Z 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 2024-08-06T20:41:04.6999057Z 2024-08-06T20:41:04.6999583Z Running test_tensorexpr 1/1 ... [2024-08-06 20:41:04.699668] 2024-08-06T20:41:04.7001692Z 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'] ... [2024-08-06 20:41:04.699952] 2024-08-06T20:41:07.4179665Z 2024-08-06T20:41:07.4180555Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_83e9bb815d747091_.log 2024-08-06T20:41:07.4205967Z 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 2024-08-06T20:41:07.4226146Z 2024-08-06T20:41:07.4226334Z Running test_autocast 1/1 ... [2024-08-06 20:41:07.418169] 2024-08-06T20:41:07.4227589Z 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'] ... [2024-08-06 20:41:07.418545] 2024-08-06T20:41:23.7049287Z 2024-08-06T20:41:23.7050166Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_a837ce26ca83adea_.log 2024-08-06T20:41:23.7055457Z Running 16 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_cache_disabled, test/test_autocast.py::TestAutocastGPU::test_cast_cache_is_global, 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 2024-08-06T20:41:23.7060377Z 2024-08-06T20:41:23.7060591Z Running test_cpp_extensions_jit 1/1 ... [2024-08-06 20:41:23.705100] 2024-08-06T20:41:23.7061782Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_jit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 20:41:23.705419] 2024-08-06T20:41:58.4191582Z 2024-08-06T20:41:58.4192724Z test_cpp_extensions_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_jit_1.1_b1456a04f369c744_.log 2024-08-06T20:41:58.4205743Z Running 26 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning 2024-08-06T20:41:58.4216190Z 2024-08-06T20:41:58.4216398Z Running test_sort_and_select 1/1 ... [2024-08-06 20:41:58.419322] 2024-08-06T20:41:58.4217544Z 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'] ... [2024-08-06 20:41:58.419684] 2024-08-06T20:42:48.4055044Z 2024-08-06T20:42:48.4058035Z 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_609e5da0417ae5f0_.log 2024-08-06T20:42:48.4099046Z Running 110 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_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_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 2024-08-06T20:42:48.4135987Z 2024-08-06T20:42:48.4136195Z Running test_reductions 1/1 ... [2024-08-06 20:42:48.406097] 2024-08-06T20:42:48.4137419Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 20:42:48.406439] 2024-08-06T20:59:41.6406922Z 2024-08-06T20:59:41.6408722Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_56806ce5cac37920_.log 2024-08-06T20:59:41.8166582Z Running 4585 items in this shard: test/test_reductions.py::TestReductionsCPU::test_accreal_type_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float64, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_uint8, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float32, 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_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex128, 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_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int64, 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_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float16, 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_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bool, 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_float16, 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_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_cpu_device_mismatch_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_reduction_uint8_overflow_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 2024-08-06T20:59:41.9864230Z 2024-08-06T20:59:41.9864485Z Running test_cuda_primary_ctx 1/1 ... [2024-08-06 20:59:41.648676] 2024-08-06T20:59:41.9865755Z 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'] ... [2024-08-06 20:59:41.649094] 2024-08-06T20:59:44.1268243Z 2024-08-06T20:59:44.1269494Z 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_fafd7dda94f022bb_.log 2024-08-06T20:59:44.1270355Z Running 0 items in this shard: 2024-08-06T20:59:44.1270640Z 2024-08-06T20:59:44.1271426Z Running test_cuda_nvml_based_avail 1/1 ... [2024-08-06 20:59:44.126965] 2024-08-06T20:59:44.1275270Z 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'] ... [2024-08-06 20:59:44.127284] 2024-08-06T20:59:46.5888345Z 2024-08-06T20:59:46.5889375Z 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_8312b71bc207f43c_.log 2024-08-06T20:59:46.5890200Z Running 0 items in this shard: 2024-08-06T20:59:46.5890408Z 2024-08-06T20:59:46.5891654Z Running nn/test_convolution 1/1 ... [2024-08-06 20:59:46.588999] 2024-08-06T20:59:46.5895333Z 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'] ... [2024-08-06 20:59:46.589315] 2024-08-06T21:03:59.6855751Z 2024-08-06T21:03:59.6859074Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_9939d28985a36ef3_.log 2024-08-06T21:03:59.7202480Z Running 588 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv1d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_1x1, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_OneDNN, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_backward_twice, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias_v2, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_with_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_without_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_missing_argument, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_wbias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_half_cublas_gemm, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size_downsample_upsample, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose3d_correct_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv1d_issue_120547, test/nn/test_convolution.py::TestConvolutionNN::test_conv2d_discontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_issue_120406, test/nn/test_convolution.py::TestConvolutionNN::test_conv_backcompat, test/nn/test_convolution.py::TestConvolutionNN::test_conv_cudnn_memory_layout_dominance, test/nn/test_convolution.py::TestConvolutionNN::test_conv_invalid_groups, test/nn/test_convolution.py::TestConvolutionNN::test_conv_modules_raise_error_on_incorrect_input_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv_padding_mode, test/nn/test_convolution.py::TestConvolutionNN::test_conv_shapecheck, test/nn/test_convolution.py::TestConvolutionNN::test_conv_tbc, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_non_contiguous, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_noncontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_not_mutate_stride, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grouped_conv_cudnn_nhwc_support, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv1d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv3d, test/nn/test_convolution.py::TestConvolutionNN::test_mismatch_shape_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_nnpack_conv, test/nn/test_convolution.py::TestConvolutionNN::test_permute_conv2d_issue_120211, test/nn/test_convolution.py::TestConvolutionNN::test_thnn_conv_strided_padded_dilated, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose3d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_contig_wrong_stride_cudnn_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_no_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_complex64, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_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_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 2024-08-06T21:03:59.7537570Z 2024-08-06T21:03:59.7537772Z Running nn/test_pooling 1/1 ... [2024-08-06 21:03:59.686710] 2024-08-06T21:03:59.7538910Z 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'] ... [2024-08-06 21:03:59.687003] 2024-08-06T21:05:08.3572613Z 2024-08-06T21:05:08.3573705Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_84afd7a5eb718186_.log 2024-08-06T21:05:08.3615722Z Running 100 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::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 2024-08-06T21:05:08.3653743Z 2024-08-06T21:05:08.3653963Z Running test_multiprocessing 1/1 ... [2024-08-06 21:05:08.357591] 2024-08-06T21:05:08.3655161Z 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'] ... [2024-08-06 21:05:08.357905] 2024-08-06T21:05:43.1227755Z 2024-08-06T21:05:43.1229196Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_3beffe099fce4971_.log 2024-08-06T21:05:43.1246652Z 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 2024-08-06T21:05:43.1260584Z 2024-08-06T21:05:43.1260834Z Running test_mobile_optimizer 1/1 ... [2024-08-06 21:05:43.122758] 2024-08-06T21:05:43.1261993Z 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'] ... [2024-08-06 21:05:43.123152] 2024-08-06T21:05:50.7489050Z 2024-08-06T21:05:50.7490414Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_82d26e45d77aa22d_.log 2024-08-06T21:05:50.7496018Z 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 2024-08-06T21:05:50.7499617Z 2024-08-06T21:05:50.7499854Z Running test_multiprocessing_spawn 1/1 ... [2024-08-06 21:05:50.749032] 2024-08-06T21:05:50.7501101Z 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'] ... [2024-08-06 21:05:50.749359] 2024-08-06T21:09:43.7033158Z 2024-08-06T21:09:43.7034602Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_9ef1316b250b1fdb_.log 2024-08-06T21:09:43.7042882Z Running 19 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, 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, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable 2024-08-06T21:09:43.7048813Z 2024-08-06T21:09:43.7049024Z Running test_spectral_ops 1/1 ... [2024-08-06 21:09:43.703071] 2024-08-06T21:09:43.7050160Z 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'] ... [2024-08-06 21:09:43.703431] 2024-08-06T21:10:18.0737462Z 2024-08-06T21:10:18.0738917Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_6d811bdd69943e2e_.log 2024-08-06T21:10:18.0837427Z 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, 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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 2024-08-06T21:10:18.0928554Z 2024-08-06T21:10:18.0928838Z Running distributions/test_distributions 1/2 ... [2024-08-06 21:10:18.074501] 2024-08-06T21:10:18.0930149Z 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'] ... [2024-08-06 21:10:18.074904] 2024-08-06T21:16:37.2093079Z 2024-08-06T21:16:37.2094319Z distributions/test_distributions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.2_26ec67a9f4153123_.log 2024-08-06T21:16:37.2148607Z 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 2024-08-06T21:16:37.2200202Z 2024-08-06T21:16:37.2200547Z Running distributions/test_distributions 2/2 ... [2024-08-06 21:16:37.209597] 2024-08-06T21:16:37.2201864Z 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'] ... [2024-08-06 21:16:37.209916] 2024-08-06T21:24:00.9596984Z 2024-08-06T21:24:00.9598137Z distributions/test_distributions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.2_d3e6570de8053c03_.log 2024-08-06T21:24:00.9640462Z Running 96 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_multinomial_sequential_draw, 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 2024-08-06T21:24:00.9678943Z 2024-08-06T21:24:00.9679114Z Running doctests 1/1 ... [2024-08-06 21:24:00.959966] 2024-08-06T21:24:00.9694665Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2024-08-06T21:24:00.9695158Z Listing tests 2024-08-06T21:24:01.2205164Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-08-06T21:24:01.2206436Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.2207076Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-08-06T21:24:01.2207435Z 2024-08-06T21:24:01.2207615Z This is helpful when you want to visualize data over some 2024-08-06T21:24:01.2208083Z range of inputs. See below for a plotting example. 2024-08-06T21:24:01.2208346Z 2024-08-06T21:24:01.2208526Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-08-06T21:24:01.2209172Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-08-06T21:24:01.2209689Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-08-06T21:24:01.2210158Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-08-06T21:24:01.2210609Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-08-06T21:24:01.2211018Z to the result shape. 2024-08-06T21:24:01.2211208Z 2024-08-06T21:24:01.2211327Z .. note:: 2024-08-06T21:24:01.2211631Z 0D inputs are treated equivalently to 1D inputs of a 2024-08-06T21:24:01.2211995Z single element. 2024-08-06T21:24:01.2212176Z 2024-08-06T21:24:01.2212269Z .. warning:: 2024-08-06T21:24:01.2212607Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-08-06T21:24:01.2213123Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-08-06T21:24:01.2213410Z 2024-08-06T21:24:01.2213564Z In the future `torch.meshgrid` will transition to 2024-08-06T21:24:01.2213949Z `indexing='xy'` as the default. 2024-08-06T21:24:01.2214172Z 2024-08-06T21:24:01.2214364Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-08-06T21:24:01.2214840Z this issue with the goal of migrating to NumPy's behavior. 2024-08-06T21:24:01.2215131Z 2024-08-06T21:24:01.2215236Z .. seealso:: 2024-08-06T21:24:01.2215381Z 2024-08-06T21:24:01.2215590Z :func:`torch.cartesian_prod` has the same effect but it 2024-08-06T21:24:01.2215998Z collects the data in a tensor of vectors. 2024-08-06T21:24:01.2216250Z 2024-08-06T21:24:01.2216336Z Args: 2024-08-06T21:24:01.2216731Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-08-06T21:24:01.2217271Z treated as tensors of size :math:`(1,)` automatically 2024-08-06T21:24:01.2217558Z 2024-08-06T21:24:01.2217736Z indexing: (str, optional): the indexing mode, either "xy" 2024-08-06T21:24:01.2218305Z or "ij", defaults to "ij". See warning for future changes. 2024-08-06T21:24:01.2218592Z 2024-08-06T21:24:01.2218759Z If "xy" is selected, the first dimension corresponds 2024-08-06T21:24:01.2219184Z to the cardinality of the second input and the second 2024-08-06T21:24:01.2219641Z dimension corresponds to the cardinality of the first 2024-08-06T21:24:01.2220023Z input. 2024-08-06T21:24:01.2220172Z 2024-08-06T21:24:01.2220320Z If "ij" is selected, the dimensions are in the same 2024-08-06T21:24:01.2220719Z order as the cardinality of the inputs. 2024-08-06T21:24:01.2220960Z 2024-08-06T21:24:01.2221063Z Returns: 2024-08-06T21:24:01.2221359Z seq (sequence of Tensors): If the input has :math:`N` 2024-08-06T21:24:01.2221794Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-08-06T21:24:01.2222275Z output will also have :math:`N` tensors, where each tensor 2024-08-06T21:24:01.2222961Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-08-06T21:24:01.2223299Z 2024-08-06T21:24:01.2223445Z Example:: 2024-08-06T21:24:01.2223641Z 2024-08-06T21:24:01.2223851Z >>> x = torch.tensor([1, 2, 3]) 2024-08-06T21:24:01.2224338Z >>> y = torch.tensor([4, 5, 6]) 2024-08-06T21:24:01.2224565Z 2024-08-06T21:24:01.2224758Z Observe the element-wise pairings across the grid, (1, 4), 2024-08-06T21:24:01.2225200Z (1, 5), ..., (3, 6). This is the same thing as the 2024-08-06T21:24:01.2225577Z cartesian product. 2024-08-06T21:24:01.2226090Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-08-06T21:24:01.2226667Z >>> grid_x 2024-08-06T21:24:01.2227261Z tensor([[1, 1, 1], 2024-08-06T21:24:01.2227559Z [2, 2, 2], 2024-08-06T21:24:01.2227922Z [3, 3, 3]]) 2024-08-06T21:24:01.2228180Z >>> grid_y 2024-08-06T21:24:01.2228431Z tensor([[4, 5, 6], 2024-08-06T21:24:01.2228715Z [4, 5, 6], 2024-08-06T21:24:01.2229059Z [4, 5, 6]]) 2024-08-06T21:24:01.2229245Z 2024-08-06T21:24:01.2229412Z This correspondence can be seen when these grids are 2024-08-06T21:24:01.2229801Z stacked properly. 2024-08-06T21:24:01.2230183Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-08-06T21:24:01.2230637Z ... torch.cartesian_prod(x, y)) 2024-08-06T21:24:01.2230972Z True 2024-08-06T21:24:01.2231109Z 2024-08-06T21:24:01.2231304Z `torch.meshgrid` is commonly used to produce a grid for 2024-08-06T21:24:01.2231679Z plotting. 2024-08-06T21:24:01.2232021Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-08-06T21:24:01.2232397Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-08-06T21:24:01.2232761Z >>> import matplotlib.pyplot as plt 2024-08-06T21:24:01.2233134Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-08-06T21:24:01.2233500Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-08-06T21:24:01.2233862Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-08-06T21:24:01.2234241Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-08-06T21:24:01.2234603Z >>> ax = plt.axes(projection='3d') 2024-08-06T21:24:01.2234975Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-08-06T21:24:01.2235338Z >>> plt.show() 2024-08-06T21:24:01.2235503Z 2024-08-06T21:24:01.2235649Z .. image:: ../_static/img/meshgrid.png 2024-08-06T21:24:01.2235960Z :width: 512 2024-08-06T21:24:01.2236124Z 2024-08-06T21:24:01.2236210Z 2024-08-06T21:24:01.2236588Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.2236956Z 2024-08-06T21:24:01.2237516Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-08-06T21:24:01.2238461Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.2239227Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-08-06T21:24:01.2239738Z 2024-08-06T21:24:01.2239889Z Returns the unique elements of the input tensor. 2024-08-06T21:24:01.2240145Z 2024-08-06T21:24:01.2240456Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-08-06T21:24:01.2241064Z this function also eliminates non-consecutive duplicate values. 2024-08-06T21:24:01.2241401Z 2024-08-06T21:24:01.2241631Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-08-06T21:24:01.2242280Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-08-06T21:24:01.2243208Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-08-06T21:24:01.2243784Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-08-06T21:24:01.2244088Z 2024-08-06T21:24:01.2244177Z Args: 2024-08-06T21:24:01.2244414Z input (Tensor): the input tensor 2024-08-06T21:24:01.2244845Z sorted (bool): Whether to sort the unique elements in ascending order 2024-08-06T21:24:01.2245297Z before returning as output. 2024-08-06T21:24:01.2245731Z return_inverse (bool): Whether to also return the indices for where 2024-08-06T21:24:01.2246269Z elements in the original input ended up in the returned unique list. 2024-08-06T21:24:01.2246827Z return_counts (bool): Whether to also return the counts for each unique 2024-08-06T21:24:01.2247263Z element. 2024-08-06T21:24:01.2247610Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-08-06T21:24:01.2248227Z unique of the flattened input is returned. Otherwise, each of the 2024-08-06T21:24:01.2248764Z tensors indexed by the given dimension is treated as one of the 2024-08-06T21:24:01.2249289Z elements to apply the unique operation upon. See examples for more 2024-08-06T21:24:01.2249734Z details. Default: ``None`` 2024-08-06T21:24:01.2249957Z 2024-08-06T21:24:01.2250045Z Returns: 2024-08-06T21:24:01.2250466Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-08-06T21:24:01.2250865Z 2024-08-06T21:24:01.2251059Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-08-06T21:24:01.2251512Z - **inverse_indices** (*Tensor*): (optional) if 2024-08-06T21:24:01.2251998Z :attr:`return_inverse` is True, there will be an additional 2024-08-06T21:24:01.2252489Z returned tensor (same shape as input) representing the indices 2024-08-06T21:24:01.2253015Z for where elements in the original input map to in the output; 2024-08-06T21:24:01.2253528Z otherwise, this function will only return a single tensor. 2024-08-06T21:24:01.2253944Z - **counts** (*Tensor*): (optional) if 2024-08-06T21:24:01.2254355Z :attr:`return_counts` is True, there will be an additional 2024-08-06T21:24:01.2254835Z returned tensor (same shape as output or output.size(dim), 2024-08-06T21:24:01.2255317Z if dim was specified) representing the number of occurrences 2024-08-06T21:24:01.2255735Z for each unique value or tensor. 2024-08-06T21:24:01.2255955Z 2024-08-06T21:24:01.2256062Z Example:: 2024-08-06T21:24:01.2256190Z 2024-08-06T21:24:01.2256404Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-08-06T21:24:01.2256832Z >>> output 2024-08-06T21:24:01.2257068Z tensor([1, 2, 3]) 2024-08-06T21:24:01.2257232Z 2024-08-06T21:24:01.2257371Z >>> output, inverse_indices = torch.unique( 2024-08-06T21:24:01.2258406Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-06T21:24:01.2258859Z >>> output 2024-08-06T21:24:01.2259100Z tensor([1, 2, 3]) 2024-08-06T21:24:01.2259352Z >>> inverse_indices 2024-08-06T21:24:01.2259624Z tensor([0, 2, 1, 2]) 2024-08-06T21:24:01.2259795Z 2024-08-06T21:24:01.2259940Z >>> output, inverse_indices = torch.unique( 2024-08-06T21:24:01.2260415Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-06T21:24:01.2260867Z >>> output 2024-08-06T21:24:01.2261107Z tensor([1, 2, 3]) 2024-08-06T21:24:01.2261358Z >>> inverse_indices 2024-08-06T21:24:01.2261629Z tensor([[0, 2], 2024-08-06T21:24:01.2261867Z [1, 2]]) 2024-08-06T21:24:01.2262034Z 2024-08-06T21:24:01.2262133Z >>> a = torch.tensor([ 2024-08-06T21:24:01.2262403Z ... [ 2024-08-06T21:24:01.2262623Z ... [1, 1, 0, 0], 2024-08-06T21:24:01.2262906Z ... [1, 1, 0, 0], 2024-08-06T21:24:01.2263182Z ... [0, 0, 1, 1], 2024-08-06T21:24:01.2263440Z ... ], 2024-08-06T21:24:01.2263663Z ... [ 2024-08-06T21:24:01.2263893Z ... [0, 0, 1, 1], 2024-08-06T21:24:01.2264157Z ... [0, 0, 1, 1], 2024-08-06T21:24:01.2264435Z ... [1, 1, 1, 1], 2024-08-06T21:24:01.2264705Z ... ], 2024-08-06T21:24:01.2264921Z ... [ 2024-08-06T21:24:01.2265151Z ... [1, 1, 0, 0], 2024-08-06T21:24:01.2265428Z ... [1, 1, 0, 0], 2024-08-06T21:24:01.2265691Z ... [0, 0, 1, 1], 2024-08-06T21:24:01.2265965Z ... ], 2024-08-06T21:24:01.2266192Z ... ]) 2024-08-06T21:24:01.2266318Z 2024-08-06T21:24:01.2266546Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-08-06T21:24:01.2267233Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-08-06T21:24:01.2267705Z >>> # each other, so one of them will be removed. 2024-08-06T21:24:01.2268048Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-08-06T21:24:01.2268357Z tensor(True) 2024-08-06T21:24:01.2268637Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-08-06T21:24:01.2268961Z >>> a_unique_dim0 2024-08-06T21:24:01.2269232Z tensor([[[0, 0, 1, 1], 2024-08-06T21:24:01.2269509Z [0, 0, 1, 1], 2024-08-06T21:24:01.2269768Z [1, 1, 1, 1]], 2024-08-06T21:24:01.2270044Z [[1, 1, 0, 0], 2024-08-06T21:24:01.2270315Z [1, 1, 0, 0], 2024-08-06T21:24:01.2270574Z [0, 0, 1, 1]]]) 2024-08-06T21:24:01.2270805Z 2024-08-06T21:24:01.2271021Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-08-06T21:24:01.2271460Z >>> # `a_unique_dim0`: 2024-08-06T21:24:01.2271760Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-08-06T21:24:01.2272093Z tensor(True) 2024-08-06T21:24:01.2272376Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-08-06T21:24:01.2272691Z tensor(True) 2024-08-06T21:24:01.2272849Z 2024-08-06T21:24:01.2273056Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-08-06T21:24:01.2273568Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-08-06T21:24:01.2273966Z >>> # them will be removed. 2024-08-06T21:24:01.2274269Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-08-06T21:24:01.2274572Z tensor(True) 2024-08-06T21:24:01.2274815Z >>> torch.unique(a, dim=1) 2024-08-06T21:24:01.2275113Z tensor([[[0, 0, 1, 1], 2024-08-06T21:24:01.2275385Z [1, 1, 0, 0]], 2024-08-06T21:24:01.2275642Z [[1, 1, 1, 1], 2024-08-06T21:24:01.2275907Z [0, 0, 1, 1]], 2024-08-06T21:24:01.2276177Z [[0, 0, 1, 1], 2024-08-06T21:24:01.2276430Z [1, 1, 0, 0]]]) 2024-08-06T21:24:01.2276703Z 2024-08-06T21:24:01.2276914Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-08-06T21:24:01.2277410Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-08-06T21:24:01.2277854Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-08-06T21:24:01.2278263Z >>> # sub-tensors will be removed. 2024-08-06T21:24:01.2278590Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-08-06T21:24:01.2278880Z tensor(True) 2024-08-06T21:24:01.2279142Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-08-06T21:24:01.2279445Z tensor(True) 2024-08-06T21:24:01.2279697Z >>> torch.unique(a, dim=2) 2024-08-06T21:24:01.2279990Z tensor([[[0, 1], 2024-08-06T21:24:01.2280244Z [0, 1], 2024-08-06T21:24:01.2280482Z [1, 0]], 2024-08-06T21:24:01.2280735Z [[1, 0], 2024-08-06T21:24:01.2280980Z [1, 0], 2024-08-06T21:24:01.2281222Z [1, 1]], 2024-08-06T21:24:01.2281473Z [[0, 1], 2024-08-06T21:24:01.2281719Z [0, 1], 2024-08-06T21:24:01.2281958Z [1, 0]]]) 2024-08-06T21:24:01.2282208Z 2024-08-06T21:24:01.2282578Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.2282945Z 2024-08-06T21:24:01.2392276Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2024-08-06T21:24:01.2393107Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.2393503Z 2024-08-06T21:24:01.2393662Z Load a model from a github repo or a local directory. 2024-08-06T21:24:01.2393938Z 2024-08-06T21:24:01.2394182Z Note: Loading a model is the typical use case, but this can also be used to 2024-08-06T21:24:01.2394854Z for loading other objects such as tokenizers, loss functions, etc. 2024-08-06T21:24:01.2395189Z 2024-08-06T21:24:01.2395372Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-08-06T21:24:01.2395829Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-08-06T21:24:01.2396202Z ref (a tag or a branch). 2024-08-06T21:24:01.2396384Z 2024-08-06T21:24:01.2396552Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-08-06T21:24:01.2396938Z path to a local directory. 2024-08-06T21:24:01.2397112Z 2024-08-06T21:24:01.2397193Z Args: 2024-08-06T21:24:01.2397446Z repo_or_dir (str): If ``source`` is 'github', 2024-08-06T21:24:01.2398129Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-08-06T21:24:01.2398884Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-08-06T21:24:01.2399776Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-08-06T21:24:01.2400382Z If ``source`` is 'local' then it should be a path to a local directory. 2024-08-06T21:24:01.2400953Z model (str): the name of a callable (entrypoint) defined in the 2024-08-06T21:24:01.2401428Z repo/dir's ``hubconf.py``. 2024-08-06T21:24:01.2401837Z *args (optional): the corresponding args for callable ``model``. 2024-08-06T21:24:01.2402465Z source (str, optional): 'github' or 'local'. Specifies how 2024-08-06T21:24:01.2403102Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-08-06T21:24:01.2403752Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-08-06T21:24:01.2404333Z This parameter was introduced in v1.12 and helps ensuring that users 2024-08-06T21:24:01.2404832Z only run code from repos that they trust. 2024-08-06T21:24:01.2405101Z 2024-08-06T21:24:01.2405355Z - If ``False``, a prompt will ask the user whether the repo should 2024-08-06T21:24:01.2405766Z be trusted. 2024-08-06T21:24:01.2406170Z - If ``True``, the repo will be added to the trusted list and loaded 2024-08-06T21:24:01.2406739Z without requiring explicit confirmation. 2024-08-06T21:24:01.2407179Z - If ``"check"``, the repo will be checked against the list of 2024-08-06T21:24:01.2407734Z trusted repos in the cache. If it is not present in that list, the 2024-08-06T21:24:01.2408310Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-08-06T21:24:01.2408816Z - If ``None``: this will raise a warning, inviting the user to set 2024-08-06T21:24:01.2409370Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-08-06T21:24:01.2409930Z is only present for backward compatibility and will be removed in 2024-08-06T21:24:01.2410353Z v2.0. 2024-08-06T21:24:01.2410536Z 2024-08-06T21:24:01.2410758Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-08-06T21:24:01.2411338Z force_reload (bool, optional): whether to force a fresh download of 2024-08-06T21:24:01.2411868Z the github repo unconditionally. Does not have any effect if 2024-08-06T21:24:01.2412357Z ``source = 'local'``. Default is ``False``. 2024-08-06T21:24:01.2412837Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-08-06T21:24:01.2413368Z local caches. Note that the message about first download cannot be 2024-08-06T21:24:01.2413921Z muted. Does not have any effect if ``source = 'local'``. 2024-08-06T21:24:01.2414359Z Default is ``True``. 2024-08-06T21:24:01.2414828Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-08-06T21:24:01.2415592Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-08-06T21:24:01.2416333Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-08-06T21:24:01.2416994Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-08-06T21:24:01.2417564Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-08-06T21:24:01.2417896Z 2024-08-06T21:24:01.2417999Z Returns: 2024-08-06T21:24:01.2418352Z The output of the ``model`` callable when called with the given 2024-08-06T21:24:01.2418766Z ``*args`` and ``**kwargs``. 2024-08-06T21:24:01.2419004Z 2024-08-06T21:24:01.2419107Z Example: 2024-08-06T21:24:01.2419360Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-06T21:24:01.2419764Z >>> # from a github repo 2024-08-06T21:24:01.2420056Z >>> repo = "pytorch/vision" 2024-08-06T21:24:01.2420339Z >>> model = torch.hub.load( 2024-08-06T21:24:01.2420776Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-08-06T21:24:01.2421259Z ... ) 2024-08-06T21:24:01.2421478Z >>> # from a local directory 2024-08-06T21:24:01.2421804Z >>> path = "/some/local/path/pytorch/vision" 2024-08-06T21:24:01.2422209Z >>> # xdoctest: +SKIP 2024-08-06T21:24:01.2422620Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-08-06T21:24:01.2423052Z 2024-08-06T21:24:01.2423307Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.2423731Z 2024-08-06T21:24:01.2424264Z 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. 2024-08-06T21:24:01.2425190Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.2425742Z Download object at the given URL to a local path. 2024-08-06T21:24:01.2426046Z 2024-08-06T21:24:01.2426131Z Args: 2024-08-06T21:24:01.2426364Z url (str): URL of the object to download 2024-08-06T21:24:01.2426964Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-08-06T21:24:01.2427709Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-08-06T21:24:01.2428295Z Default: None 2024-08-06T21:24:01.2428792Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-08-06T21:24:01.2429263Z Default: True 2024-08-06T21:24:01.2429424Z 2024-08-06T21:24:01.2429524Z Example: 2024-08-06T21:24:01.2429838Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-06T21:24:01.2430205Z >>> # xdoctest: +REQUIRES(POSIX) 2024-08-06T21:24:01.2430548Z >>> torch.hub.download_url_to_file( 2024-08-06T21:24:01.2430996Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-08-06T21:24:01.2431443Z ... "/tmp/temporary_file", 2024-08-06T21:24:01.2431750Z ... ) 2024-08-06T21:24:01.2431931Z 2024-08-06T21:24:01.2432013Z 2024-08-06T21:24:01.2432383Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.2432752Z 2024-08-06T21:24:01.2433265Z 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. 2024-08-06T21:24:01.2434125Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.2434654Z Loads the Torch serialized object at the given URL. 2024-08-06T21:24:01.2434911Z 2024-08-06T21:24:01.2435104Z If downloaded file is a zip file, it will be automatically 2024-08-06T21:24:01.2435481Z decompressed. 2024-08-06T21:24:01.2435690Z 2024-08-06T21:24:01.2435903Z If the object is already present in `model_dir`, it's deserialized and 2024-08-06T21:24:01.2436325Z returned. 2024-08-06T21:24:01.2436657Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-08-06T21:24:01.2437189Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-08-06T21:24:01.2437509Z 2024-08-06T21:24:01.2437597Z Args: 2024-08-06T21:24:01.2437892Z url (str): URL of the object to download 2024-08-06T21:24:01.2438314Z model_dir (str, optional): directory in which to save the object 2024-08-06T21:24:01.2438989Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-08-06T21:24:01.2439717Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-08-06T21:24:01.2440184Z Default: True 2024-08-06T21:24:01.2440683Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-08-06T21:24:01.2441347Z ``filename-.ext`` where ```` is the first eight or more 2024-08-06T21:24:01.2441903Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-08-06T21:24:01.2442699Z ensure unique names and to verify the contents of the file. 2024-08-06T21:24:01.2443117Z Default: False 2024-08-06T21:24:01.2443618Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-08-06T21:24:01.2444408Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-08-06T21:24:01.2445112Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-08-06T21:24:01.2445489Z 2024-08-06T21:24:01.2445590Z Example: 2024-08-06T21:24:01.2445847Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-06T21:24:01.2446247Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-08-06T21:24:01.2446732Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-08-06T21:24:01.2447144Z ... ) 2024-08-06T21:24:01.2447281Z 2024-08-06T21:24:01.2447363Z 2024-08-06T21:24:01.2447735Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.2448100Z 2024-08-06T21:24:01.2472048Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=334. 2024-08-06T21:24:01.2473049Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-06T21:24:01.2473666Z Registers the function implementation as the fallback for the given key. 2024-08-06T21:24:01.2474022Z 2024-08-06T21:24:01.2474238Z This function only works for a library with global namespace ("_"). 2024-08-06T21:24:01.2474580Z 2024-08-06T21:24:01.2474665Z Args: 2024-08-06T21:24:01.2475087Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-08-06T21:24:01.2475600Z to register a fallthrough. 2024-08-06T21:24:01.2476143Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-08-06T21:24:01.2476757Z the dispatch key that the library was created with. 2024-08-06T21:24:01.2477416Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-08-06T21:24:01.2478228Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-08-06T21:24:01.2478677Z 2024-08-06T21:24:01.2480448Z Example:: 2024-08-06T21:24:01.2480717Z >>> my_lib = Library("_", "IMPL") 2024-08-06T21:24:01.2481071Z >>> def fallback_kernel(op, *args, **kwargs): 2024-08-06T21:24:01.2481457Z >>> # Handle all autocast ops generically 2024-08-06T21:24:01.2481793Z >>> # ... 2024-08-06T21:24:01.2482113Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-08-06T21:24:01.2482524Z 2024-08-06T21:24:01.2483284Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-08-06T21:24:01.2484029Z 2024-08-06T21:24:01.2484284Z my_lib.fallback(fallback_kernel, "Autocast") 2024-08-06T21:24:01.2484609Z ^ 2024-08-06T21:24:01.2535071Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=677. 2024-08-06T21:24:01.2536113Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-06T21:24:01.2537057Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-08-06T21:24:01.2537576Z 2024-08-06T21:24:01.2537755Z Also sometimes known as a "meta kernel", "abstract impl". 2024-08-06T21:24:01.2538075Z 2024-08-06T21:24:01.2538324Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-08-06T21:24:01.2538916Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-08-06T21:24:01.2539489Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-08-06T21:24:01.2540104Z what the properties of the output Tensors are. 2024-08-06T21:24:01.2540358Z 2024-08-06T21:24:01.2540616Z The FakeTensor implementation has the same signature as the operator. 2024-08-06T21:24:01.2541175Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-08-06T21:24:01.2541728Z implementation, assume that all Tensor inputs to the operator are 2024-08-06T21:24:01.2542282Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-08-06T21:24:01.2543004Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-08-06T21:24:01.2543573Z The FakeTensor implementation must consist of only PyTorch operations 2024-08-06T21:24:01.2544122Z (and may not directly access the storage or data of any input or 2024-08-06T21:24:01.2544564Z intermediate Tensors). 2024-08-06T21:24:01.2544839Z 2024-08-06T21:24:01.2545042Z This API may be used as a decorator (see examples). 2024-08-06T21:24:01.2545415Z 2024-08-06T21:24:01.2545613Z For a detailed guide on custom ops, please see 2024-08-06T21:24:01.2546203Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-08-06T21:24:01.2546556Z 2024-08-06T21:24:01.2546661Z Examples: 2024-08-06T21:24:01.2547078Z >>> import torch 2024-08-06T21:24:01.2547356Z >>> import numpy as np 2024-08-06T21:24:01.2547639Z >>> from torch import Tensor 2024-08-06T21:24:01.2547941Z >>> 2024-08-06T21:24:01.2548265Z >>> # Example 1: an operator without data-dependent output shape 2024-08-06T21:24:01.2548801Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-08-06T21:24:01.2549354Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-08-06T21:24:01.2549878Z >>> raise NotImplementedError("Implementation goes here") 2024-08-06T21:24:01.2550259Z >>> 2024-08-06T21:24:01.2550543Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-08-06T21:24:01.2550932Z >>> def _(x, weight, bias): 2024-08-06T21:24:01.2551236Z >>> assert x.dim() == 2 2024-08-06T21:24:01.2551537Z >>> assert weight.dim() == 2 2024-08-06T21:24:01.2551858Z >>> assert bias.dim() == 1 2024-08-06T21:24:01.2552190Z >>> assert x.shape[1] == weight.shape[1] 2024-08-06T21:24:01.2552562Z >>> assert weight.shape[0] == bias.shape[0] 2024-08-06T21:24:01.2552926Z >>> assert x.device == weight.device 2024-08-06T21:24:01.2553240Z >>> 2024-08-06T21:24:01.2553480Z >>> return (x @ weight.t()) + bias 2024-08-06T21:24:01.2553793Z >>> 2024-08-06T21:24:01.2554080Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-08-06T21:24:01.2554476Z >>> x = torch.randn(2, 3) 2024-08-06T21:24:01.2554787Z >>> w = torch.randn(3, 3) 2024-08-06T21:24:01.2555081Z >>> b = torch.randn(3) 2024-08-06T21:24:01.2555421Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-08-06T21:24:01.2555760Z >>> 2024-08-06T21:24:01.2555978Z >>> assert y.shape == (2, 3) 2024-08-06T21:24:01.2556319Z >>> 2024-08-06T21:24:01.2556625Z >>> # Example 2: an operator with data-dependent output shape 2024-08-06T21:24:01.2557136Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-08-06T21:24:01.2557614Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-08-06T21:24:01.2557972Z >>> x_np = x.numpy(force=True) 2024-08-06T21:24:01.2558313Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-08-06T21:24:01.2558696Z >>> return torch.tensor(res, device=x.device) 2024-08-06T21:24:01.2559036Z >>> 2024-08-06T21:24:01.2559328Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-08-06T21:24:01.2559707Z >>> def _(x): 2024-08-06T21:24:01.2560010Z >>> # Number of nonzero-elements is data-dependent. 2024-08-06T21:24:01.2560457Z >>> # Since we cannot peek at the data in an fake impl, 2024-08-06T21:24:01.2560885Z >>> # we use the ctx object to construct a new symint that 2024-08-06T21:24:01.2561286Z >>> # represents the data-dependent size. 2024-08-06T21:24:01.2561635Z >>> ctx = torch.library.get_ctx() 2024-08-06T21:24:01.2561982Z >>> nnz = ctx.new_dynamic_size() 2024-08-06T21:24:01.2562311Z >>> shape = [nnz, x.dim()] 2024-08-06T21:24:01.2562656Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-08-06T21:24:01.2563019Z >>> return result 2024-08-06T21:24:01.2563284Z >>> 2024-08-06T21:24:01.2563582Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-06T21:24:01.2563962Z >>> 2024-08-06T21:24:01.2564197Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-08-06T21:24:01.2564654Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-08-06T21:24:01.2565131Z >>> trace.print_readable() 2024-08-06T21:24:01.2565417Z >>> 2024-08-06T21:24:01.2565753Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-08-06T21:24:01.2566105Z 2024-08-06T21:24:01.2566187Z 2024-08-06T21:24:01.2566897Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-08-06T21:24:01.2567539Z 2024-08-06T21:24:01.2567635Z _._ = None 2024-08-06T21:24:01.2567832Z ^ 2024-08-06T21:24:01.2568451Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=798. 2024-08-06T21:24:01.2569326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.2569836Z Register a backward formula for this custom op. 2024-08-06T21:24:01.2570099Z 2024-08-06T21:24:01.2570303Z In order for an operator to work with autograd, you need to register 2024-08-06T21:24:01.2570726Z a backward formula: 2024-08-06T21:24:01.2571085Z 1. You must tell us how to compute gradients during the backward pass 2024-08-06T21:24:01.2571534Z by providing us a "backward" function. 2024-08-06T21:24:01.2571979Z 2. If you need any values from the forward to compute gradients, you can 2024-08-06T21:24:01.2572445Z use `setup_context` to save values for backward. 2024-08-06T21:24:01.2572709Z 2024-08-06T21:24:01.2572935Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-08-06T21:24:01.2573480Z - ``grads`` is one or more gradients. The number of gradients matches 2024-08-06T21:24:01.2573905Z the number of outputs of the operator. 2024-08-06T21:24:01.2574353Z The ``ctx`` object is `the same ctx object `_ used by 2024-08-06T21:24:01.2574932Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-08-06T21:24:01.2575435Z same as :meth:`torch.autograd.Function.backward`. 2024-08-06T21:24:01.2575712Z 2024-08-06T21:24:01.2575949Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-08-06T21:24:01.2576542Z Please save quantities needed for backward onto the ``ctx`` object via 2024-08-06T21:24:01.2577133Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-08-06T21:24:01.2577687Z or assigning them as attributes of ``ctx``. If your custom op has 2024-08-06T21:24:01.2578209Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-08-06T21:24:01.2578738Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-08-06T21:24:01.2579043Z 2024-08-06T21:24:01.2579273Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-08-06T21:24:01.2579829Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-08-06T21:24:01.2580411Z not depend on or mutate global state. If you need a non-traceable backward, 2024-08-06T21:24:01.2581029Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-08-06T21:24:01.2581370Z 2024-08-06T21:24:01.2581463Z Examples: 2024-08-06T21:24:01.2581703Z >>> import torch 2024-08-06T21:24:01.2581977Z >>> import numpy as np 2024-08-06T21:24:01.2582266Z >>> from torch import Tensor 2024-08-06T21:24:01.2582569Z >>> 2024-08-06T21:24:01.2582909Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-08-06T21:24:01.2583340Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-08-06T21:24:01.2583682Z >>> x_np = x.cpu().numpy() 2024-08-06T21:24:01.2583999Z >>> y_np = np.sin(x_np) 2024-08-06T21:24:01.2584354Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-06T21:24:01.2584721Z >>> 2024-08-06T21:24:01.2585001Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-08-06T21:24:01.2585355Z >>> x, = inputs 2024-08-06T21:24:01.2585638Z >>> ctx.save_for_backward(x) 2024-08-06T21:24:01.2585949Z >>> 2024-08-06T21:24:01.2586168Z >>> def backward(ctx, grad): 2024-08-06T21:24:01.2586481Z >>> x, = ctx.saved_tensors 2024-08-06T21:24:01.2586866Z >>> return grad * x.cos() 2024-08-06T21:24:01.2587152Z >>> 2024-08-06T21:24:01.2587464Z >>> torch.library.register_autograd( 2024-08-06T21:24:01.2587881Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-08-06T21:24:01.2588255Z ... ) 2024-08-06T21:24:01.2588472Z >>> 2024-08-06T21:24:01.2588720Z >>> x = torch.randn(3, requires_grad=True) 2024-08-06T21:24:01.2589196Z >>> y = numpy_sin(x) 2024-08-06T21:24:01.2589644Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-06T21:24:01.2590113Z >>> assert torch.allclose(grad_x, x.cos()) 2024-08-06T21:24:01.2590476Z >>> 2024-08-06T21:24:01.2590777Z >>> # Example with a keyword-only arg 2024-08-06T21:24:01.2591288Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-06T21:24:01.2591954Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-08-06T21:24:01.2592582Z >>> x_np = x.cpu().numpy() 2024-08-06T21:24:01.2593089Z >>> y_np = x_np * val 2024-08-06T21:24:01.2593438Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-06T21:24:01.2593802Z >>> 2024-08-06T21:24:01.2594159Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-08-06T21:24:01.2594623Z >>> ctx.val = keyword_only_inputs["val"] 2024-08-06T21:24:01.2594950Z >>> 2024-08-06T21:24:01.2595182Z >>> def backward(ctx, grad): 2024-08-06T21:24:01.2595481Z >>> return grad * ctx.val 2024-08-06T21:24:01.2595771Z >>> 2024-08-06T21:24:01.2596018Z >>> torch.library.register_autograd( 2024-08-06T21:24:01.2596423Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-08-06T21:24:01.2596816Z ... ) 2024-08-06T21:24:01.2597034Z >>> 2024-08-06T21:24:01.2597270Z >>> x = torch.randn(3, requires_grad=True) 2024-08-06T21:24:01.2597691Z >>> y = numpy_mul(x, val=3.14) 2024-08-06T21:24:01.2598088Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-06T21:24:01.2598542Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-08-06T21:24:01.2598844Z 2024-08-06T21:24:01.2598928Z 2024-08-06T21:24:01.2599301Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.2599667Z 2024-08-06T21:24:01.2600169Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1206. 2024-08-06T21:24:01.2600996Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.2601601Z Given an operator and some sample arguments, tests if the operator is 2024-08-06T21:24:01.2602073Z registered correctly. 2024-08-06T21:24:01.2602241Z 2024-08-06T21:24:01.2602450Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-08-06T21:24:01.2603027Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-08-06T21:24:01.2603621Z and these APIs require that the functions you pass them satisfy certain 2024-08-06T21:24:01.2604186Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-08-06T21:24:01.2604681Z ``opcheck`` tests these metadata and properties. 2024-08-06T21:24:01.2604934Z 2024-08-06T21:24:01.2605059Z Concretely, we test the following: 2024-08-06T21:24:01.2605420Z - test_schema: if the operator's schema is correct. 2024-08-06T21:24:01.2605899Z - test_autograd_registration: if autograd was registered correctly. 2024-08-06T21:24:01.2606414Z - test_faketensor: If the operator has a FakeTensor kernel 2024-08-06T21:24:01.2606880Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-08-06T21:24:01.2607381Z but not sufficient) for the operator to work with PyTorch compilation 2024-08-06T21:24:01.2607835Z APIs (torch.compile/export/FX). 2024-08-06T21:24:01.2608250Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-08-06T21:24:01.2608814Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-08-06T21:24:01.2609321Z This checks that the outputs (and gradients, if applicable) are the 2024-08-06T21:24:01.2609805Z same under eager-mode PyTorch and torch.compile. 2024-08-06T21:24:01.2610195Z This test is a superset of ``test_faketensor``. 2024-08-06T21:24:01.2610457Z 2024-08-06T21:24:01.2610654Z For best results, please call ``opcheck`` multiple times with a 2024-08-06T21:24:01.2611147Z representative set of inputs. If your operator supports 2024-08-06T21:24:01.2611667Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-08-06T21:24:01.2612248Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-08-06T21:24:01.2612753Z use ``opcheck`` with inputs on all supported devices. 2024-08-06T21:24:01.2613023Z 2024-08-06T21:24:01.2613114Z Args: 2024-08-06T21:24:01.2613426Z op: The operator. Must either be a function decorated with 2024-08-06T21:24:01.2613944Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-08-06T21:24:01.2614484Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-08-06T21:24:01.2614930Z args: The args to the operator 2024-08-06T21:24:01.2615261Z kwargs: The kwargs to the operator 2024-08-06T21:24:01.2615660Z test_utils: Tests that we should run. Default: all of them. 2024-08-06T21:24:01.2616097Z Example: ("test_schema", "test_faketensor") 2024-08-06T21:24:01.2616539Z raise_exception: If we should raise an exception on the first 2024-08-06T21:24:01.2617007Z error. If False, we will return a dict with information 2024-08-06T21:24:01.2617411Z on if each test passed or not. 2024-08-06T21:24:01.2617641Z 2024-08-06T21:24:01.2617735Z .. warning:: 2024-08-06T21:24:01.2617902Z 2024-08-06T21:24:01.2618144Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-08-06T21:24:01.2618684Z opcheck tests if your usage of torch.library APIs is correct while 2024-08-06T21:24:01.2619230Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-08-06T21:24:01.2619793Z mathematically correct. Use both to test custom ops that support 2024-08-06T21:24:01.2620226Z gradient computation. 2024-08-06T21:24:01.2620428Z 2024-08-06T21:24:01.2620519Z Example: 2024-08-06T21:24:01.2620646Z 2024-08-06T21:24:01.2620798Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:01.2621249Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-06T21:24:01.2621719Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-08-06T21:24:01.2622123Z >>> x_np = x.numpy(force=True) 2024-08-06T21:24:01.2622434Z >>> z_np = x_np + y 2024-08-06T21:24:01.2622765Z >>> return torch.from_numpy(z_np).to(x.device) 2024-08-06T21:24:01.2623110Z >>> 2024-08-06T21:24:01.2623339Z >>> @numpy_sin.register_fake 2024-08-06T21:24:01.2623646Z >>> def _(x, y): 2024-08-06T21:24:01.2623928Z >>> return torch.empty_like(x) 2024-08-06T21:24:01.2624223Z >>> 2024-08-06T21:24:01.2624476Z >>> def setup_context(ctx, inputs, output): 2024-08-06T21:24:01.2624816Z >>> y, = inputs 2024-08-06T21:24:01.2625071Z >>> ctx.y = y 2024-08-06T21:24:01.2625327Z >>> 2024-08-06T21:24:01.2625558Z >>> def backward(ctx, grad): 2024-08-06T21:24:01.2625863Z >>> return grad * ctx.y, None 2024-08-06T21:24:01.2626170Z >>> 2024-08-06T21:24:01.2626520Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-08-06T21:24:01.2627059Z >>> 2024-08-06T21:24:01.2627284Z >>> sample_inputs = [ 2024-08-06T21:24:01.2627577Z >>> (torch.randn(3), 3.14), 2024-08-06T21:24:01.2627912Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-08-06T21:24:01.2628364Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-08-06T21:24:01.2628821Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-08-06T21:24:01.2629205Z >>> ] 2024-08-06T21:24:01.2629424Z >>> 2024-08-06T21:24:01.2629662Z >>> for args in sample_inputs: 2024-08-06T21:24:01.2629992Z >>> torch.library.opcheck(foo, args) 2024-08-06T21:24:01.2630240Z 2024-08-06T21:24:01.2630323Z 2024-08-06T21:24:01.2630694Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.2631060Z 2024-08-06T21:24:01.3021139Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1042. 2024-08-06T21:24:01.3022405Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.3023128Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) 2024-08-06T21:24:01.3023577Z 2024-08-06T21:24:01.3023760Z Loads an object saved with :func:`torch.save` from a file. 2024-08-06T21:24:01.3024063Z 2024-08-06T21:24:01.3024295Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-08-06T21:24:01.3024875Z which underlie tensors, specially. They are first deserialized on the 2024-08-06T21:24:01.3025438Z CPU and are then moved to the device they were saved from. If this fails 2024-08-06T21:24:01.3025994Z (e.g. because the run time system doesn't have certain devices), an exception 2024-08-06T21:24:01.3026587Z is raised. However, storages can be dynamically remapped to an alternative 2024-08-06T21:24:01.3027183Z set of devices using the :attr:`map_location` argument. 2024-08-06T21:24:01.3027464Z 2024-08-06T21:24:01.3027746Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-08-06T21:24:01.3028495Z storage with two arguments: storage and location. The storage argument 2024-08-06T21:24:01.3029082Z will be the initial deserialization of the storage, residing on the CPU. 2024-08-06T21:24:01.3029658Z Each serialized storage has a location tag associated with it which 2024-08-06T21:24:01.3030198Z identifies the device it was saved from, and this tag is the second 2024-08-06T21:24:01.3030786Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-08-06T21:24:01.3031385Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-08-06T21:24:01.3031924Z :attr:`map_location` should return either ``None`` or a storage. If 2024-08-06T21:24:01.3032502Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-08-06T21:24:01.3033185Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-08-06T21:24:01.3033793Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-08-06T21:24:01.3034158Z 2024-08-06T21:24:01.3034399Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-08-06T21:24:01.3034998Z a device tag, it indicates the location where all tensors should be loaded. 2024-08-06T21:24:01.3035343Z 2024-08-06T21:24:01.3035617Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-08-06T21:24:01.3036195Z appearing in the file (keys), to ones that specify where to put the 2024-08-06T21:24:01.3036621Z storages (values). 2024-08-06T21:24:01.3036779Z 2024-08-06T21:24:01.3037014Z User extensions can register their own location tags and tagging and 2024-08-06T21:24:01.3037610Z deserialization methods using :func:`torch.serialization.register_package`. 2024-08-06T21:24:01.3037999Z 2024-08-06T21:24:01.3038083Z Args: 2024-08-06T21:24:01.3038533Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-08-06T21:24:01.3039145Z or a string or os.PathLike object containing a file name 2024-08-06T21:24:01.3039840Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-08-06T21:24:01.3040384Z locations 2024-08-06T21:24:01.3040774Z pickle_module: module used for unpickling metadata and objects (has to 2024-08-06T21:24:01.3041283Z match the :attr:`pickle_module` used to serialize file) 2024-08-06T21:24:01.3041789Z weights_only: Indicates whether unpickler should be restricted to 2024-08-06T21:24:01.3042294Z loading only tensors, primitive types, dictionaries 2024-08-06T21:24:01.3042996Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-08-06T21:24:01.3043681Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-08-06T21:24:01.3044475Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-08-06T21:24:01.3045267Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-08-06T21:24:01.3046061Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-08-06T21:24:01.3046749Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-08-06T21:24:01.3047357Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-08-06T21:24:01.3047937Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-08-06T21:24:01.3048385Z :attr:`errors=...`. 2024-08-06T21:24:01.3048570Z 2024-08-06T21:24:01.3048702Z .. warning:: 2024-08-06T21:24:01.3049052Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-08-06T21:24:01.3049588Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-08-06T21:24:01.3050250Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-08-06T21:24:01.3050879Z during unpickling. Never load data that could have come from an untrusted 2024-08-06T21:24:01.3051531Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-08-06T21:24:01.3051937Z 2024-08-06T21:24:01.3052039Z .. note:: 2024-08-06T21:24:01.3052418Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-08-06T21:24:01.3053063Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-08-06T21:24:01.3053717Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-08-06T21:24:01.3054158Z 2024-08-06T21:24:01.3054258Z .. note:: 2024-08-06T21:24:01.3054632Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-08-06T21:24:01.3055219Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-08-06T21:24:01.3055775Z when loading files saved by Python 2 in Python 3. If this default 2024-08-06T21:24:01.3056352Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-08-06T21:24:01.3056978Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-08-06T21:24:01.3057588Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-08-06T21:24:01.3070627Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-08-06T21:24:01.3071051Z 2024-08-06T21:24:01.3071182Z Example: 2024-08-06T21:24:01.3071481Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-08-06T21:24:01.3071888Z >>> torch.load("tensors.pt", weights_only=True) 2024-08-06T21:24:01.3072247Z # Load all tensors onto the CPU 2024-08-06T21:24:01.3072730Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-08-06T21:24:01.3073431Z # Load all tensors onto the CPU, using a function 2024-08-06T21:24:01.3073784Z >>> torch.load( 2024-08-06T21:24:01.3074195Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-08-06T21:24:01.3074659Z ... ) 2024-08-06T21:24:01.3074891Z # Load all tensors onto GPU 1 2024-08-06T21:24:01.3075205Z >>> torch.load( 2024-08-06T21:24:01.3075471Z ... "tensors.pt", 2024-08-06T21:24:01.3075809Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-08-06T21:24:01.3076196Z ... weights_only=True, 2024-08-06T21:24:01.3076512Z ... ) # type: ignore[attr-defined] 2024-08-06T21:24:01.3076840Z # Map tensors from GPU 1 to GPU 0 2024-08-06T21:24:01.3077321Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-08-06T21:24:01.3077817Z # Load tensor from io.BytesIO object 2024-08-06T21:24:01.3078294Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-08-06T21:24:01.3078790Z >>> with open("tensor.pt", "rb") as f: 2024-08-06T21:24:01.3079141Z ... buffer = io.BytesIO(f.read()) 2024-08-06T21:24:01.3079482Z >>> torch.load(buffer, weights_only=False) 2024-08-06T21:24:01.3079887Z # Load a module with 'ascii' encoding for unpickling 2024-08-06T21:24:01.3080409Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-08-06T21:24:01.3080986Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-08-06T21:24:01.3081382Z 2024-08-06T21:24:01.3081754Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.3082119Z 2024-08-06T21:24:01.4365126Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=343. 2024-08-06T21:24:01.4366236Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-06T21:24:01.4366729Z Retrieves the CUDA runtime API module. 2024-08-06T21:24:01.4366967Z 2024-08-06T21:24:01.4366972Z 2024-08-06T21:24:01.4367227Z This function initializes the CUDA runtime environment if it is not already 2024-08-06T21:24:01.4367833Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-08-06T21:24:01.4368408Z runtime API module provides access to various CUDA runtime functions. 2024-08-06T21:24:01.4368752Z 2024-08-06T21:24:01.4368839Z Args: 2024-08-06T21:24:01.4369055Z ``None`` 2024-08-06T21:24:01.4369191Z 2024-08-06T21:24:01.4369290Z Returns: 2024-08-06T21:24:01.4369555Z module: The CUDA runtime API module (_cudart). 2024-08-06T21:24:01.4369930Z 2024-08-06T21:24:01.4370018Z Raises: 2024-08-06T21:24:01.4370384Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-08-06T21:24:01.4371093Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-08-06T21:24:01.4371583Z 2024-08-06T21:24:01.4371722Z Example of CUDA operations with profiling: 2024-08-06T21:24:01.4372068Z >>> import torch 2024-08-06T21:24:01.4372363Z >>> from torch.cuda import cudart, check_error 2024-08-06T21:24:01.4372709Z >>> import os 2024-08-06T21:24:01.4372959Z >>> 2024-08-06T21:24:01.4373197Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-08-06T21:24:01.4373513Z >>> 2024-08-06T21:24:01.4373782Z >>> def perform_cuda_operations_with_streams(): 2024-08-06T21:24:01.4374144Z >>> stream = torch.cuda.Stream() 2024-08-06T21:24:01.4374496Z >>> with torch.cuda.stream(stream): 2024-08-06T21:24:01.4374861Z >>> x = torch.randn(100, 100, device='cuda') 2024-08-06T21:24:01.4375224Z >>> y = torch.randn(100, 100, device='cuda') 2024-08-06T21:24:01.4375576Z >>> z = torch.mul(x, y) 2024-08-06T21:24:01.4375891Z >>> return z 2024-08-06T21:24:01.4376128Z >>> 2024-08-06T21:24:01.4376473Z >>> torch.cuda.synchronize() 2024-08-06T21:24:01.4376827Z >>> print("====== Start nsys profiling ======") 2024-08-06T21:24:01.4377206Z >>> check_error(cudart().cudaProfilerStart()) 2024-08-06T21:24:01.4377623Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-08-06T21:24:01.4378033Z >>> result = perform_cuda_operations_with_streams() 2024-08-06T21:24:01.4378417Z >>> print("CUDA operations completed.") 2024-08-06T21:24:01.4378838Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-08-06T21:24:01.4379257Z >>> print("====== End nsys profiling ======") 2024-08-06T21:24:01.4379496Z 2024-08-06T21:24:01.4379700Z To run this example and save the profiling information, execute: 2024-08-06T21:24:01.4380394Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-06T21:24:01.4380883Z 2024-08-06T21:24:01.4381134Z This command profiles the CUDA operations in the provided script and saves 2024-08-06T21:24:01.4381704Z the profiling information to a file named `trace_name.prof`. 2024-08-06T21:24:01.4382255Z The `--profile-from-start off` option ensures that profiling starts only 2024-08-06T21:24:01.4382764Z after the `cudaProfilerStart` call in the script. 2024-08-06T21:24:01.4383261Z The `--csv` and `--print-summary` options format the profiling output as a 2024-08-06T21:24:01.4383727Z CSV file and print a summary, respectively. 2024-08-06T21:24:01.4384232Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-08-06T21:24:01.4384755Z overwrite of the output file if it already exists. 2024-08-06T21:24:01.4385098Z 2024-08-06T21:24:01.4385882Z 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)) 2024-08-06T21:24:01.4386676Z 2024-08-06T21:24:01.4387126Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-06T21:24:01.4387702Z ^ 2024-08-06T21:24:01.4466430Z 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. 2024-08-06T21:24:01.4467387Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.4467767Z 2024-08-06T21:24:01.4467996Z Append the given callback function to this ``Future``, which will be run 2024-08-06T21:24:01.4468557Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-08-06T21:24:01.4469088Z the same ``Future``, but the order in which they will be executed cannot 2024-08-06T21:24:01.4469738Z be guaranteed (to enforce a certain order consider chaining: 2024-08-06T21:24:01.4470248Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-08-06T21:24:01.4470783Z is the reference to this ``Future``. The callback function can use the 2024-08-06T21:24:01.4471312Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-08-06T21:24:01.4471862Z already completed, the given callback will be run immediately inline. 2024-08-06T21:24:01.4472195Z 2024-08-06T21:24:01.4472401Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-08-06T21:24:01.4472931Z callback might be invoked while the async kernels that are populating 2024-08-06T21:24:01.4473479Z those tensors haven't yet finished executing on the device. However, the 2024-08-06T21:24:01.4474031Z callback will be invoked with some dedicated streams set as current 2024-08-06T21:24:01.4474560Z (fetched from a global pool) which will be synchronized with those 2024-08-06T21:24:01.4475174Z kernels. Hence any operation performed by the callback on these tensors 2024-08-06T21:24:01.4475912Z will be scheduled on the device after the kernels complete. In other 2024-08-06T21:24:01.4476524Z words, as long as the callback doesn't switch streams, it can safely 2024-08-06T21:24:01.4477189Z manipulate the result without any additional synchronization. This is 2024-08-06T21:24:01.4477699Z similar to the non-blocking behavior of :meth:`wait`. 2024-08-06T21:24:01.4477965Z 2024-08-06T21:24:01.4478192Z Similarly, if the callback returns a value that contains tensors that 2024-08-06T21:24:01.4478713Z reside on a GPU, it can do so even if the kernels that are producing 2024-08-06T21:24:01.4479245Z these tensors are still running on the device, as long as the callback 2024-08-06T21:24:01.4479790Z didn't change streams during its execution. If one wants to change 2024-08-06T21:24:01.4480309Z streams, one must be careful to re-synchronize them with the original 2024-08-06T21:24:01.4480886Z streams, that is, those that were current when the callback was invoked. 2024-08-06T21:24:01.4481226Z 2024-08-06T21:24:01.4481311Z Args: 2024-08-06T21:24:01.4481641Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-08-06T21:24:01.4482063Z the only argument. 2024-08-06T21:24:01.4482297Z 2024-08-06T21:24:01.4482384Z Returns: 2024-08-06T21:24:01.4482672Z A new ``Future`` object that holds the return value of the 2024-08-06T21:24:01.4483116Z ``callback`` and will be marked as completed when the given 2024-08-06T21:24:01.4483508Z ``callback`` finishes. 2024-08-06T21:24:01.4483676Z 2024-08-06T21:24:01.4483892Z .. note:: Note that if the callback function throws, either 2024-08-06T21:24:01.4484391Z through the original future being completed with an exception and 2024-08-06T21:24:01.4484904Z calling ``fut.wait()``, or through other code in the callback, the 2024-08-06T21:24:01.4485425Z future returned by ``then`` will be marked appropriately with the 2024-08-06T21:24:01.4485946Z encountered error. However, if this callback later completes 2024-08-06T21:24:01.4486466Z additional futures, those futures are not marked as completed with 2024-08-06T21:24:01.4487066Z an error and the user is responsible for handling completion/waiting 2024-08-06T21:24:01.4487502Z on those futures independently. 2024-08-06T21:24:01.4487706Z 2024-08-06T21:24:01.4487795Z Example:: 2024-08-06T21:24:01.4488072Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-06T21:24:01.4488437Z >>> def callback(fut): 2024-08-06T21:24:01.4488739Z ... print(f"RPC return value is {fut.wait()}.") 2024-08-06T21:24:01.4489102Z >>> fut = torch.futures.Future() 2024-08-06T21:24:01.4489485Z >>> # The inserted callback will print the return value when 2024-08-06T21:24:01.4489880Z >>> # receiving the response from "worker1" 2024-08-06T21:24:01.4490219Z >>> cb_fut = fut.then(callback) 2024-08-06T21:24:01.4490562Z >>> chain_cb_fut = cb_fut.then( 2024-08-06T21:24:01.4490902Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-08-06T21:24:01.4491252Z ... ) 2024-08-06T21:24:01.4491474Z >>> fut.set_result(5) 2024-08-06T21:24:01.4491737Z RPC return value is 5. 2024-08-06T21:24:01.4492022Z Chained cb done. None 2024-08-06T21:24:01.4492188Z 2024-08-06T21:24:01.4492450Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.4492816Z 2024-08-06T21:24:01.4493346Z 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. 2024-08-06T21:24:01.4494252Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.4494626Z 2024-08-06T21:24:01.4494842Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-08-06T21:24:01.4495369Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-08-06T21:24:01.4495818Z cannot be marked completed twice. 2024-08-06T21:24:01.4496015Z 2024-08-06T21:24:01.4496243Z If the result contains tensors that reside on GPUs, this method can be 2024-08-06T21:24:01.4496788Z called even if the asynchronous kernels that are populating those 2024-08-06T21:24:01.4497374Z tensors haven't yet completed running on the device, provided that the 2024-08-06T21:24:01.4497941Z streams on which those kernels were enqueued are set as the current ones 2024-08-06T21:24:01.4498496Z when this method is called. Put simply, it's safe to call this method 2024-08-06T21:24:01.4499032Z immediately after launching those kernels, without any additional 2024-08-06T21:24:01.4499594Z synchronization, as long as one doesn't change streams in between. This 2024-08-06T21:24:01.4500159Z method will record events on all the relevant current streams and will 2024-08-06T21:24:01.4500685Z use them to ensure proper scheduling for all the consumers of this 2024-08-06T21:24:01.4501096Z ``Future``. 2024-08-06T21:24:01.4501221Z 2024-08-06T21:24:01.4501315Z Args: 2024-08-06T21:24:01.4501586Z result (object): the result object of this ``Future``. 2024-08-06T21:24:01.4501872Z 2024-08-06T21:24:01.4501962Z Example:: 2024-08-06T21:24:01.4502231Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-06T21:24:01.4502580Z >>> import threading 2024-08-06T21:24:01.4502837Z >>> import time 2024-08-06T21:24:01.4503095Z >>> def slow_set_future(fut, value): 2024-08-06T21:24:01.4503403Z ... time.sleep(0.5) 2024-08-06T21:24:01.4503684Z ... fut.set_result(value) 2024-08-06T21:24:01.4504132Z >>> fut = torch.futures.Future() 2024-08-06T21:24:01.4504440Z >>> t = threading.Thread( 2024-08-06T21:24:01.4504739Z ... target=slow_set_future, 2024-08-06T21:24:01.4505056Z ... args=(fut, torch.ones(2) * 3) 2024-08-06T21:24:01.4505352Z ... ) 2024-08-06T21:24:01.4505565Z >>> t.start() 2024-08-06T21:24:01.4505794Z >>> print(fut.wait()) 2024-08-06T21:24:01.4506057Z tensor([3., 3.]) 2024-08-06T21:24:01.4506300Z >>> t.join() 2024-08-06T21:24:01.4506433Z 2024-08-06T21:24:01.4506686Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.4507187Z 2024-08-06T21:24:01.4679050Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=201. 2024-08-06T21:24:01.4680039Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:01.4680572Z Return the sum of each row of the given sparse tensor. 2024-08-06T21:24:01.4680856Z 2024-08-06T21:24:01.4681082Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-08-06T21:24:01.4681630Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-08-06T21:24:01.4682153Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-08-06T21:24:01.4682621Z returns a dense tensor instead of a sparse tensor. 2024-08-06T21:24:01.4683060Z 2024-08-06T21:24:01.4683322Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-08-06T21:24:01.4683901Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-08-06T21:24:01.4684209Z 2024-08-06T21:24:01.4684438Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-08-06T21:24:01.4685023Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-08-06T21:24:01.4685378Z 2024-08-06T21:24:01.4685483Z Args: 2024-08-06T21:24:01.4685726Z input (Tensor): the input sparse tensor 2024-08-06T21:24:01.4686244Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-08-06T21:24:01.4686738Z over all dims. 2024-08-06T21:24:01.4687157Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-08-06T21:24:01.4687650Z Default: dtype of :attr:`input`. 2024-08-06T21:24:01.4687880Z 2024-08-06T21:24:01.4687994Z Example:: 2024-08-06T21:24:01.4688122Z 2024-08-06T21:24:01.4688222Z >>> nnz = 3 2024-08-06T21:24:01.4688452Z >>> dims = [5, 5, 2, 3] 2024-08-06T21:24:01.4688805Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-08-06T21:24:01.4689365Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-08-06T21:24:01.4689789Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-08-06T21:24:01.4690137Z >>> size = torch.Size(dims) 2024-08-06T21:24:01.4690487Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-06T21:24:01.4690854Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-08-06T21:24:01.4691178Z >>> S 2024-08-06T21:24:01.4691424Z tensor(indices=tensor([[2, 0, 3], 2024-08-06T21:24:01.4691730Z [2, 4, 1]]), 2024-08-06T21:24:01.4692090Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-08-06T21:24:01.4692448Z [ 0.3411, 0.0918, -0.2312]], 2024-08-06T21:24:01.4692672Z 2024-08-06T21:24:01.4692782Z [[ 0.5348, 0.0634, -2.0494], 2024-08-06T21:24:01.4693120Z [-0.7125, -1.0646, 2.1844]], 2024-08-06T21:24:01.4693340Z 2024-08-06T21:24:01.4693466Z [[ 0.1276, 0.1874, -0.6334], 2024-08-06T21:24:01.4693791Z [-1.9682, -0.5340, 0.7483]]]), 2024-08-06T21:24:01.4694167Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-08-06T21:24:01.4694420Z 2024-08-06T21:24:01.4694625Z # when sum over only part of sparse_dims, return a sparse tensor 2024-08-06T21:24:01.4695039Z >>> torch.sparse.sum(S, [1, 3]) 2024-08-06T21:24:01.4695373Z tensor(indices=tensor([[0, 2, 3]]), 2024-08-06T21:24:01.4695712Z values=tensor([[-1.4512, 0.4073], 2024-08-06T21:24:01.4696031Z [-0.8901, 0.2017], 2024-08-06T21:24:01.4696355Z [-0.3183, -1.7539]]), 2024-08-06T21:24:01.4696710Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-08-06T21:24:01.4697035Z 2024-08-06T21:24:01.4697188Z # when sum over all sparse dim, return a dense tensor 2024-08-06T21:24:01.4697568Z # with summed dims squeezed 2024-08-06T21:24:01.4697893Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-08-06T21:24:01.4698203Z tensor([-2.6596, -1.1450]) 2024-08-06T21:24:01.4698491Z 2024-08-06T21:24:01.4698867Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:01.4699236Z 2024-08-06T21:24:02.5511751Z 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=2720. 2024-08-06T21:24:02.5512749Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.5513148Z 2024-08-06T21:24:02.5513372Z Gathers picklable objects from the whole group in a single process. 2024-08-06T21:24:02.5513943Z 2024-08-06T21:24:02.5514193Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-08-06T21:24:02.5514693Z object must be picklable in order to be gathered. 2024-08-06T21:24:02.5514962Z 2024-08-06T21:24:02.5515044Z Args: 2024-08-06T21:24:02.5515310Z obj (Any): Input object. Must be picklable. 2024-08-06T21:24:02.5515749Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-08-06T21:24:02.5516265Z should be correctly sized as the size of the group for this 2024-08-06T21:24:02.5516784Z collective and will contain the output. Must be ``None`` on non-dst 2024-08-06T21:24:02.5517228Z ranks. (default is ``None``) 2024-08-06T21:24:02.5517809Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-08-06T21:24:02.5518538Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-08-06T21:24:02.5519081Z the default process group will be used. Default is ``None``. 2024-08-06T21:24:02.5519382Z 2024-08-06T21:24:02.5519494Z Returns: 2024-08-06T21:24:02.5519809Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-08-06T21:24:02.5520218Z output of the collective. 2024-08-06T21:24:02.5520522Z 2024-08-06T21:24:02.5520769Z .. note:: Note that this API differs slightly from the gather collective 2024-08-06T21:24:02.5521321Z since it does not provide an async_op handle and thus will be a blocking 2024-08-06T21:24:02.5521743Z call. 2024-08-06T21:24:02.5521859Z 2024-08-06T21:24:02.5522104Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-08-06T21:24:02.5522654Z of objects must be moved to the GPU device before communication takes 2024-08-06T21:24:02.5523131Z place. In this case, the device used is given by 2024-08-06T21:24:02.5523608Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-08-06T21:24:02.5524144Z ensure that this is set so that each rank has an individual GPU, via 2024-08-06T21:24:02.5524584Z ``torch.cuda.set_device()``. 2024-08-06T21:24:02.5524780Z 2024-08-06T21:24:02.5524883Z .. warning:: 2024-08-06T21:24:02.5525203Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-08-06T21:24:02.5525743Z known to be insecure. It is possible to construct malicious pickle data 2024-08-06T21:24:02.5526301Z which will execute arbitrary code during unpickling. Only call this 2024-08-06T21:24:02.5526734Z function with data you trust. 2024-08-06T21:24:02.5526944Z 2024-08-06T21:24:02.5527033Z .. warning:: 2024-08-06T21:24:02.5527379Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-08-06T21:24:02.5527929Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-08-06T21:24:02.5528448Z pickled. Please consider using :func:`gather` instead. 2024-08-06T21:24:02.5528728Z 2024-08-06T21:24:02.5528834Z Example:: 2024-08-06T21:24:02.5529087Z >>> # xdoctest: +SKIP("need process group init") 2024-08-06T21:24:02.5529518Z >>> # Note: Process group initialization omitted on each rank. 2024-08-06T21:24:02.5529993Z >>> import torch.distributed as dist 2024-08-06T21:24:02.5530315Z >>> # Assumes world_size of 3. 2024-08-06T21:24:02.5530697Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-08-06T21:24:02.5531115Z >>> output = [None for _ in gather_objects] 2024-08-06T21:24:02.5531446Z >>> dist.gather_object( 2024-08-06T21:24:02.5531750Z ... gather_objects[dist.get_rank()], 2024-08-06T21:24:02.5532111Z ... output if dist.get_rank() == 0 else None, 2024-08-06T21:24:02.5532431Z ... dst=0 2024-08-06T21:24:02.5532657Z ... ) 2024-08-06T21:24:02.5532871Z >>> # On rank 0 2024-08-06T21:24:02.5533090Z >>> output 2024-08-06T21:24:02.5533315Z ['foo', 12, {1: 2}] 2024-08-06T21:24:02.5533467Z 2024-08-06T21:24:02.5533728Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.5534127Z 2024-08-06T21:24:02.5683660Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-08-06T21:24:02.5684969Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.5685361Z 2024-08-06T21:24:02.5685481Z Module ``torch.distributed.launch``. 2024-08-06T21:24:02.5685695Z 2024-08-06T21:24:02.5685961Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-08-06T21:24:02.5686471Z training processes on each of the training nodes. 2024-08-06T21:24:02.5686744Z 2024-08-06T21:24:02.5686853Z .. warning:: 2024-08-06T21:24:02.5686979Z 2024-08-06T21:24:02.5687245Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-08-06T21:24:02.5687625Z 2024-08-06T21:24:02.5687864Z The utility can be used for single-node distributed training, in which one or 2024-08-06T21:24:02.5688466Z more processes per node will be spawned. The utility can be used for either 2024-08-06T21:24:02.5689042Z CPU training or GPU training. If the utility is used for GPU training, 2024-08-06T21:24:02.5689630Z each distributed process will be operating on a single GPU. This can achieve 2024-08-06T21:24:02.5690395Z well-improved single-node training performance. It can also be used in 2024-08-06T21:24:02.5691021Z multi-node distributed training, by spawning up multiple processes on each node 2024-08-06T21:24:02.5691645Z for well-improved multi-node distributed training performance as well. 2024-08-06T21:24:02.5692224Z This will especially be beneficial for systems with multiple Infiniband 2024-08-06T21:24:02.5692832Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-08-06T21:24:02.5693362Z aggregated communication bandwidth. 2024-08-06T21:24:02.5693583Z 2024-08-06T21:24:02.5693816Z In both cases of single-node distributed training or multi-node distributed 2024-08-06T21:24:02.5694415Z training, this utility will launch the given number of processes per node 2024-08-06T21:24:02.5694987Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-08-06T21:24:02.5695564Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-08-06T21:24:02.5696120Z and each process will be operating on a single GPU from *GPU 0 to 2024-08-06T21:24:02.5696541Z GPU (nproc_per_node - 1)*. 2024-08-06T21:24:02.5696731Z 2024-08-06T21:24:02.5696832Z **How to use this module:** 2024-08-06T21:24:02.5697007Z 2024-08-06T21:24:02.5697172Z 1. Single-Node multi-process distributed training 2024-08-06T21:24:02.5697431Z 2024-08-06T21:24:02.5697523Z :: 2024-08-06T21:24:02.5697643Z 2024-08-06T21:24:02.5697883Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-06T21:24:02.5698431Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-08-06T21:24:02.5698852Z arguments of your training script) 2024-08-06T21:24:02.5699098Z 2024-08-06T21:24:02.5699306Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-08-06T21:24:02.5699640Z 2024-08-06T21:24:02.5699692Z 2024-08-06T21:24:02.5699834Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-08-06T21:24:02.5700084Z 2024-08-06T21:24:02.5700187Z :: 2024-08-06T21:24:02.5700298Z 2024-08-06T21:24:02.5700534Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-06T21:24:02.5701053Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-08-06T21:24:02.5701538Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-06T21:24:02.5702007Z and all other arguments of your training script) 2024-08-06T21:24:02.5702284Z 2024-08-06T21:24:02.5702369Z Node 2: 2024-08-06T21:24:02.5702482Z 2024-08-06T21:24:02.5702578Z :: 2024-08-06T21:24:02.5702685Z 2024-08-06T21:24:02.5702919Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-06T21:24:02.5703482Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-08-06T21:24:02.5703963Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-06T21:24:02.5704441Z and all other arguments of your training script) 2024-08-06T21:24:02.5704705Z 2024-08-06T21:24:02.5704866Z 3. To look up what optional arguments this module offers: 2024-08-06T21:24:02.5705149Z 2024-08-06T21:24:02.5705235Z :: 2024-08-06T21:24:02.5705343Z 2024-08-06T21:24:02.5705490Z python -m torch.distributed.launch --help 2024-08-06T21:24:02.5705730Z 2024-08-06T21:24:02.5705733Z 2024-08-06T21:24:02.5705831Z **Important Notices:** 2024-08-06T21:24:02.5705999Z 2024-08-06T21:24:02.5706185Z 1. This utility and multi-process distributed (single-node or 2024-08-06T21:24:02.5706823Z multi-node) GPU training currently only achieves the best performance using 2024-08-06T21:24:02.5707432Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-08-06T21:24:02.5707908Z use for GPU training. 2024-08-06T21:24:02.5708063Z 2024-08-06T21:24:02.5708293Z 2. In your training program, you must parse the command-line argument: 2024-08-06T21:24:02.5708859Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-08-06T21:24:02.5709496Z If your training program uses GPUs, you should ensure that your code only 2024-08-06T21:24:02.5710037Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-08-06T21:24:02.5710341Z 2024-08-06T21:24:02.5710462Z Parsing the local_rank argument 2024-08-06T21:24:02.5710651Z 2024-08-06T21:24:02.5710735Z :: 2024-08-06T21:24:02.5710857Z 2024-08-06T21:24:02.5710954Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.5711225Z >>> import argparse 2024-08-06T21:24:02.5711503Z >>> parser = argparse.ArgumentParser() 2024-08-06T21:24:02.5711935Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-08-06T21:24:02.5712356Z >>> args = parser.parse_args() 2024-08-06T21:24:02.5712560Z 2024-08-06T21:24:02.5712681Z Set your device to local rank using either 2024-08-06T21:24:02.5712919Z 2024-08-06T21:24:02.5713001Z :: 2024-08-06T21:24:02.5713111Z 2024-08-06T21:24:02.5713324Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-08-06T21:24:02.5713636Z 2024-08-06T21:24:02.5713722Z or 2024-08-06T21:24:02.5713841Z 2024-08-06T21:24:02.5714039Z :: 2024-08-06T21:24:02.5714145Z 2024-08-06T21:24:02.5714307Z >>> with torch.cuda.device(args.local_rank): 2024-08-06T21:24:02.5714640Z >>> # your code to run 2024-08-06T21:24:02.5714914Z >>> ... 2024-08-06T21:24:02.5715042Z 2024-08-06T21:24:02.5715162Z .. versionchanged:: 2.0.0 2024-08-06T21:24:02.5715334Z 2024-08-06T21:24:02.5715580Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-08-06T21:24:02.5716188Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-08-06T21:24:02.5716692Z previously used underscored ``--local_rank``. 2024-08-06T21:24:02.5716943Z 2024-08-06T21:24:02.5717196Z For backward compatibility, it may be necessary for users to handle both 2024-08-06T21:24:02.5717851Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-08-06T21:24:02.5718462Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-08-06T21:24:02.5719058Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-08-06T21:24:02.5719655Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-08-06T21:24:02.5720168Z including ``"--local-rank"`` should be sufficient. 2024-08-06T21:24:02.5720425Z 2024-08-06T21:24:02.5720679Z 3. In your training program, you are supposed to call the following function 2024-08-06T21:24:02.5721264Z at the beginning to start the distributed backend. It is strongly recommended 2024-08-06T21:24:02.5721863Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-08-06T21:24:02.5722459Z but ``env://`` is the one that is officially supported by this module. 2024-08-06T21:24:02.5722763Z 2024-08-06T21:24:02.5722871Z :: 2024-08-06T21:24:02.5722981Z 2024-08-06T21:24:02.5723187Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-08-06T21:24:02.5723661Z >>> init_method='env://') 2024-08-06T21:24:02.5723900Z 2024-08-06T21:24:02.5724152Z 4. In your training program, you can either use regular distributed functions 2024-08-06T21:24:02.5724748Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-08-06T21:24:02.5725311Z training program uses GPUs for training and you would like to use 2024-08-06T21:24:02.5725821Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-08-06T21:24:02.5726218Z here is how to configure it. 2024-08-06T21:24:02.5726409Z 2024-08-06T21:24:02.5726493Z :: 2024-08-06T21:24:02.5726600Z 2024-08-06T21:24:02.5726800Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-08-06T21:24:02.5727239Z >>> device_ids=[args.local_rank], 2024-08-06T21:24:02.5727642Z >>> output_device=args.local_rank) 2024-08-06T21:24:02.5727899Z 2024-08-06T21:24:02.5728238Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-08-06T21:24:02.5728836Z that your code will be operating on. This is generally the local rank of the 2024-08-06T21:24:02.5729413Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-08-06T21:24:02.5729987Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-08-06T21:24:02.5730414Z utility 2024-08-06T21:24:02.5730527Z 2024-08-06T21:24:02.5730774Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-08-06T21:24:02.5731356Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-08-06T21:24:02.5731917Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-08-06T21:24:02.5732439Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-08-06T21:24:02.5732924Z will not pass ``--local-rank`` when you specify this flag. 2024-08-06T21:24:02.5733205Z 2024-08-06T21:24:02.5733313Z .. warning:: 2024-08-06T21:24:02.5733441Z 2024-08-06T21:24:02.5733643Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-08-06T21:24:02.5734152Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-08-06T21:24:02.5734575Z write to a networked filesystem. See 2024-08-06T21:24:02.5735012Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-08-06T21:24:02.5735511Z how things can go wrong if you don't do this correctly. 2024-08-06T21:24:02.5735782Z 2024-08-06T21:24:02.5735798Z 2024-08-06T21:24:02.5735802Z 2024-08-06T21:24:02.5735806Z 2024-08-06T21:24:02.5736055Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.5736421Z 2024-08-06T21:24:02.6091273Z 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. 2024-08-06T21:24:02.6093030Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.6093429Z 2024-08-06T21:24:02.6093735Z DistributedOptimizer takes remote references to parameters scattered 2024-08-06T21:24:02.6094375Z across workers and applies the given optimizer locally for each parameter. 2024-08-06T21:24:02.6094743Z 2024-08-06T21:24:02.6095013Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-08-06T21:24:02.6095572Z to retrieve the gradients for specific parameters. 2024-08-06T21:24:02.6095872Z 2024-08-06T21:24:02.6095997Z Concurrent calls to 2024-08-06T21:24:02.6096353Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-08-06T21:24:02.6096857Z either from the same or different clients, will 2024-08-06T21:24:02.6097474Z be serialized on each worker -- as each worker's optimizer can only work 2024-08-06T21:24:02.6098033Z on one set of gradients at a time. However, there is no guarantee that 2024-08-06T21:24:02.6098667Z the full forward-backward-optimizer sequence will execute for one client 2024-08-06T21:24:02.6099294Z at a time. This means that the gradients being applied may not correspond 2024-08-06T21:24:02.6099908Z to the latest forward pass executed on a given worker. Also, there is no 2024-08-06T21:24:02.6100366Z guaranteed ordering across workers. 2024-08-06T21:24:02.6100631Z 2024-08-06T21:24:02.6100893Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-08-06T21:24:02.6101639Z by default, so that optimizer updates are not blocked by the Python Global 2024-08-06T21:24:02.6102406Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-08-06T21:24:02.6103062Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-08-06T21:24:02.6103822Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-08-06T21:24:02.6104307Z for your own custom optimizers. 2024-08-06T21:24:02.6104558Z 2024-08-06T21:24:02.6104641Z Args: 2024-08-06T21:24:02.6105094Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-08-06T21:24:02.6105584Z instantiate on each worker. 2024-08-06T21:24:02.6106042Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-08-06T21:24:02.6106462Z to optimize. 2024-08-06T21:24:02.6106964Z args: arguments to pass to the optimizer constructor on each worker. 2024-08-06T21:24:02.6107522Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-08-06T21:24:02.6107914Z 2024-08-06T21:24:02.6108015Z Example:: 2024-08-06T21:24:02.6108256Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:02.6108687Z >>> import torch.distributed.autograd as dist_autograd 2024-08-06T21:24:02.6109130Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:02.6109484Z >>> from torch import optim 2024-08-06T21:24:02.6109884Z >>> from torch.distributed.optim import DistributedOptimizer 2024-08-06T21:24:02.6110292Z >>> 2024-08-06T21:24:02.6110553Z >>> with dist_autograd.context() as context_id: 2024-08-06T21:24:02.6110951Z >>> # Forward pass. 2024-08-06T21:24:02.6111321Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-08-06T21:24:02.6111897Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-08-06T21:24:02.6112384Z >>> loss = rref1.to_here() + rref2.to_here() 2024-08-06T21:24:02.6112717Z >>> 2024-08-06T21:24:02.6112981Z >>> # Backward pass. 2024-08-06T21:24:02.6113299Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-08-06T21:24:02.6113655Z >>> 2024-08-06T21:24:02.6113911Z >>> # Optimizer. 2024-08-06T21:24:02.6114178Z >>> dist_optim = DistributedOptimizer( 2024-08-06T21:24:02.6114559Z >>> optim.SGD, 2024-08-06T21:24:02.6114812Z >>> [rref1, rref2], 2024-08-06T21:24:02.6115066Z >>> lr=0.05, 2024-08-06T21:24:02.6115417Z >>> ) 2024-08-06T21:24:02.6115658Z >>> dist_optim.step(context_id) 2024-08-06T21:24:02.6115867Z 2024-08-06T21:24:02.6116089Z __ https://github.com/pytorch/tutorials/pull/1465 2024-08-06T21:24:02.6116365Z 2024-08-06T21:24:02.6116620Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.6117049Z 2024-08-06T21:24:02.6117854Z 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. 2024-08-06T21:24:02.6118991Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.6119426Z 2024-08-06T21:24:02.6119861Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-08-06T21:24:02.6120599Z This optimizer runs local optimizer at every step. 2024-08-06T21:24:02.6121228Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-08-06T21:24:02.6121740Z 2024-08-06T21:24:02.6121840Z Args: 2024-08-06T21:24:02.6122063Z optim: The local optimizer. 2024-08-06T21:24:02.6122537Z averager: A model averager instance to run post-localSGD algorithm. 2024-08-06T21:24:02.6122893Z 2024-08-06T21:24:02.6123027Z Example:: 2024-08-06T21:24:02.6123150Z 2024-08-06T21:24:02.6123277Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:02.6123629Z >>> import torch 2024-08-06T21:24:02.6123948Z >>> import torch.distributed as dist 2024-08-06T21:24:02.6124489Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-06T21:24:02.6124989Z >>> import torch.nn as nn 2024-08-06T21:24:02.6125425Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-08-06T21:24:02.6126047Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-06T21:24:02.6126550Z >>> PostLocalSGDState, 2024-08-06T21:24:02.6126893Z >>> post_localSGD_hook, 2024-08-06T21:24:02.6127151Z >>> ) 2024-08-06T21:24:02.6127357Z >>> 2024-08-06T21:24:02.6127791Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-06T21:24:02.6128194Z >>> module, device_ids=[rank], output_device=rank 2024-08-06T21:24:02.6128629Z >>> ) 2024-08-06T21:24:02.6128822Z >>> 2024-08-06T21:24:02.6129144Z >>> # Register a post-localSGD communication hook. 2024-08-06T21:24:02.6129704Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-06T21:24:02.6130281Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-06T21:24:02.6130632Z >>> 2024-08-06T21:24:02.6130956Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-08-06T21:24:02.6131587Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-08-06T21:24:02.6132118Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-06T21:24:02.6132620Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-08-06T21:24:02.6133072Z >>> opt = PostLocalSGDOptimizer( 2024-08-06T21:24:02.6133384Z >>> optim=local_optim, 2024-08-06T21:24:02.6133823Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-06T21:24:02.6134274Z >>> ) 2024-08-06T21:24:02.6134468Z >>> 2024-08-06T21:24:02.6134814Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-08-06T21:24:02.6135525Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-08-06T21:24:02.6136314Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-08-06T21:24:02.6136918Z >>> for step in range(0, 200): 2024-08-06T21:24:02.6137222Z >>> opt.zero_grad() 2024-08-06T21:24:02.6137497Z >>> loss = loss_fn(output, labels) 2024-08-06T21:24:02.6137817Z >>> loss.backward() 2024-08-06T21:24:02.6138137Z >>> opt.step() 2024-08-06T21:24:02.6138289Z 2024-08-06T21:24:02.6138547Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.6138930Z 2024-08-06T21:24:02.6210009Z 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. 2024-08-06T21:24:02.6211133Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.6211514Z 2024-08-06T21:24:02.6211935Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-08-06T21:24:02.6212453Z 2024-08-06T21:24:02.6212580Z The sharing is done as described by ZeRO_. 2024-08-06T21:24:02.6212977Z 2024-08-06T21:24:02.6213146Z The local optimizer instance in each rank is only 2024-08-06T21:24:02.6213654Z responsible for updating approximately ``1 / world_size`` parameters and 2024-08-06T21:24:02.6214225Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-08-06T21:24:02.6214780Z parameters are updated locally, each rank will broadcast its parameters to 2024-08-06T21:24:02.6215333Z all other peers to keep all model replicas in the same state. 2024-08-06T21:24:02.6215828Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-08-06T21:24:02.6216385Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-08-06T21:24:02.6216863Z memory consumption. 2024-08-06T21:24:02.6217013Z 2024-08-06T21:24:02.6217285Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-08-06T21:24:02.6217883Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-08-06T21:24:02.6218472Z not divided among ranks. The partition is arbitrary and might not match the 2024-08-06T21:24:02.6218963Z the parameter registration or usage order. 2024-08-06T21:24:02.6219193Z 2024-08-06T21:24:02.6219285Z Arguments: 2024-08-06T21:24:02.6219605Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-08-06T21:24:02.6220194Z or :class:`dict` s giving all parameters, which will be sharded 2024-08-06T21:24:02.6220602Z across ranks. 2024-08-06T21:24:02.6220753Z 2024-08-06T21:24:02.6220841Z Keyword Args: 2024-08-06T21:24:02.6221208Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-08-06T21:24:02.6221644Z optimizer. 2024-08-06T21:24:02.6221985Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-08-06T21:24:02.6222516Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-08-06T21:24:02.6222979Z :meth:`torch.distributed.init_process_group`). 2024-08-06T21:24:02.6223454Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-08-06T21:24:02.6224016Z packed into buckets to speed up communication, and ``param.data`` 2024-08-06T21:24:02.6224552Z fields point to bucket views at different offsets; if ``False``, 2024-08-06T21:24:02.6225070Z each individual parameter is communicated separately, and each 2024-08-06T21:24:02.6225552Z ``params.data`` stays intact (default: ``False``). 2024-08-06T21:24:02.6226019Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-08-06T21:24:02.6226527Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-08-06T21:24:02.6227139Z synchronization; this requires (1) either a functional optimizer 2024-08-06T21:24:02.6227659Z for the ``optimizer_class`` argument or one with a functional 2024-08-06T21:24:02.6228122Z equivalent and (2) registering a DDP communication hook 2024-08-06T21:24:02.6228621Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-08-06T21:24:02.6229111Z parameters are packed into buckets matching those in 2024-08-06T21:24:02.6229538Z :class:`DistributedDataParallel`, meaning that the 2024-08-06T21:24:02.6230024Z ``parameters_as_bucket_view`` argument is ignored. 2024-08-06T21:24:02.6230482Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-08-06T21:24:02.6230875Z (per normal). 2024-08-06T21:24:02.6231133Z (default: ``False``) 2024-08-06T21:24:02.6231545Z **defaults: any trailing arguments, which are forwarded to the local 2024-08-06T21:24:02.6231956Z optimizer. 2024-08-06T21:24:02.6232107Z 2024-08-06T21:24:02.6232211Z Example:: 2024-08-06T21:24:02.6232330Z 2024-08-06T21:24:02.6232442Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.6232709Z >>> import torch.nn as nn 2024-08-06T21:24:02.6233102Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-08-06T21:24:02.6233713Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-08-06T21:24:02.6234295Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-08-06T21:24:02.6234769Z >>> ddp = DDP(model, device_ids=[rank]) 2024-08-06T21:24:02.6235129Z >>> opt = ZeroRedundancyOptimizer( 2024-08-06T21:24:02.6235450Z >>> ddp.parameters(), 2024-08-06T21:24:02.6235772Z >>> optimizer_class=torch.optim.Adam, 2024-08-06T21:24:02.6236101Z >>> lr=0.01 2024-08-06T21:24:02.6236321Z >>> ) 2024-08-06T21:24:02.6236557Z >>> ddp(inputs).sum().backward() 2024-08-06T21:24:02.6236863Z >>> opt.step() 2024-08-06T21:24:02.6237003Z 2024-08-06T21:24:02.6237094Z .. warning:: 2024-08-06T21:24:02.6237436Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-08-06T21:24:02.6237909Z passed-in parameters are the same dense type. 2024-08-06T21:24:02.6238164Z 2024-08-06T21:24:02.6238252Z .. warning:: 2024-08-06T21:24:02.6238594Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-08-06T21:24:02.6239131Z the way that overlapping :class:`DistributedDataParallel` with 2024-08-06T21:24:02.6239672Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-08-06T21:24:02.6240246Z two or three training iterations do not perform parameter updates in 2024-08-06T21:24:02.6240864Z the optimizer step, depending on if ``static_graph=False`` or 2024-08-06T21:24:02.6241359Z ``static_graph=True``, respectively. This is because it needs 2024-08-06T21:24:02.6241831Z information about the gradient bucketing strategy used by 2024-08-06T21:24:02.6242350Z :class:`DistributedDataParallel`, which is not finalized until the 2024-08-06T21:24:02.6243077Z second forward pass if ``static_graph=False`` or until the third 2024-08-06T21:24:02.6243592Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-08-06T21:24:02.6244033Z is to prepend dummy inputs. 2024-08-06T21:24:02.6244218Z 2024-08-06T21:24:02.6244488Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-08-06T21:24:02.6244860Z 2024-08-06T21:24:02.6244996Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-08-06T21:24:02.6245239Z 2024-08-06T21:24:02.6245243Z 2024-08-06T21:24:02.6245494Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.6245859Z 2024-08-06T21:24:02.6624412Z 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. 2024-08-06T21:24:02.6625503Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.6625881Z 2024-08-06T21:24:02.6626133Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-08-06T21:24:02.6626626Z Needs to be called on all ranks in an SPMD fashion. 2024-08-06T21:24:02.6626987Z 2024-08-06T21:24:02.6627073Z Args: 2024-08-06T21:24:02.6627466Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-08-06T21:24:02.6628026Z of shards that represent the local shards on this rank. 2024-08-06T21:24:02.6628538Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-08-06T21:24:02.6629162Z shape of the overall sharded tensor. 2024-08-06T21:24:02.6629385Z 2024-08-06T21:24:02.6629481Z Keyword args: 2024-08-06T21:24:02.6629885Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-08-06T21:24:02.6630397Z the default process group will be used. 2024-08-06T21:24:02.6630797Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-06T21:24:02.6631298Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-06T21:24:02.6631820Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-06T21:24:02.6632219Z Default: ``False``. 2024-08-06T21:24:02.6632402Z 2024-08-06T21:24:02.6632487Z Returns: 2024-08-06T21:24:02.6632765Z A :class:`ShardedTensor` object handle on this rank 2024-08-06T21:24:02.6633081Z 2024-08-06T21:24:02.6633085Z 2024-08-06T21:24:02.6633174Z Examples: 2024-08-06T21:24:02.6633555Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-08-06T21:24:02.6634124Z each shard have a (5, 5) local tensor, we can do it like below: 2024-08-06T21:24:02.6634416Z 2024-08-06T21:24:02.6634518Z on rank 0: 2024-08-06T21:24:02.6634763Z >>> # xdoctest: +SKIP("not distributed") 2024-08-06T21:24:02.6635122Z >>> local_shard_metadata = ShardMetadata( 2024-08-06T21:24:02.6635448Z >>> shard_offsets=[0, 0], 2024-08-06T21:24:02.6635745Z >>> shard_lengths=[5, 5], 2024-08-06T21:24:02.6636050Z >>> placement="rank:0/cuda:0" 2024-08-06T21:24:02.6636336Z >>> ) 2024-08-06T21:24:02.6636651Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-06T21:24:02.6637161Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-06T21:24:02.6637468Z 2024-08-06T21:24:02.6637555Z on rank 1: 2024-08-06T21:24:02.6637808Z >>> # xdoctest: +SKIP("not distributed") 2024-08-06T21:24:02.6638159Z >>> local_shard_metadata = ShardMetadata( 2024-08-06T21:24:02.6638482Z >>> shard_offsets=[5, 0], 2024-08-06T21:24:02.6638780Z >>> shard_lengths=[5, 5], 2024-08-06T21:24:02.6639171Z >>> placement="rank:1/cuda:1" 2024-08-06T21:24:02.6639456Z >>> ) 2024-08-06T21:24:02.6639772Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-06T21:24:02.6640279Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-06T21:24:02.6640584Z 2024-08-06T21:24:02.6640850Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.6641214Z 2024-08-06T21:24:02.6726797Z 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. 2024-08-06T21:24:02.6728052Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.6728431Z 2024-08-06T21:24:02.6728765Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-08-06T21:24:02.6729251Z size and sharding spec on each rank. 2024-08-06T21:24:02.6729542Z 2024-08-06T21:24:02.6729626Z Args: 2024-08-06T21:24:02.6729971Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-08-06T21:24:02.6730639Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-08-06T21:24:02.6731241Z The specification describing how to shard the Tensor. 2024-08-06T21:24:02.6731749Z global_size (Sequence[int]): Size of the sharded tensor. 2024-08-06T21:24:02.6732294Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-08-06T21:24:02.6732860Z Default: None 2024-08-06T21:24:02.6733360Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-06T21:24:02.6734043Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-06T21:24:02.6734620Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-06T21:24:02.6735234Z Default: ``False``. 2024-08-06T21:24:02.6735405Z 2024-08-06T21:24:02.6735503Z Returns: 2024-08-06T21:24:02.6735865Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-08-06T21:24:02.6736349Z tensor stored in the current rank. 2024-08-06T21:24:02.6736569Z 2024-08-06T21:24:02.6736669Z Examples: 2024-08-06T21:24:02.6736880Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.6737189Z >>> # All tensors below are of torch.int64 type. 2024-08-06T21:24:02.6737555Z >>> # We have 2 process groups, 2 ranks. 2024-08-06T21:24:02.6737950Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-08-06T21:24:02.6738448Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-08-06T21:24:02.6738865Z >>> local_tensor 2024-08-06T21:24:02.6739170Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-08-06T21:24:02.6739473Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-08-06T21:24:02.6739776Z >>> sharding_dim = 0 2024-08-06T21:24:02.6740058Z >>> sharding_spec = ChunkShardingSpec( 2024-08-06T21:24:02.6740395Z dim=sharding_dim, 2024-08-06T21:24:02.6740684Z placements=[ 2024-08-06T21:24:02.6740942Z "rank:0/cuda:0", 2024-08-06T21:24:02.6741230Z "rank:1/cuda:1", 2024-08-06T21:24:02.6741506Z ], 2024-08-06T21:24:02.6741715Z ) 2024-08-06T21:24:02.6742100Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-08-06T21:24:02.6742766Z >>> st 2024-08-06T21:24:02.6742976Z ShardedTensor( 2024-08-06T21:24:02.6743235Z ShardedTensorMetadata( 2024-08-06T21:24:02.6743533Z shards_metadata=[ 2024-08-06T21:24:02.6743975Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-08-06T21:24:02.6744633Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-08-06T21:24:02.6745100Z ], 2024-08-06T21:24:02.6745324Z size=torch.Size([2, 4]) 2024-08-06T21:24:02.6745611Z ) 2024-08-06T21:24:02.6745933Z >>> st.local_tensor() 2024-08-06T21:24:02.6746191Z tensor([1, 2, 3, 4]) # Rank 0 2024-08-06T21:24:02.6746482Z tensor([3, 4, 5, 6]) # Rank 1 2024-08-06T21:24:02.6746667Z 2024-08-06T21:24:02.6747032Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-08-06T21:24:02.6747652Z rank validations, and we only validate the local shard on the current rank. 2024-08-06T21:24:02.6748231Z We fully rely on the user to ensure local tensor is sharded based on the 2024-08-06T21:24:02.6748667Z sharding spec. 2024-08-06T21:24:02.6748821Z 2024-08-06T21:24:02.6749073Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.6749458Z 2024-08-06T21:24:02.6750166Z 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. 2024-08-06T21:24:02.6751236Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.6751624Z 2024-08-06T21:24:02.6751875Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-08-06T21:24:02.6752348Z single local shard. 2024-08-06T21:24:02.6752493Z 2024-08-06T21:24:02.6752717Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-08-06T21:24:02.6753301Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-08-06T21:24:02.6753772Z we swap local shards directly. 2024-08-06T21:24:02.6754214Z For more generic cases, we merge different shards across different ranks and split 2024-08-06T21:24:02.6754852Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-08-06T21:24:02.6755221Z 2024-08-06T21:24:02.6755315Z Args: 2024-08-06T21:24:02.6755710Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-08-06T21:24:02.6756344Z specification describing how the tensor is sharded. 2024-08-06T21:24:02.6756618Z 2024-08-06T21:24:02.6756719Z Returns: 2024-08-06T21:24:02.6757034Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-08-06T21:24:02.6757365Z 2024-08-06T21:24:02.6757454Z Examples: 2024-08-06T21:24:02.6757686Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.6757967Z >>> # We have 2 process groups, 2 ranks. 2024-08-06T21:24:02.6758373Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-08-06T21:24:02.6758784Z >>> tensor = torch.stack([tensor, tensor]) 2024-08-06T21:24:02.6759094Z >>> tensor 2024-08-06T21:24:02.6759345Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-08-06T21:24:02.6759697Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-08-06T21:24:02.6760071Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-08-06T21:24:02.6760422Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-08-06T21:24:02.6760759Z >>> sharding_dim = 0 2024-08-06T21:24:02.6761023Z >>> spec = ChunkShardingSpec( 2024-08-06T21:24:02.6761339Z dim=sharding_dim, 2024-08-06T21:24:02.6761625Z placements=[ 2024-08-06T21:24:02.6761878Z "rank:0/cuda:0", 2024-08-06T21:24:02.6762165Z "rank:1/cuda:1", 2024-08-06T21:24:02.6762446Z "rank:2/cuda:2", 2024-08-06T21:24:02.6762713Z "rank:3/cuda:3", 2024-08-06T21:24:02.6762984Z ], 2024-08-06T21:24:02.6763204Z ) 2024-08-06T21:24:02.6763417Z >>> current_offsets = [0] * 2 2024-08-06T21:24:02.6763719Z >>> current_offsets[0] = rank * 2 2024-08-06T21:24:02.6764045Z >>> shard_metadata = ShardMetadata( 2024-08-06T21:24:02.6764400Z shard_offsets=copy.deepcopy(current_offsets), 2024-08-06T21:24:02.6764772Z shard_sizes=tensor.size(), 2024-08-06T21:24:02.6765109Z placement=spec.placements[rank], 2024-08-06T21:24:02.6765416Z ) 2024-08-06T21:24:02.6765632Z >>> local_shards = [ 2024-08-06T21:24:02.6765887Z Shard( 2024-08-06T21:24:02.6766177Z tensor=tensor, 2024-08-06T21:24:02.6766466Z metadata=shard_metadata, 2024-08-06T21:24:02.6766755Z ) 2024-08-06T21:24:02.6766968Z ] 2024-08-06T21:24:02.6767320Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-08-06T21:24:02.6767754Z >>> sharding_dim = 1 2024-08-06T21:24:02.6768049Z >>> resharding_spec = ChunkShardingSpec( 2024-08-06T21:24:02.6768387Z dim=sharding_dim, 2024-08-06T21:24:02.6768658Z placements=[ 2024-08-06T21:24:02.6768926Z "rank:0/cuda:0", 2024-08-06T21:24:02.6769210Z "rank:1/cuda:1", 2024-08-06T21:24:02.6769480Z "rank:2/cuda:2", 2024-08-06T21:24:02.6769762Z "rank:3/cuda:3", 2024-08-06T21:24:02.6770031Z ], 2024-08-06T21:24:02.6770236Z ) 2024-08-06T21:24:02.6770467Z >>> st.reshard(resharding_spec) 2024-08-06T21:24:02.6770790Z >>> tensor = st.local_shards()[0].tensor 2024-08-06T21:24:02.6771089Z >>> tensor 2024-08-06T21:24:02.6771367Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-08-06T21:24:02.6771774Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-08-06T21:24:02.6772154Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-08-06T21:24:02.6772548Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-08-06T21:24:02.6772801Z 2024-08-06T21:24:02.6773069Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.6773440Z 2024-08-06T21:24:02.6873512Z 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. 2024-08-06T21:24:02.6874564Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.6875114Z 2024-08-06T21:24:02.6875338Z Representation of a sharding plan, describes how to shard a module 2024-08-06T21:24:02.6875964Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-08-06T21:24:02.6876643Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-08-06T21:24:02.6877298Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-08-06T21:24:02.6877658Z 2024-08-06T21:24:02.6877743Z Args: 2024-08-06T21:24:02.6878132Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-08-06T21:24:02.6878691Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-08-06T21:24:02.6879232Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-08-06T21:24:02.6879941Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-08-06T21:24:02.6880444Z a parameter to a `ShardingSpec`. 2024-08-06T21:24:02.6880930Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-08-06T21:24:02.6881415Z to a `Sharder` object. 2024-08-06T21:24:02.6881959Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-08-06T21:24:02.6882659Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-08-06T21:24:02.6883292Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-08-06T21:24:02.6883750Z Default: `None` 2024-08-06T21:24:02.6884172Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-08-06T21:24:02.6884778Z a module's sharded output to be returned as a Tensor from its local shards to 2024-08-06T21:24:02.6885397Z ensure further processing in a data parallel fashion. ("" in list means the 2024-08-06T21:24:02.6885867Z root module). 2024-08-06T21:24:02.6886106Z Default: None 2024-08-06T21:24:02.6886355Z Example: 2024-08-06T21:24:02.6886851Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-08-06T21:24:02.6887534Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-08-06T21:24:02.6887946Z 2024-08-06T21:24:02.6888118Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-08-06T21:24:02.6888518Z >>> class MyModule(nn.Module): 2024-08-06T21:24:02.6888826Z >>> def __init__(self) -> None: 2024-08-06T21:24:02.6889141Z >>> super().__init__() 2024-08-06T21:24:02.6889441Z >>> self.fc1 = nn.Linear() 2024-08-06T21:24:02.6889735Z >>> self.gelu = nn.GELU() 2024-08-06T21:24:02.6890043Z >>> self.fc2 = nn.Linear() 2024-08-06T21:24:02.6890354Z >>> self.relu = nn.Linear() 2024-08-06T21:24:02.6890636Z >>> 2024-08-06T21:24:02.6890862Z >>> def forward(self, input): 2024-08-06T21:24:02.6891251Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-08-06T21:24:02.6891542Z 2024-08-06T21:24:02.6891547Z 2024-08-06T21:24:02.6891681Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-08-06T21:24:02.6892040Z >>> sharding_plan = ShardingPlan( 2024-08-06T21:24:02.6892345Z >>> plan={ 2024-08-06T21:24:02.6892577Z >>> "fc1.weight": spec1, 2024-08-06T21:24:02.6892881Z >>> "fc2.weight": spec2 2024-08-06T21:24:02.6893166Z >>> }, 2024-08-06T21:24:02.6893381Z >>> output_plan={ 2024-08-06T21:24:02.6893652Z >>> "fc2": output_spec 2024-08-06T21:24:02.6893931Z >>> }, 2024-08-06T21:24:02.6894155Z >>> return_local_tensor=["fc2"] 2024-08-06T21:24:02.6894454Z >>> ) 2024-08-06T21:24:02.6894569Z 2024-08-06T21:24:02.6894833Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.6895203Z 2024-08-06T21:24:02.7489919Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/local_map.py line=30. 2024-08-06T21:24:02.7491253Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.7491646Z 2024-08-06T21:24:02.7491907Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-08-06T21:24:02.7492504Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-08-06T21:24:02.7492829Z 2024-08-06T21:24:02.7492915Z Args: 2024-08-06T21:24:02.7493244Z func (Callable): the function to be applied on each local shard of 2024-08-06T21:24:02.7493667Z :class:`DTensor`s. 2024-08-06T21:24:02.7494060Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-08-06T21:24:02.7494658Z the desired placements of the :class:`DTensor`s in `func`'s flattened output. 2024-08-06T21:24:02.7495340Z If the flattened `output` is a single value, the `out_placements` should be 2024-08-06T21:24:02.7495929Z of type `PlacementType`. Otherwise if the flattened `output` has multiple 2024-08-06T21:24:02.7496547Z values, the `out_placements` should be a tuple of `PlacementType` values 1:1 2024-08-06T21:24:02.7497041Z mapping to the flattened `output`. 2024-08-06T21:24:02.7497460Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-08-06T21:24:02.7498015Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-08-06T21:24:02.7498487Z the `PlacementType` should be `None`. 2024-08-06T21:24:02.7498947Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-08-06T21:24:02.7499534Z in. In this case, even if `out_placements` is not `None`, the result function 2024-08-06T21:24:02.7500111Z should ignore the desired placements because the application is not on 2024-08-06T21:24:02.7500566Z :class:`DTensors`. 2024-08-06T21:24:02.7501022Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-08-06T21:24:02.7501651Z the required placements of the :class:`DTensor`s in `func`'s flattened input. 2024-08-06T21:24:02.7502847Z If `in_placements` is specified, `local_map` would examine whether the 2024-08-06T21:24:02.7503421Z placements of each :class:`DTensor` argument is the same as the required 2024-08-06T21:24:02.7503956Z placements or not. If the placements are not the same and 2024-08-06T21:24:02.7504497Z `redistribute_inputs` is `False`, an exception will be raised. Otherwise if 2024-08-06T21:24:02.7505092Z `redistribute_inputs` is `True`, the argument will be first redistributed to 2024-08-06T21:24:02.7505713Z the required sharding placements before passing its local tensor to `func`. 2024-08-06T21:24:02.7506346Z The only exception is when required placements are not `None` and the 2024-08-06T21:24:02.7507002Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-08-06T21:24:02.7507575Z will be skipped and the argument will be directly passed to `func`. 2024-08-06T21:24:02.7508136Z If `in_placements` is `None`, no placements examination will be performed. 2024-08-06T21:24:02.7508583Z Default: `None` 2024-08-06T21:24:02.7508876Z device_mesh (:class:`DeviceMesh`, optional): 2024-08-06T21:24:02.7509340Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-08-06T21:24:02.7509905Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-08-06T21:24:02.7510482Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-08-06T21:24:02.7510953Z device mesh. Default: `None`. 2024-08-06T21:24:02.7511287Z redistribute_inputs (bool, optional): 2024-08-06T21:24:02.7511759Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-08-06T21:24:02.7512389Z their placements are different from the required input placements. If this 2024-08-06T21:24:02.7513026Z value is `False` and some :class:`DTensor` input has a different placement, 2024-08-06T21:24:02.7528324Z an exception will be raised. Default: `False`. 2024-08-06T21:24:02.7528615Z 2024-08-06T21:24:02.7528708Z Returns: 2024-08-06T21:24:02.7529103Z A `Callable` that applies `func` to each local shard of the input :class:`DTensor` 2024-08-06T21:24:02.7529731Z and returns a :class:`DTensor` constructed from the return value of `func`. 2024-08-06T21:24:02.7530085Z 2024-08-06T21:24:02.7530177Z Raises: 2024-08-06T21:24:02.7530573Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-08-06T21:24:02.7531241Z mesh, or if they are placed on a different device mesh than the `device_mesh` 2024-08-06T21:24:02.7531694Z argument passed in. 2024-08-06T21:24:02.7531973Z 2024-08-06T21:24:02.7532247Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-08-06T21:24:02.7532936Z output placement in `out_placements` be `None`. An AssertionError will be raised 2024-08-06T21:24:02.7533417Z if this is not the case. 2024-08-06T21:24:02.7533622Z 2024-08-06T21:24:02.7533887Z ValueError: If `redistribute_inputs=False` but the input :class:`DTensor` needs 2024-08-06T21:24:02.7534428Z a redistribution according to `in_placements`. 2024-08-06T21:24:02.7534687Z 2024-08-06T21:24:02.7534795Z Example: 2024-08-06T21:24:02.7535026Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:02.7535414Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-08-06T21:24:02.7535800Z >>> partial_sum_tensor = torch.mm(W, X) 2024-08-06T21:24:02.7536271Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-08-06T21:24:02.7536750Z >>> return reduced_tensor 2024-08-06T21:24:02.7537040Z >>> 2024-08-06T21:24:02.7537280Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-08-06T21:24:02.7537660Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-08-06T21:24:02.7538005Z >>> Y = torch.mm(W, X) 2024-08-06T21:24:02.7538438Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-08-06T21:24:02.7538944Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-08-06T21:24:02.7539345Z >>> 2024-08-06T21:24:02.7539734Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-08-06T21:24:02.7540267Z >>> local_mm_allreduce_forward = local_map( 2024-08-06T21:24:02.7540618Z >>> mm_allreduce_forward, 2024-08-06T21:24:02.7540930Z >>> out_placements=[Replicate()], 2024-08-06T21:24:02.7541290Z >>> in_placements=[col_wise, row_wise], 2024-08-06T21:24:02.7541662Z >>> device_mesh=device_mesh, 2024-08-06T21:24:02.7541993Z >>> ) 2024-08-06T21:24:02.7542211Z >>> 2024-08-06T21:24:02.7542770Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-08-06T21:24:02.7543425Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-08-06T21:24:02.7544150Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-08-06T21:24:02.7544598Z 2024-08-06T21:24:02.7544804Z NOTE: This API is currently experimental and subject to change 2024-08-06T21:24:02.7545104Z 2024-08-06T21:24:02.7545359Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.7545743Z 2024-08-06T21:24:02.7546539Z 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=22. 2024-08-06T21:24:02.7553352Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.7553774Z 2024-08-06T21:24:02.7554048Z ``register_sharding`` is an experimental API that allows users to register sharding 2024-08-06T21:24:02.7554707Z strategies for an operator when the tensor inputs and outputs are :class:`DTensor`s. 2024-08-06T21:24:02.7555534Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-08-06T21:24:02.7556155Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-08-06T21:24:02.7556773Z when users would like to overwrite default sharding strategies of existing operators. 2024-08-06T21:24:02.7557164Z 2024-08-06T21:24:02.7557244Z Args: 2024-08-06T21:24:02.7557473Z op (Union[OpOverload, List[OpOverload]]): 2024-08-06T21:24:02.7557889Z An op or a list of ops to register the customized sharding function. 2024-08-06T21:24:02.7578851Z 2024-08-06T21:24:02.7579019Z Returns: 2024-08-06T21:24:02.7579426Z A function decorator which can be used to wrap a function that defines the sharding 2024-08-06T21:24:02.7580159Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-08-06T21:24:02.7580812Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-08-06T21:24:02.7581511Z already implemented the operator. The customized sharding function takes the same inputs 2024-08-06T21:24:02.7582159Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-08-06T21:24:02.7582776Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-08-06T21:24:02.7583427Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-08-06T21:24:02.7583929Z corresponding intput placements. 2024-08-06T21:24:02.7584142Z 2024-08-06T21:24:02.7584221Z Example: 2024-08-06T21:24:02.7584439Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:02.7584772Z >>> @register_sharding(aten._softmax.default) 2024-08-06T21:24:02.7585159Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-08-06T21:24:02.7585561Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-08-06T21:24:02.7585923Z >>> acceptable_shardings = [] 2024-08-06T21:24:02.7586203Z >>> 2024-08-06T21:24:02.7586580Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-08-06T21:24:02.7587073Z >>> acceptable_shardings.append(all_replicate) 2024-08-06T21:24:02.7587399Z >>> 2024-08-06T21:24:02.7587629Z >>> for sharding_dim in range(x.ndim): 2024-08-06T21:24:02.7587961Z >>> if sharding_dim != softmax_dim: 2024-08-06T21:24:02.7588270Z >>> all_sharded = ( 2024-08-06T21:24:02.7588569Z >>> [Shard(sharding_dim)], 2024-08-06T21:24:02.7588900Z >>> [Shard(sharding_dim), None, None], 2024-08-06T21:24:02.7589212Z >>> ) 2024-08-06T21:24:02.7589493Z >>> acceptable_shardings.append(all_sharded) 2024-08-06T21:24:02.7589819Z >>> 2024-08-06T21:24:02.7590039Z >>> return acceptable_shardings 2024-08-06T21:24:02.7590249Z 2024-08-06T21:24:02.7590500Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.7590868Z 2024-08-06T21:24:02.9149604Z 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. 2024-08-06T21:24:02.9150758Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9151148Z 2024-08-06T21:24:02.9151264Z Run post-localSGD algorithm. 2024-08-06T21:24:02.9151464Z 2024-08-06T21:24:02.9151699Z This DDP communication hook is used for running post-localSGD algorithm, 2024-08-06T21:24:02.9152212Z by combining with a model averaging component (e.g., 2024-08-06T21:24:02.9152806Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-08-06T21:24:02.9153368Z that runs after the optimizer step. 2024-08-06T21:24:02.9153601Z 2024-08-06T21:24:02.9153684Z Args: 2024-08-06T21:24:02.9154030Z state (PostLocalSGDState): State information to run post-localSGD. 2024-08-06T21:24:02.9154868Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-08-06T21:24:02.9155697Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-06T21:24:02.9156492Z Note that since DDP comm hook only supports single process single device mode, 2024-08-06T21:24:02.9157014Z only exactly one tensor is stored in this bucket. 2024-08-06T21:24:02.9157275Z 2024-08-06T21:24:02.9157364Z Returns: 2024-08-06T21:24:02.9157734Z Future handler of the communication, which updates the gradients in place. 2024-08-06T21:24:02.9158091Z 2024-08-06T21:24:02.9158210Z Example:: 2024-08-06T21:24:02.9158424Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.9158860Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-08-06T21:24:02.9159422Z start_localSGD_iter=10) 2024-08-06T21:24:02.9159825Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-08-06T21:24:02.9160459Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-08-06T21:24:02.9161268Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-08-06T21:24:02.9161730Z 2024-08-06T21:24:02.9161982Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9162361Z 2024-08-06T21:24:02.9197640Z 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=342. 2024-08-06T21:24:02.9198729Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9199107Z 2024-08-06T21:24:02.9199237Z Implement PowerSGD algorithm. 2024-08-06T21:24:02.9199430Z 2024-08-06T21:24:02.9199657Z This DDP communication hook implements PowerSGD gradient compression 2024-08-06T21:24:02.9200259Z algorithm described in the `paper `_. 2024-08-06T21:24:02.9201000Z Once gradient tensors are aggregated across all workers, this hook applies 2024-08-06T21:24:02.9201459Z compression as follows: 2024-08-06T21:24:02.9201638Z 2024-08-06T21:24:02.9202081Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-08-06T21:24:02.9202643Z 2024-08-06T21:24:02.9203085Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-08-06T21:24:02.9203629Z 2024-08-06T21:24:02.9204041Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-08-06T21:24:02.9204563Z 2024-08-06T21:24:02.9204691Z 2. Handles uncompressed tensors: 2024-08-06T21:24:02.9204888Z 2024-08-06T21:24:02.9205409Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-08-06T21:24:02.9206055Z 2024-08-06T21:24:02.9206402Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-08-06T21:24:02.9206860Z 2024-08-06T21:24:02.9207105Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-08-06T21:24:02.9207449Z 2024-08-06T21:24:02.9207707Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-08-06T21:24:02.9208382Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-08-06T21:24:02.9208818Z 2024-08-06T21:24:02.9208963Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-08-06T21:24:02.9209217Z 2024-08-06T21:24:02.9209341Z 3.3. Allreduces Ps as a batch; 2024-08-06T21:24:02.9209540Z 2024-08-06T21:24:02.9209657Z 3.4. Orthogonalizes each P in Ps; 2024-08-06T21:24:02.9209882Z 2024-08-06T21:24:02.9210120Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-08-06T21:24:02.9210434Z 2024-08-06T21:24:02.9210544Z 3.6. Allreduces Qs as a batch; 2024-08-06T21:24:02.9210759Z 2024-08-06T21:24:02.9211058Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-08-06T21:24:02.9211479Z 2024-08-06T21:24:02.9211890Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-08-06T21:24:02.9212707Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-08-06T21:24:02.9213535Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-08-06T21:24:02.9214095Z 2024-08-06T21:24:02.9214180Z Args: 2024-08-06T21:24:02.9215131Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-08-06T21:24:02.9216039Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-08-06T21:24:02.9216632Z and ``min_compression_rate``. 2024-08-06T21:24:02.9217270Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-06T21:24:02.9218054Z Note that since DDP comm hook only supports single process single device mode, 2024-08-06T21:24:02.9218562Z only exactly one tensor is stored in this bucket. 2024-08-06T21:24:02.9218832Z 2024-08-06T21:24:02.9218918Z Returns: 2024-08-06T21:24:02.9219282Z Future handler of the communication, which updates the gradients in place. 2024-08-06T21:24:02.9219641Z 2024-08-06T21:24:02.9219737Z Example:: 2024-08-06T21:24:02.9219963Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.9220416Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-08-06T21:24:02.9220962Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-08-06T21:24:02.9221413Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-08-06T21:24:02.9221679Z 2024-08-06T21:24:02.9222041Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9222407Z 2024-08-06T21:24:02.9236774Z 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=36. 2024-08-06T21:24:02.9237895Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9238285Z 2024-08-06T21:24:02.9238471Z Averages parameters periodically after the warm-up stage. 2024-08-06T21:24:02.9238765Z 2024-08-06T21:24:02.9239038Z This can be used for running `post-local SGD `_, 2024-08-06T21:24:02.9239611Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-08-06T21:24:02.9240143Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-08-06T21:24:02.9240509Z 2024-08-06T21:24:02.9240593Z Args: 2024-08-06T21:24:02.9240882Z period (int): The number of steps per model averaging. 2024-08-06T21:24:02.9241419Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-08-06T21:24:02.9241937Z Otherwise, only DDP needs to be used. 2024-08-06T21:24:02.9242578Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-08-06T21:24:02.9243026Z model averaging is skipped. 2024-08-06T21:24:02.9243452Z process_group: The process group to be used for all-reduce. 2024-08-06T21:24:02.9243902Z If ``None``, the default process group, which 2024-08-06T21:24:02.9244345Z is created by :func:`torch.distributed.init_process_group`, 2024-08-06T21:24:02.9244784Z will be used. (default: ``None``) 2024-08-06T21:24:02.9245014Z 2024-08-06T21:24:02.9245241Z Example:: 2024-08-06T21:24:02.9245363Z 2024-08-06T21:24:02.9245488Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:02.9245823Z >>> import torch 2024-08-06T21:24:02.9246104Z >>> import torch.distributed as dist 2024-08-06T21:24:02.9246638Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-08-06T21:24:02.9247343Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-06T21:24:02.9247834Z >>> import torch.nn as nn 2024-08-06T21:24:02.9248095Z >>> 2024-08-06T21:24:02.9248391Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-06T21:24:02.9248791Z >>> torch.cuda.set_device(rank) 2024-08-06T21:24:02.9249117Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-08-06T21:24:02.9249517Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-06T21:24:02.9249979Z >>> module, device_ids=[rank], output_device=rank 2024-08-06T21:24:02.9250320Z >>> ) 2024-08-06T21:24:02.9250593Z >>> # Register a post-localSGD communication hook. 2024-08-06T21:24:02.9251160Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-06T21:24:02.9251903Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-06T21:24:02.9252326Z >>> 2024-08-06T21:24:02.9252717Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-06T21:24:02.9253416Z >>> # After 100 steps, run model averaging every 4 steps. 2024-08-06T21:24:02.9254484Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-06T21:24:02.9255185Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-06T21:24:02.9255660Z >>> for step in range(0, 200): 2024-08-06T21:24:02.9255965Z >>> optimizer.zero_grad() 2024-08-06T21:24:02.9256274Z >>> loss = loss_fn(output, labels) 2024-08-06T21:24:02.9256601Z >>> loss.backward() 2024-08-06T21:24:02.9256867Z >>> optimizer.step() 2024-08-06T21:24:02.9257364Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-08-06T21:24:02.9257886Z >>> # inter-node communication only occurs every 4 iterations after 2024-08-06T21:24:02.9258324Z >>> # the initial ``warmup_steps`` period. 2024-08-06T21:24:02.9258724Z >>> averager.average_parameters(model.parameters()) 2024-08-06T21:24:02.9258993Z 2024-08-06T21:24:02.9259257Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9259619Z 2024-08-06T21:24:02.9260515Z 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. 2024-08-06T21:24:02.9261725Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9262114Z 2024-08-06T21:24:02.9262449Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-08-06T21:24:02.9262911Z 2024-08-06T21:24:02.9263223Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-08-06T21:24:02.9263857Z by using different periods concurrently after the warm-up stage. 2024-08-06T21:24:02.9264568Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-08-06T21:24:02.9265430Z that supports `post-local SGD `_, which essentially only supports 2024-08-06T21:24:02.9266195Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-08-06T21:24:02.9267039Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-08-06T21:24:02.9267825Z Similarly, the process groups within this class do not have such an intra-machine process 2024-08-06T21:24:02.9268528Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-08-06T21:24:02.9268958Z 2024-08-06T21:24:02.9269061Z Args: 2024-08-06T21:24:02.9269437Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-08-06T21:24:02.9270026Z process group size, used for initializing process groups of 2024-08-06T21:24:02.9270577Z different sizes in a hierarchy to average parameters concurrently. 2024-08-06T21:24:02.9271128Z Particularly, at each iteration, there will be at most a single 2024-08-06T21:24:02.9271703Z process group that runs averaging -- the period of such group should 2024-08-06T21:24:02.9272271Z have the largest period which the current step can be divided by. 2024-08-06T21:24:02.9272796Z For example, if the dict has three keys: 2, 4, and 8, 2024-08-06T21:24:02.9273314Z then this means totally three process groups will be created to 2024-08-06T21:24:02.9273853Z average parameters every 2, 4, and 8 iterations, respectively. 2024-08-06T21:24:02.9274381Z At the 4th iteration, only the second process group will run 2024-08-06T21:24:02.9274875Z averaging, because the first process group should be a 2024-08-06T21:24:02.9275391Z subset of the second process group, and no need to execute the first 2024-08-06T21:24:02.9275860Z process group redundantly. 2024-08-06T21:24:02.9276300Z On the other hand, the third process group can only be triggered 2024-08-06T21:24:02.9276853Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-08-06T21:24:02.9277496Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-08-06T21:24:02.9278374Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-08-06T21:24:02.9279216Z If ``None``, the default process group, which is created 2024-08-06T21:24:02.9279713Z by :func:`torch.distributed.init_process_group`, will be used. 2024-08-06T21:24:02.9280168Z (default: ``None``) 2024-08-06T21:24:02.9280402Z 2024-08-06T21:24:02.9280513Z Example:: 2024-08-06T21:24:02.9280746Z >>> # xdoctest: +SKIP('undefined rank') 2024-08-06T21:24:02.9281097Z >>> from collections import OrderedDict 2024-08-06T21:24:02.9281423Z >>> import torch 2024-08-06T21:24:02.9281686Z >>> import torch.distributed as dist 2024-08-06T21:24:02.9282191Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-06T21:24:02.9282696Z >>> PostLocalSGDState, 2024-08-06T21:24:02.9282983Z >>> post_localSGD_hook, 2024-08-06T21:24:02.9283258Z >>> ) 2024-08-06T21:24:02.9283766Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-08-06T21:24:02.9284350Z >>> import torch.nn as nn 2024-08-06T21:24:02.9284622Z >>> 2024-08-06T21:24:02.9284917Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-06T21:24:02.9285307Z >>> torch.cuda.set_device(rank) 2024-08-06T21:24:02.9285651Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-08-06T21:24:02.9286051Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-06T21:24:02.9286444Z >>> module, device_ids=[rank], output_device=rank 2024-08-06T21:24:02.9286780Z >>> ) 2024-08-06T21:24:02.9287046Z >>> # Register a post-localSGD communication hook. 2024-08-06T21:24:02.9287570Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-08-06T21:24:02.9288083Z >>> subgroup, _ = dist.new_subgroups() 2024-08-06T21:24:02.9288665Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-08-06T21:24:02.9289250Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-06T21:24:02.9289616Z >>> 2024-08-06T21:24:02.9290027Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-08-06T21:24:02.9290538Z >>> # the 16 processes every 16 iterations. 2024-08-06T21:24:02.9290957Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-08-06T21:24:02.9291490Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-08-06T21:24:02.9292162Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-06T21:24:02.9292850Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-06T21:24:02.9293423Z >>> # After 100 steps, run model averaging at two levels. 2024-08-06T21:24:02.9293799Z >>> for step in range(0, 200): 2024-08-06T21:24:02.9294097Z >>> optimizer.zero_grad() 2024-08-06T21:24:02.9294406Z >>> loss = loss_fn(output, labels) 2024-08-06T21:24:02.9294733Z >>> loss.backward() 2024-08-06T21:24:02.9294999Z >>> optimizer.step() 2024-08-06T21:24:02.9295337Z >>> # Average parameters after ``optimizer.step()``. 2024-08-06T21:24:02.9295894Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-08-06T21:24:02.9296447Z >>> averager.average_parameters(model.parameters()) 2024-08-06T21:24:02.9296732Z 2024-08-06T21:24:02.9296824Z .. warning :: 2024-08-06T21:24:02.9297210Z The last group size in the dict must be the size of the provided ``process_group``, 2024-08-06T21:24:02.9297807Z which indicates model averaging at the highest level of the hierarchy. 2024-08-06T21:24:02.9298464Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-08-06T21:24:02.9298882Z 2024-08-06T21:24:02.9298985Z .. warning :: 2024-08-06T21:24:02.9299405Z `HierarchicalModelAverager` is experimental and subject to change. 2024-08-06T21:24:02.9299759Z 2024-08-06T21:24:02.9300009Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9300383Z 2024-08-06T21:24:02.9449826Z 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. 2024-08-06T21:24:02.9450921Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9451301Z 2024-08-06T21:24:02.9451589Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-08-06T21:24:02.9452254Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-08-06T21:24:02.9452743Z 2024-08-06T21:24:02.9452904Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-08-06T21:24:02.9453174Z 2024-08-06T21:24:02.9453281Z .. warning:: 2024-08-06T21:24:02.9453655Z Current implementation only supports loading Tensors. 2024-08-06T21:24:02.9453962Z 2024-08-06T21:24:02.9454075Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:02.9454389Z >>> sd = {"mode": model} 2024-08-06T21:24:02.9454632Z >>> dcp.load( 2024-08-06T21:24:02.9454852Z >>> sd, 2024-08-06T21:24:02.9455126Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-06T21:24:02.9455502Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-06T21:24:02.9455849Z >>> checkpoint_id="path_to_model.pt" 2024-08-06T21:24:02.9456153Z >>> ) 2024-08-06T21:24:02.9456263Z 2024-08-06T21:24:02.9456513Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9456891Z 2024-08-06T21:24:02.9457577Z 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. 2024-08-06T21:24:02.9458754Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9459127Z 2024-08-06T21:24:02.9459510Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-08-06T21:24:02.9460308Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-08-06T21:24:02.9460868Z metadata file, like Torch Save files. 2024-08-06T21:24:02.9461082Z 2024-08-06T21:24:02.9461273Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-08-06T21:24:02.9461556Z 2024-08-06T21:24:02.9461649Z .. warning:: 2024-08-06T21:24:02.9461960Z Current implementation only supports loading Tensors. 2024-08-06T21:24:02.9462237Z 2024-08-06T21:24:02.9462364Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:02.9462748Z >>> sd = {"mode": model} 2024-08-06T21:24:02.9463007Z >>> dcp.load( 2024-08-06T21:24:02.9463230Z >>> sd, 2024-08-06T21:24:02.9463496Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-06T21:24:02.9463884Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-06T21:24:02.9464235Z >>> checkpoint_id="path_to_model.pt" 2024-08-06T21:24:02.9464527Z >>> ) 2024-08-06T21:24:02.9464648Z 2024-08-06T21:24:02.9464899Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9465265Z 2024-08-06T21:24:02.9505508Z 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. 2024-08-06T21:24:02.9506616Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9507068Z 2024-08-06T21:24:02.9507282Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-08-06T21:24:02.9507636Z 2024-08-06T21:24:02.9507801Z This is the current recommended way to checkpoint FSDP. 2024-08-06T21:24:02.9508184Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.9508493Z >>> import torch.distributed.checkpoint as dist_cp 2024-08-06T21:24:02.9508854Z >>> # Save 2024-08-06T21:24:02.9509094Z >>> model: torch.nn.Model 2024-08-06T21:24:02.9509578Z >>> optim_params = model.parameters() 2024-08-06T21:24:02.9509950Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-08-06T21:24:02.9510301Z >>> # Save 2024-08-06T21:24:02.9510635Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-08-06T21:24:02.9511076Z >>> state_dict = { 2024-08-06T21:24:02.9511396Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-08-06T21:24:02.9511767Z >>> "model": model.state_dict() 2024-08-06T21:24:02.9512071Z >>> } 2024-08-06T21:24:02.9512307Z >>> dist_cp.save_state_dict( 2024-08-06T21:24:02.9512597Z >>> state_dict=optim_state, 2024-08-06T21:24:02.9512987Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-08-06T21:24:02.9513427Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-08-06T21:24:02.9513754Z >>> ) 2024-08-06T21:24:02.9513968Z >>> 2024-08-06T21:24:02.9514173Z >>> # Load 2024-08-06T21:24:02.9514518Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-08-06T21:24:02.9514996Z >>> model_state_dict = model_tp.state_dict() 2024-08-06T21:24:02.9515332Z >>> checkpoint = { 2024-08-06T21:24:02.9515582Z >>> "model": model_state_dict 2024-08-06T21:24:02.9515878Z >>> } 2024-08-06T21:24:02.9516093Z >>> dist_cp.load_state_dict( 2024-08-06T21:24:02.9516393Z >>> state_dict=checkpoint, 2024-08-06T21:24:02.9529759Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-08-06T21:24:02.9530544Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-08-06T21:24:02.9530867Z >>> ) 2024-08-06T21:24:02.9531149Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-08-06T21:24:02.9531511Z >>> 2024-08-06T21:24:02.9531789Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-08-06T21:24:02.9532182Z >>> model_state_dict, 2024-08-06T21:24:02.9532561Z >>> optimizer_key="optimizer", 2024-08-06T21:24:02.9532957Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-08-06T21:24:02.9533340Z >>> ) 2024-08-06T21:24:02.9533548Z >>> 2024-08-06T21:24:02.9533799Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-08-06T21:24:02.9534187Z >>> model, optim, optim_state["optimizer"] 2024-08-06T21:24:02.9534518Z >>> ) 2024-08-06T21:24:02.9534711Z >>> 2024-08-06T21:24:02.9534943Z >>> optim.load_state_dict(flattened_osd) 2024-08-06T21:24:02.9535171Z 2024-08-06T21:24:02.9535435Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9535803Z 2024-08-06T21:24:02.9536410Z 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. 2024-08-06T21:24:02.9537736Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9538276Z 2024-08-06T21:24:02.9538585Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-08-06T21:24:02.9538994Z 2024-08-06T21:24:02.9539299Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-08-06T21:24:02.9539729Z 2024-08-06T21:24:02.9540008Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-06T21:24:02.9540528Z will be visible to the whole process. 2024-08-06T21:24:02.9540741Z 2024-08-06T21:24:02.9541031Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-08-06T21:24:02.9541426Z 2024-08-06T21:24:02.9541548Z 1) set_up_planner - called on all ranks. 2024-08-06T21:24:02.9541902Z Signals the start of a checkpoint save. 2024-08-06T21:24:02.9542129Z 2024-08-06T21:24:02.9542268Z 2) create_local_plan - called on all ranks. 2024-08-06T21:24:02.9543004Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-08-06T21:24:02.9543430Z 2024-08-06T21:24:02.9543613Z 3) create_global_plan - called on the coordinator rank only. 2024-08-06T21:24:02.9544245Z Takes the SavePlan from all ranks and make any global decision. 2024-08-06T21:24:02.9544554Z 2024-08-06T21:24:02.9544682Z 4) finish_plan - called on all ranks. 2024-08-06T21:24:02.9545105Z This gives each rank a chance to adjust to global planning decisions. 2024-08-06T21:24:02.9545450Z 2024-08-06T21:24:02.9545604Z 5) resolve_data - called multiple times on each rank 2024-08-06T21:24:02.9546078Z Lookups a value on the `state_dict` for the storage layer to write. 2024-08-06T21:24:02.9546393Z 2024-08-06T21:24:02.9546695Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-08-06T21:24:02.9547466Z most changes can be expressed by changes in a single method. 2024-08-06T21:24:02.9547808Z 2024-08-06T21:24:02.9547930Z There are 3 usual patterns of extension: 2024-08-06T21:24:02.9548172Z 2024-08-06T21:24:02.9548432Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-08-06T21:24:02.9549041Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-08-06T21:24:02.9549378Z 2024-08-06T21:24:02.9549490Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:02.9549847Z >>> class RenamePlanner(DefaultSavePlanner): 2024-08-06T21:24:02.9550193Z >>> def set_up_planner( 2024-08-06T21:24:02.9550447Z >>> self, 2024-08-06T21:24:02.9550702Z >>> state_dict: STATE_DICT_TYPE, 2024-08-06T21:24:02.9551040Z >>> storage_meta: Optional[StorageMeta], 2024-08-06T21:24:02.9551369Z >>> is_coordinator: bool, 2024-08-06T21:24:02.9551656Z >>> ) -> None: 2024-08-06T21:24:02.9551910Z >>> # prefix all keys with `foo_`` 2024-08-06T21:24:02.9552412Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-08-06T21:24:02.9552827Z 2024-08-06T21:24:02.9553159Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-08-06T21:24:02.9553660Z 2024-08-06T21:24:02.9553791Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:02.9554128Z >>> class FP16Planner(DefaultSavePlanner): 2024-08-06T21:24:02.9554473Z >>> def create_local_plan(self): 2024-08-06T21:24:02.9554806Z >>> plan = super().create_local_plan() 2024-08-06T21:24:02.9555124Z >>> for p in plan: 2024-08-06T21:24:02.9555419Z >>> if p.tensor_data is not None: 2024-08-06T21:24:02.9555807Z >>> p.tensor_data.properties.dtype = torch.float16 2024-08-06T21:24:02.9556165Z >>> return plan 2024-08-06T21:24:02.9556409Z >>> 2024-08-06T21:24:02.9556638Z >>> def resolve_data(self, write_item): 2024-08-06T21:24:02.9556973Z >>> item = super().resolve_data(write_item) 2024-08-06T21:24:02.9557539Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-08-06T21:24:02.9557925Z 2024-08-06T21:24:02.9558281Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-08-06T21:24:02.9558739Z 2024-08-06T21:24:02.9558870Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:02.9559192Z >>> from itertools import islice 2024-08-06T21:24:02.9559523Z >>> from dataclasses import replace 2024-08-06T21:24:02.9559907Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-08-06T21:24:02.9560458Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-08-06T21:24:02.9560989Z >>> # This sample doesn't handle ShardedTensors 2024-08-06T21:24:02.9561365Z >>> def create_global_plan(self, all_plans): 2024-08-06T21:24:02.9561697Z >>> def chunk(it, size): 2024-08-06T21:24:02.9561994Z >>> it = iter(it) 2024-08-06T21:24:02.9562349Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-08-06T21:24:02.9562718Z >>> all_plans = [ 2024-08-06T21:24:02.9563036Z >>> replace(plan, items=items) for plan, items in 2024-08-06T21:24:02.9563484Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-08-06T21:24:02.9563923Z >>> ] 2024-08-06T21:24:02.9564202Z >>> return super().create_global_plan(all_plans) 2024-08-06T21:24:02.9564451Z 2024-08-06T21:24:02.9564733Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-08-06T21:24:02.9565381Z accomplished by having each rank contribute their data items in the local plan and 2024-08-06T21:24:02.9565893Z the global planner aggregate them: 2024-08-06T21:24:02.9566102Z 2024-08-06T21:24:02.9566228Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:02.9566598Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-08-06T21:24:02.9567000Z >>> def create_local_plan(self) -> SavePlan: 2024-08-06T21:24:02.9567365Z >>> plan = super().create_local_plan() 2024-08-06T21:24:02.9567747Z >>> return replace(plan, planner_data="per-rank-data") 2024-08-06T21:24:02.9568110Z >>> 2024-08-06T21:24:02.9568530Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-08-06T21:24:02.9569134Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-08-06T21:24:02.9569604Z >>> merged_data = [p.planner_data for p in global_plan] 2024-08-06T21:24:02.9570045Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-08-06T21:24:02.9570433Z >>> return global_plan, metadata 2024-08-06T21:24:02.9570656Z 2024-08-06T21:24:02.9570907Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9571270Z 2024-08-06T21:24:02.9571876Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=275. 2024-08-06T21:24:02.9572846Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9573251Z 2024-08-06T21:24:02.9573538Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-08-06T21:24:02.9573950Z 2024-08-06T21:24:02.9574243Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-08-06T21:24:02.9574647Z 2024-08-06T21:24:02.9574939Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-06T21:24:02.9575446Z will be visible to the whole process. 2024-08-06T21:24:02.9575672Z 2024-08-06T21:24:02.9575948Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-08-06T21:24:02.9576353Z 2024-08-06T21:24:02.9576471Z 1) set_up_planner - called on all ranks. 2024-08-06T21:24:02.9576831Z Signals the start of loading a checkpoint. 2024-08-06T21:24:02.9577068Z 2024-08-06T21:24:02.9577226Z 2) create_local_plan - called on all ranks. 2024-08-06T21:24:02.9577747Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-08-06T21:24:02.9578150Z 2024-08-06T21:24:02.9578342Z 3) create_global_plan - called on the coordinator rank only. 2024-08-06T21:24:02.9578828Z Takes the LoadPlan from all ranks and make any global decision. 2024-08-06T21:24:02.9579149Z 2024-08-06T21:24:02.9579291Z 4) load_bytes - called multiple times on each rank 2024-08-06T21:24:02.9579832Z This is called once per non-tensor value in state_dict. 2024-08-06T21:24:02.9580106Z 2024-08-06T21:24:02.9580328Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-08-06T21:24:02.9580852Z They are called in pair for each Tensor value in state_dict. 2024-08-06T21:24:02.9581153Z 2024-08-06T21:24:02.9581455Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-08-06T21:24:02.9582063Z most changes can be expressed by changes in a single method. 2024-08-06T21:24:02.9582358Z 2024-08-06T21:24:02.9582485Z There are two usual patterns of extension: 2024-08-06T21:24:02.9582728Z 2024-08-06T21:24:02.9582983Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-08-06T21:24:02.9583675Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-08-06T21:24:02.9584271Z to keep a reference to the original state_dict as load happens in place so 2024-08-06T21:24:02.9584748Z we need to be able to perform it in place 2024-08-06T21:24:02.9584975Z 2024-08-06T21:24:02.9585105Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:02.9585457Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-08-06T21:24:02.9585809Z >>> def set_up_planner( 2024-08-06T21:24:02.9586074Z >>> self, 2024-08-06T21:24:02.9586317Z >>> state_dict: STATE_DICT_TYPE, 2024-08-06T21:24:02.9586636Z >>> metadata: Metadata, 2024-08-06T21:24:02.9586998Z >>> is_coordinator: bool, 2024-08-06T21:24:02.9587278Z >>> ) -> None: 2024-08-06T21:24:02.9587551Z >>> self.original_state_dict = state_dict 2024-08-06T21:24:02.9587967Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-08-06T21:24:02.9588334Z >>> 2024-08-06T21:24:02.9588569Z >>> if self.flatten_sharded_tensors: 2024-08-06T21:24:02.9588959Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-08-06T21:24:02.9589302Z >>> 2024-08-06T21:24:02.9589525Z >>> if self.flatten_state_dict: 2024-08-06T21:24:02.9589929Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-08-06T21:24:02.9590307Z >>> 2024-08-06T21:24:02.9590528Z >>> self.state_dict = state_dict 2024-08-06T21:24:02.9590853Z >>> self.metadata = metadata 2024-08-06T21:24:02.9591169Z >>> self.is_coordinator = is_coordinator 2024-08-06T21:24:02.9591489Z >>> 2024-08-06T21:24:02.9591721Z >>> def load_bytes(self, read_item, value): 2024-08-06T21:24:02.9592047Z >>> # Remove the "foo_" prefix 2024-08-06T21:24:02.9592580Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-08-06T21:24:02.9593074Z 2024-08-06T21:24:02.9593079Z 2024-08-06T21:24:02.9593351Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-08-06T21:24:02.9593731Z 2024-08-06T21:24:02.9593844Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:02.9594221Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-08-06T21:24:02.9594614Z >>> def resolve_tensor(self, read_item): 2024-08-06T21:24:02.9594964Z >>> tensor = super().resolve_tensor(read_item) 2024-08-06T21:24:02.9595356Z >>> return torch.empty_like(tensor, device="cpu") 2024-08-06T21:24:02.9595703Z >>> 2024-08-06T21:24:02.9595939Z >>> def commit_tensor(self, read_item, tensor): 2024-08-06T21:24:02.9596347Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-08-06T21:24:02.9596613Z 2024-08-06T21:24:02.9596911Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9597276Z 2024-08-06T21:24:02.9713234Z 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. 2024-08-06T21:24:02.9714257Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9714648Z 2024-08-06T21:24:02.9714790Z Load a distributed ``state_dict`` in SPMD style. 2024-08-06T21:24:02.9715038Z 2024-08-06T21:24:02.9715229Z Each rank will try to read the least amount of data necessary 2024-08-06T21:24:02.9715754Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-08-06T21:24:02.9716365Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-08-06T21:24:02.9716761Z 2024-08-06T21:24:02.9717021Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-06T21:24:02.9717660Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-08-06T21:24:02.9718202Z ``load_state_dict`` once the deserialization is complete. 2024-08-06T21:24:02.9718491Z 2024-08-06T21:24:02.9718596Z .. warning:: 2024-08-06T21:24:02.9718901Z All tensors in ``state_dict`` must be allocated on their 2024-08-06T21:24:02.9719523Z destination device *prior to* calling this function. 2024-08-06T21:24:02.9719813Z 2024-08-06T21:24:02.9720044Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-08-06T21:24:02.9720488Z on state_dict. 2024-08-06T21:24:02.9720629Z 2024-08-06T21:24:02.9720720Z .. warning:: 2024-08-06T21:24:02.9721063Z Users must call `load_state_dict` on the root module to ensure load 2024-08-06T21:24:02.9721577Z pos-processing and non-tensor data properly propagates. 2024-08-06T21:24:02.9721866Z 2024-08-06T21:24:02.9721953Z .. note: 2024-08-06T21:24:02.9722307Z If no process group is initialized, this function will assume the intent 2024-08-06T21:24:02.9722882Z is to load a checkpoint into the local process. This can be useful in the 2024-08-06T21:24:02.9723504Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-08-06T21:24:02.9723970Z or ShardedTensor) 2024-08-06T21:24:02.9724143Z 2024-08-06T21:24:02.9724238Z .. note: 2024-08-06T21:24:02.9724506Z Rank 0 is assumed to be the coordinator rank. 2024-08-06T21:24:02.9724752Z 2024-08-06T21:24:02.9724833Z Args: 2024-08-06T21:24:02.9725109Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-06T21:24:02.9725521Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-06T21:24:02.9725986Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-06T21:24:02.9726525Z depends on the storage. It can be a path to a folder or to a file. 2024-08-06T21:24:02.9727020Z It can also be a key if the storage is a key-value store. 2024-08-06T21:24:02.9727392Z (Default: ``None``) 2024-08-06T21:24:02.9727701Z storage_reader (Optional[StorageReader]): 2024-08-06T21:24:02.9728145Z Instance of StorageWriter used to perform reads. If this is not 2024-08-06T21:24:02.9728718Z specified, DCP will automatically infer the reader based on the 2024-08-06T21:24:02.9729251Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-06T21:24:02.9729680Z be raised. (Default: ``None``) 2024-08-06T21:24:02.9729997Z planner (Optional[LoadPlanner]): 2024-08-06T21:24:02.9730418Z Instance of LoadPlanner. If this is not specificed, the default 2024-08-06T21:24:02.9730872Z planner will be used. (Default: ``None``) 2024-08-06T21:24:02.9731230Z process_group (Optional[ProcessGroup]): 2024-08-06T21:24:02.9731647Z ProcessGroup to be used for cross-rank synchronization. 2024-08-06T21:24:02.9732043Z (Default: ``None``) 2024-08-06T21:24:02.9732217Z 2024-08-06T21:24:02.9732302Z Returns: 2024-08-06T21:24:02.9732511Z None. 2024-08-06T21:24:02.9732672Z 2024-08-06T21:24:02.9732770Z Examples 2024-08-06T21:24:02.9732981Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.9733251Z >>> my_model = MyModule() 2024-08-06T21:24:02.9733576Z >>> optimizer = Adagrad(my_model.parameters()) 2024-08-06T21:24:02.9733940Z >>> model_state_dict = my_model.state_dict() 2024-08-06T21:24:02.9734479Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-08-06T21:24:02.9734896Z 2024-08-06T21:24:02.9735062Z >>> torch.distributed.checkpoint.load_state_dict( 2024-08-06T21:24:02.9735426Z >>> state_dict=model_state_dict, 2024-08-06T21:24:02.9735762Z >>> storage_reader=fs_storage_reader, 2024-08-06T21:24:02.9736076Z >>> ) 2024-08-06T21:24:02.9736190Z 2024-08-06T21:24:02.9736386Z >>> # module.load_state_dict() function might have customized steps 2024-08-06T21:24:02.9736824Z >>> # to flush the state_dict, must call it to 2024-08-06T21:24:02.9737170Z >>> # ensure correct behavior. 2024-08-06T21:24:02.9737490Z >>> my_model.load_state_dict(model_state_dict) 2024-08-06T21:24:02.9737738Z 2024-08-06T21:24:02.9737847Z .. note:: 2024-08-06T21:24:02.9738314Z load_state_dict uses collectives to coordinate reads across ranks. 2024-08-06T21:24:02.9739138Z For NCCL-based process groups, internal tensor representations of 2024-08-06T21:24:02.9740009Z objects must be moved to the GPU device before communication takes place. 2024-08-06T21:24:02.9740590Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-06T21:24:02.9741161Z and it is the user's responsibility to ensure that this is set so that each 2024-08-06T21:24:02.9741684Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-06T21:24:02.9741993Z 2024-08-06T21:24:02.9742244Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9742877Z 2024-08-06T21:24:02.9743528Z 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. 2024-08-06T21:24:02.9744504Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9744886Z 2024-08-06T21:24:02.9745007Z Save a distributed model in SPMD style. 2024-08-06T21:24:02.9745243Z 2024-08-06T21:24:02.9745435Z This function is different from ``torch.save()`` as it handles 2024-08-06T21:24:02.9746165Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-08-06T21:24:02.9746597Z 2024-08-06T21:24:02.9747071Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-06T21:24:02.9747652Z save will call ``state_dict`` before serialization. 2024-08-06T21:24:02.9747925Z 2024-08-06T21:24:02.9748057Z .. warning:: 2024-08-06T21:24:02.9748452Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-08-06T21:24:02.9748952Z for saved state_dicts. 2024-08-06T21:24:02.9749142Z 2024-08-06T21:24:02.9749232Z .. warning:: 2024-08-06T21:24:02.9749575Z If using the `process_group` argument, make sure that only its ranks 2024-08-06T21:24:02.9750256Z call `save_state_dict` and that all data in state_dict belong to it. 2024-08-06T21:24:02.9750590Z 2024-08-06T21:24:02.9750677Z .. note:: 2024-08-06T21:24:02.9751079Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-08-06T21:24:02.9751765Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-08-06T21:24:02.9752261Z group needs to be passed in. 2024-08-06T21:24:02.9752454Z 2024-08-06T21:24:02.9752551Z .. note:: 2024-08-06T21:24:02.9752935Z If no process group is available, this function assumes the intention is to save the 2024-08-06T21:24:02.9753495Z state_dict in the local process. 2024-08-06T21:24:02.9753718Z 2024-08-06T21:24:02.9753803Z .. note: 2024-08-06T21:24:02.9754048Z Rank 0 is assumed to be the coordinator rank. 2024-08-06T21:24:02.9754359Z 2024-08-06T21:24:02.9754363Z 2024-08-06T21:24:02.9754448Z Args: 2024-08-06T21:24:02.9754725Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-06T21:24:02.9755142Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-06T21:24:02.9755612Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-06T21:24:02.9756151Z depends on the storage. It can be a path to a folder or to a file. 2024-08-06T21:24:02.9756644Z It can also be a key if the storage is a key-value store. 2024-08-06T21:24:02.9757021Z (Default: ``None``) 2024-08-06T21:24:02.9757380Z storage_writer (Optional[StorageWriter]): 2024-08-06T21:24:02.9757833Z Instance of StorageWriter used to perform writes. If this is not 2024-08-06T21:24:02.9758352Z specified, DCP will automatically infer the writer based on the 2024-08-06T21:24:02.9758875Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-06T21:24:02.9759304Z be raised. (Default: ``None``) 2024-08-06T21:24:02.9759624Z planner (Optional[SavePlanner]): 2024-08-06T21:24:02.9760042Z Instance of SavePlanner. If this is not specificed, the default 2024-08-06T21:24:02.9760512Z planner will be used. (Default: ``None``) 2024-08-06T21:24:02.9760961Z process_group (Optional[ProcessGroup]): 2024-08-06T21:24:02.9761389Z ProcessGroup to be used for cross-rank synchronization. 2024-08-06T21:24:02.9761793Z (Default: ``None``) 2024-08-06T21:24:02.9761965Z 2024-08-06T21:24:02.9762053Z Returns: 2024-08-06T21:24:02.9762338Z Metadata: Metadata object for the saved checkpoint. 2024-08-06T21:24:02.9762606Z 2024-08-06T21:24:02.9762707Z Example: 2024-08-06T21:24:02.9762919Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.9763196Z >>> my_model = MyModule() 2024-08-06T21:24:02.9763377Z 2024-08-06T21:24:02.9763501Z >>> state_dict = {"model": my_model} 2024-08-06T21:24:02.9763714Z 2024-08-06T21:24:02.9764042Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-06T21:24:02.9764629Z >>> torch.distributed.checkpoint.save( 2024-08-06T21:24:02.9764986Z >>> state_dict=state_dict, 2024-08-06T21:24:02.9765300Z >>> storage_writer=fs_storage_writer, 2024-08-06T21:24:02.9765624Z >>> ) 2024-08-06T21:24:02.9765747Z 2024-08-06T21:24:02.9765851Z .. note:: 2024-08-06T21:24:02.9766179Z save_state_dict uses collectives to coordinate writes across ranks. 2024-08-06T21:24:02.9766734Z For NCCL-based process groups, internal tensor representations of 2024-08-06T21:24:02.9767306Z objects must be moved to the GPU device before communication takes place. 2024-08-06T21:24:02.9767868Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-06T21:24:02.9768419Z and it is the user's responsibility to ensure that this is set so that 2024-08-06T21:24:02.9768944Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-06T21:24:02.9769255Z 2024-08-06T21:24:02.9769516Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9769879Z 2024-08-06T21:24:02.9770580Z 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. 2024-08-06T21:24:02.9771589Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9772234Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-08-06T21:24:02.9772912Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-08-06T21:24:02.9773316Z 2024-08-06T21:24:02.9773407Z .. warning:: 2024-08-06T21:24:02.9773711Z This feature is experimental and subject to change. 2024-08-06T21:24:02.9773983Z 2024-08-06T21:24:02.9774078Z Args: 2024-08-06T21:24:02.9774344Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-06T21:24:02.9774796Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-06T21:24:02.9775269Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-06T21:24:02.9775797Z depends on the storage. It can be a path to a folder or to a file. 2024-08-06T21:24:02.9776296Z It can also be a key if the storage is a key-value store. 2024-08-06T21:24:02.9776683Z (Default: ``None``) 2024-08-06T21:24:02.9776990Z storage_writer (Optional[StorageWriter]): 2024-08-06T21:24:02.9777515Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-08-06T21:24:02.9778093Z this is not specified, DCP will automatically infer the writer based on the 2024-08-06T21:24:02.9778655Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-06T21:24:02.9779093Z be raised. (Default: ``None``) 2024-08-06T21:24:02.9779430Z planner (Optional[SavePlanner]): 2024-08-06T21:24:02.9779849Z Instance of SavePlanner. If this is not specificed, the default 2024-08-06T21:24:02.9780307Z planner will be used. (Default: ``None``) 2024-08-06T21:24:02.9780681Z process_group (Optional[ProcessGroup]): 2024-08-06T21:24:02.9781097Z ProcessGroup to be used for cross-rank synchronization. 2024-08-06T21:24:02.9781596Z (Default: ``None``) 2024-08-06T21:24:02.9781792Z 2024-08-06T21:24:02.9781882Z Returns: 2024-08-06T21:24:02.9782220Z Future: A future holding the resultant Metadata object from `save`. 2024-08-06T21:24:02.9782562Z 2024-08-06T21:24:02.9782652Z Example: 2024-08-06T21:24:02.9782893Z >>> # xdoctest: +SKIP 2024-08-06T21:24:02.9783184Z >>> my_model = MyModule() 2024-08-06T21:24:02.9783379Z 2024-08-06T21:24:02.9783493Z >>> state_dict = {"model": my_model} 2024-08-06T21:24:02.9783726Z 2024-08-06T21:24:02.9784029Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-06T21:24:02.9784670Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-08-06T21:24:02.9785094Z >>> state_dict=state_dict, 2024-08-06T21:24:02.9785428Z >>> storage_writer=fs_storage_writer, 2024-08-06T21:24:02.9785748Z >>> ) 2024-08-06T21:24:02.9785955Z >>> 2024-08-06T21:24:02.9786181Z >>> # ... do some work ... 2024-08-06T21:24:02.9786461Z >>> 2024-08-06T21:24:02.9786683Z >>> checkpoint_future.result() 2024-08-06T21:24:02.9786984Z 2024-08-06T21:24:02.9787067Z 2024-08-06T21:24:02.9787439Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9787812Z 2024-08-06T21:24:02.9840460Z 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. 2024-08-06T21:24:02.9841535Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:02.9841938Z 2024-08-06T21:24:02.9842141Z Initialize rendezvous event object and record its operations. 2024-08-06T21:24:02.9842633Z 2024-08-06T21:24:02.9842881Z Args: 2024-08-06T21:24:02.9843120Z run_id (str): The run id of the rendezvous. 2024-08-06T21:24:02.9843520Z message (str): The message describing the event. 2024-08-06T21:24:02.9844040Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-08-06T21:24:02.9844589Z name (str): Event name. (E.g. Current action being performed). 2024-08-06T21:24:02.9845000Z hostname (str): Hostname of the node. 2024-08-06T21:24:02.9845366Z pid (Optional[int]): The process id of the node. 2024-08-06T21:24:02.9845855Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-08-06T21:24:02.9846492Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-08-06T21:24:02.9847039Z rank (Optional[int]): The rank of the node, if known. 2024-08-06T21:24:02.9847442Z Returns: 2024-08-06T21:24:02.9847650Z None 2024-08-06T21:24:02.9847855Z Example: 2024-08-06T21:24:02.9848091Z >>> # See DynamicRendezvousHandler class 2024-08-06T21:24:02.9848423Z >>> def _record( 2024-08-06T21:24:02.9848663Z ... self, 2024-08-06T21:24:02.9848892Z ... message: str, 2024-08-06T21:24:02.9849204Z ... node_state: NodeState = NodeState.RUNNING, 2024-08-06T21:24:02.9849666Z ... rank: Optional[int] = None, 2024-08-06T21:24:02.9849966Z ... ) -> None: 2024-08-06T21:24:02.9850230Z ... construct_and_record_rdzv_event( 2024-08-06T21:24:02.9850624Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-08-06T21:24:02.9851015Z ... run_id=self._settings.run_id, 2024-08-06T21:24:02.9851344Z ... message=message, 2024-08-06T21:24:02.9851639Z ... node_state=node_state, 2024-08-06T21:24:02.9851956Z ... hostname=self._this_node.addr, 2024-08-06T21:24:02.9852294Z ... pid=self._this_node.pid, 2024-08-06T21:24:02.9852631Z ... local_id=self._this_node.local_id, 2024-08-06T21:24:02.9852951Z ... rank=rank, 2024-08-06T21:24:02.9853209Z ... ) 2024-08-06T21:24:02.9853337Z 2024-08-06T21:24:02.9853700Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:02.9854072Z 2024-08-06T21:24:03.1404150Z 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. 2024-08-06T21:24:03.1405145Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1405530Z 2024-08-06T21:24:03.1405717Z This configures FSDP-native mixed precision training. 2024-08-06T21:24:03.1406008Z 2024-08-06T21:24:03.1406102Z Attributes: 2024-08-06T21:24:03.1406509Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-08-06T21:24:03.1407071Z parameters during forward and backward and thus the dtype for 2024-08-06T21:24:03.1407622Z forward and backward computation. Outside forward and backward, the 2024-08-06T21:24:03.1408179Z *sharded* parameters are kept in full precision (e.g. for the 2024-08-06T21:24:03.1408720Z optimizer step), and for model checkpointing, the parameters are 2024-08-06T21:24:03.1409202Z always saved in full precision. (Default: ``None``) 2024-08-06T21:24:03.1409700Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-06T21:24:03.1410263Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-08-06T21:24:03.1410768Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-08-06T21:24:03.1411277Z the ``param_dtype`` value, still running gradient reduction in low 2024-08-06T21:24:03.1411815Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-08-06T21:24:03.1412352Z to force gradient reduction to run in full precision. (Default: 2024-08-06T21:24:03.1412752Z ``None``) 2024-08-06T21:24:03.1413113Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-06T21:24:03.1413843Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-08-06T21:24:03.1414358Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-08-06T21:24:03.1414892Z dtype thereafter. For model checkpointing, the buffers are saved 2024-08-06T21:24:03.1415409Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-08-06T21:24:03.1415788Z ``None``) 2024-08-06T21:24:03.1416130Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-08-06T21:24:03.1416665Z gradients to full precision after the backward pass in preparation 2024-08-06T21:24:03.1417195Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-08-06T21:24:03.1417722Z in the dtype used for gradient reduction, which can save memory if 2024-08-06T21:24:03.1418318Z using a custom optimizer that supports running in low precision. 2024-08-06T21:24:03.1418739Z (Default: ``False``) 2024-08-06T21:24:03.1419138Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-08-06T21:24:03.1419681Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-08-06T21:24:03.1420209Z that parameter and input dtypes match for forward computation, as 2024-08-06T21:24:03.1420753Z required by many ops. This may need to be set to ``True`` when only 2024-08-06T21:24:03.1421304Z applying mixed precision to some but not all FSDP modules, in which 2024-08-06T21:24:03.1421853Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-08-06T21:24:03.1422282Z (Default: ``False``) 2024-08-06T21:24:03.1422779Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-08-06T21:24:03.1423384Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-08-06T21:24:03.1423900Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-08-06T21:24:03.1424411Z this does not do anything. (Default: ``True``) 2024-08-06T21:24:03.1424945Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-08-06T21:24:03.1425586Z module classes to ignore for mixed precision when using an 2024-08-06T21:24:03.1426120Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-08-06T21:24:03.1426691Z applied to them separately with mixed precision disabled (meaning 2024-08-06T21:24:03.1427372Z that the final FSDP construction would deviate from the specified 2024-08-06T21:24:03.1427923Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-08-06T21:24:03.1428470Z not do anything. This API is experimental and subject to change. 2024-08-06T21:24:03.1428950Z (Default: ``(_BatchNorm,)``) 2024-08-06T21:24:03.1429171Z 2024-08-06T21:24:03.1429357Z .. note:: This API is experimental and subject to change. 2024-08-06T21:24:03.1429690Z 2024-08-06T21:24:03.1429930Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-08-06T21:24:03.1430328Z 2024-08-06T21:24:03.1430520Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-08-06T21:24:03.1430948Z precision, but buffers are not. 2024-08-06T21:24:03.1431197Z 2024-08-06T21:24:03.1431417Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-08-06T21:24:03.1432002Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-08-06T21:24:03.1432604Z Disabling FSDP's mixed precision for those norm modules only means that 2024-08-06T21:24:03.1433183Z the affine parameters are kept in ``float32``. However, this incurs 2024-08-06T21:24:03.1433873Z separate all-gathers and reduce-scatters for those norm modules, which 2024-08-06T21:24:03.1434438Z may be inefficient, so if the workload permits, the user should prefer 2024-08-06T21:24:03.1434934Z to still apply mixed precision to those modules. 2024-08-06T21:24:03.1435187Z 2024-08-06T21:24:03.1435465Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-08-06T21:24:03.1435993Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-08-06T21:24:03.1436548Z modules will have FSDP applied to them separately with mixed precision 2024-08-06T21:24:03.1437073Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-08-06T21:24:03.1437361Z 2024-08-06T21:24:03.1437575Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-08-06T21:24:03.1438123Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-08-06T21:24:03.1438632Z its ``cast_root_forward_inputs`` takes precedence over its 2024-08-06T21:24:03.1439089Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-08-06T21:24:03.1439600Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-08-06T21:24:03.1440204Z sufficient for the typical case where each FSDP instance has the same 2024-08-06T21:24:03.1440776Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-08-06T21:24:03.1441300Z ``param_dtype`` at the beginning of the model's forward pass. 2024-08-06T21:24:03.1441605Z 2024-08-06T21:24:03.1441810Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-08-06T21:24:03.1442374Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-08-06T21:24:03.1443128Z values to configure casting inputs or not before each instance's 2024-08-06T21:24:03.1443647Z forward. In such a case, since the casts happen before each FSDP 2024-08-06T21:24:03.1444183Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-08-06T21:24:03.1444740Z submodules run before its FSDP submodules to avoid the activation dtype 2024-08-06T21:24:03.1445306Z being changed due to a different ``MixedPrecision`` configuration. 2024-08-06T21:24:03.1445630Z 2024-08-06T21:24:03.1445738Z Example:: 2024-08-06T21:24:03.1445869Z 2024-08-06T21:24:03.1446003Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:03.1446421Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-08-06T21:24:03.1446922Z >>> model[1] = FSDP( 2024-08-06T21:24:03.1447190Z >>> model[1], 2024-08-06T21:24:03.1447667Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-08-06T21:24:03.1448184Z >>> ) 2024-08-06T21:24:03.1448401Z >>> model = FSDP( 2024-08-06T21:24:03.1448660Z >>> model, 2024-08-06T21:24:03.1449131Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-08-06T21:24:03.1449627Z >>> ) 2024-08-06T21:24:03.1449768Z 2024-08-06T21:24:03.1450056Z The above shows a working example. On the other hand, if ``model[1]`` 2024-08-06T21:24:03.1450597Z were replaced with ``model[0]``, meaning that the submodule using 2024-08-06T21:24:03.1451151Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-08-06T21:24:03.1451724Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-08-06T21:24:03.1452162Z ones. 2024-08-06T21:24:03.1452282Z 2024-08-06T21:24:03.1452286Z 2024-08-06T21:24:03.1452551Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1452921Z 2024-08-06T21:24:03.1542647Z 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=648. 2024-08-06T21:24:03.1543867Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1544506Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-06T21:24:03.1544895Z 2024-08-06T21:24:03.1545176Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-08-06T21:24:03.1545762Z The target module does not have to be a FSDP module. If the target 2024-08-06T21:24:03.1546529Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-08-06T21:24:03.1547009Z 2024-08-06T21:24:03.1547294Z .. note:: This API should be called for only the top-level (root) 2024-08-06T21:24:03.1547748Z module. 2024-08-06T21:24:03.1547902Z 2024-08-06T21:24:03.1548115Z .. note:: This API enables users to transparently use the conventional 2024-08-06T21:24:03.1548637Z ``state_dict`` API to take model checkpoints in cases where the 2024-08-06T21:24:03.1549155Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-08-06T21:24:03.1549683Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-08-06T21:24:03.1550246Z instances, while dispatching into `sharded_state_dict` implementation 2024-08-06T21:24:03.1550779Z for FSDP: 2024-08-06T21:24:03.1550929Z 2024-08-06T21:24:03.1551025Z Example:: 2024-08-06T21:24:03.1551176Z 2024-08-06T21:24:03.1551309Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:03.1551676Z >>> model = DDP(FSDP(...)) 2024-08-06T21:24:03.1551993Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:03.1552313Z >>> model, 2024-08-06T21:24:03.1552617Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-06T21:24:03.1553087Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-08-06T21:24:03.1553650Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-08-06T21:24:03.1554087Z >>> ) 2024-08-06T21:24:03.1554355Z >>> param_state_dict = model.state_dict() 2024-08-06T21:24:03.1554755Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-06T21:24:03.1555054Z 2024-08-06T21:24:03.1555141Z Args: 2024-08-06T21:24:03.1555399Z module (torch.nn.Module): Root module. 2024-08-06T21:24:03.1555870Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-06T21:24:03.1556563Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-08-06T21:24:03.1557052Z target ``state_dict_type``. 2024-08-06T21:24:03.1557517Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-08-06T21:24:03.1558016Z for the optimizer state dict. 2024-08-06T21:24:03.1558238Z 2024-08-06T21:24:03.1558344Z Returns: 2024-08-06T21:24:03.1558698Z A StateDictSettings that include the previous state_dict type and 2024-08-06T21:24:03.1559156Z configuration for the module. 2024-08-06T21:24:03.1559467Z 2024-08-06T21:24:03.1559827Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1560207Z 2024-08-06T21:24:03.1561014Z 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=804. 2024-08-06T21:24:03.1562192Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1562813Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-06T21:24:03.1563189Z 2024-08-06T21:24:03.1563506Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-08-06T21:24:03.1564075Z :meth:`set_state_dict_type` for the detail. 2024-08-06T21:24:03.1564315Z 2024-08-06T21:24:03.1564422Z Example:: 2024-08-06T21:24:03.1564559Z 2024-08-06T21:24:03.1564691Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:03.1565048Z >>> model = DDP(FSDP(...)) 2024-08-06T21:24:03.1565380Z >>> with FSDP.state_dict_type( 2024-08-06T21:24:03.1565684Z >>> model, 2024-08-06T21:24:03.1565984Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-06T21:24:03.1566349Z >>> ): 2024-08-06T21:24:03.1566605Z >>> checkpoint = model.state_dict() 2024-08-06T21:24:03.1566851Z 2024-08-06T21:24:03.1566937Z Args: 2024-08-06T21:24:03.1567194Z module (torch.nn.Module): Root module. 2024-08-06T21:24:03.1567657Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-06T21:24:03.1568253Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-08-06T21:24:03.1568766Z configuration for the target ``state_dict_type``. 2024-08-06T21:24:03.1569266Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-08-06T21:24:03.1569825Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-08-06T21:24:03.1570288Z 2024-08-06T21:24:03.1570650Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1571033Z 2024-08-06T21:24:03.1605318Z 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=1801. 2024-08-06T21:24:03.1606521Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1606913Z 2024-08-06T21:24:03.1607152Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-08-06T21:24:03.1607506Z 2024-08-06T21:24:03.1623190Z The given state-dict can be transformed to one of three types: 2024-08-06T21:24:03.1624143Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-08-06T21:24:03.1624789Z 2024-08-06T21:24:03.1625037Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-08-06T21:24:03.1625610Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-08-06T21:24:03.1626033Z avoid OOM. 2024-08-06T21:24:03.1626175Z 2024-08-06T21:24:03.1626412Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-08-06T21:24:03.1627184Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-08-06T21:24:03.1627582Z memory. 2024-08-06T21:24:03.1627713Z 2024-08-06T21:24:03.1627933Z For local state_dict, no transformation will be performed. But a state 2024-08-06T21:24:03.1628510Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-08-06T21:24:03.1628967Z nature (this is not supported yet). 2024-08-06T21:24:03.1629188Z 2024-08-06T21:24:03.1629290Z Example:: 2024-08-06T21:24:03.1629409Z 2024-08-06T21:24:03.1629548Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:03.1630014Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-06T21:24:03.1630530Z >>> from torch.distributed.fsdp import StateDictType 2024-08-06T21:24:03.1630977Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-06T21:24:03.1631461Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-06T21:24:03.1631874Z >>> # Save a checkpoint 2024-08-06T21:24:03.1632158Z >>> model, optim = ... 2024-08-06T21:24:03.1632427Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:03.1632717Z >>> model, 2024-08-06T21:24:03.1632977Z >>> StateDictType.FULL_STATE_DICT, 2024-08-06T21:24:03.1633323Z >>> FullStateDictConfig(rank0_only=False), 2024-08-06T21:24:03.1633721Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-06T21:24:03.1634065Z >>> ) 2024-08-06T21:24:03.1634287Z >>> state_dict = model.state_dict() 2024-08-06T21:24:03.1634675Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-06T21:24:03.1635107Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-06T21:24:03.1635460Z >>> # Load a checkpoint 2024-08-06T21:24:03.1635742Z >>> model, optim = ... 2024-08-06T21:24:03.1636071Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-06T21:24:03.1636477Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:03.1636773Z >>> model, 2024-08-06T21:24:03.1637046Z >>> StateDictType.FULL_STATE_DICT, 2024-08-06T21:24:03.1637393Z >>> FullStateDictConfig(rank0_only=False), 2024-08-06T21:24:03.1637788Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-06T21:24:03.1638137Z >>> ) 2024-08-06T21:24:03.1638366Z >>> model.load_state_dict(state_dict) 2024-08-06T21:24:03.1638749Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-06T21:24:03.1639130Z >>> model, optim, optim_state_dict 2024-08-06T21:24:03.1639429Z >>> ) 2024-08-06T21:24:03.1639685Z >>> optim.load_state_dict(optim_state_dict) 2024-08-06T21:24:03.1639917Z 2024-08-06T21:24:03.1640014Z Args: 2024-08-06T21:24:03.1640320Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-06T21:24:03.1640880Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-06T21:24:03.1641338Z were passed into the optimizer ``optim``. 2024-08-06T21:24:03.1641758Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-06T21:24:03.1642163Z parameters. 2024-08-06T21:24:03.1642740Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-08-06T21:24:03.1643281Z transform. If the value is None, optim.state_dict() will be used. ( 2024-08-06T21:24:03.1643704Z Default: ``None``) 2024-08-06T21:24:03.1644124Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-06T21:24:03.1644662Z are sharded or ``None`` if using the default process group. ( 2024-08-06T21:24:03.1645066Z Default: ``None``) 2024-08-06T21:24:03.1645232Z 2024-08-06T21:24:03.1645332Z Returns: 2024-08-06T21:24:03.1645638Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-08-06T21:24:03.1646124Z ``model``. The sharding of the optimizer state is based on 2024-08-06T21:24:03.1646510Z ``state_dict_type``. 2024-08-06T21:24:03.1646679Z 2024-08-06T21:24:03.1646931Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1647450Z 2024-08-06T21:24:03.1648338Z 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=1899. 2024-08-06T21:24:03.1649551Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1649940Z 2024-08-06T21:24:03.1650292Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-08-06T21:24:03.1650762Z 2024-08-06T21:24:03.1650942Z Given a ``optim_state_dict`` that is transformed through 2024-08-06T21:24:03.1651425Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-08-06T21:24:03.1651969Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-08-06T21:24:03.1652494Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-08-06T21:24:03.1652791Z 2024-08-06T21:24:03.1652920Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:03.1653397Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-06T21:24:03.1653912Z >>> from torch.distributed.fsdp import StateDictType 2024-08-06T21:24:03.1654345Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-06T21:24:03.1654832Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-06T21:24:03.1655260Z >>> # Save a checkpoint 2024-08-06T21:24:03.1655537Z >>> model, optim = ... 2024-08-06T21:24:03.1655810Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:03.1656099Z >>> model, 2024-08-06T21:24:03.1656361Z >>> StateDictType.FULL_STATE_DICT, 2024-08-06T21:24:03.1656710Z >>> FullStateDictConfig(rank0_only=False), 2024-08-06T21:24:03.1657100Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-06T21:24:03.1657494Z >>> ) 2024-08-06T21:24:03.1657716Z >>> state_dict = model.state_dict() 2024-08-06T21:24:03.1658055Z >>> original_osd = optim.state_dict() 2024-08-06T21:24:03.1658411Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-08-06T21:24:03.1658729Z >>> model, 2024-08-06T21:24:03.1658957Z >>> optim, 2024-08-06T21:24:03.1659205Z >>> optim_state_dict=original_osd 2024-08-06T21:24:03.1659500Z >>> ) 2024-08-06T21:24:03.1659765Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-06T21:24:03.1660110Z >>> # Load a checkpoint 2024-08-06T21:24:03.1660384Z >>> model, optim = ... 2024-08-06T21:24:03.1660705Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-06T21:24:03.1661067Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:03.1661355Z >>> model, 2024-08-06T21:24:03.1661680Z >>> StateDictType.FULL_STATE_DICT, 2024-08-06T21:24:03.1662023Z >>> FullStateDictConfig(rank0_only=False), 2024-08-06T21:24:03.1662414Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-06T21:24:03.1662758Z >>> ) 2024-08-06T21:24:03.1662878Z >>> model.load_state_dict(state_dict) 2024-08-06T21:24:03.1663021Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-06T21:24:03.1663147Z >>> model, optim, optim_state_dict 2024-08-06T21:24:03.1663231Z >>> ) 2024-08-06T21:24:03.1663359Z >>> optim.load_state_dict(optim_state_dict) 2024-08-06T21:24:03.1663377Z 2024-08-06T21:24:03.1663460Z Args: 2024-08-06T21:24:03.1663656Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-06T21:24:03.1663870Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-06T21:24:03.1664000Z were passed into the optimizer ``optim``. 2024-08-06T21:24:03.1664180Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-06T21:24:03.1664286Z parameters. 2024-08-06T21:24:03.1664496Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-08-06T21:24:03.1664701Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-08-06T21:24:03.1664963Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-08-06T21:24:03.1665142Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-08-06T21:24:03.1665336Z load_directly (bool): If this is set to True, this API will also 2024-08-06T21:24:03.1665548Z call optim.load_state_dict(result) before returning the result. 2024-08-06T21:24:03.1665769Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-08-06T21:24:03.1665868Z (Default: ``False``) 2024-08-06T21:24:03.1666120Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-06T21:24:03.1666307Z are sharded or ``None`` if using the default process group. ( 2024-08-06T21:24:03.1666423Z Default: ``None``) 2024-08-06T21:24:03.1666428Z 2024-08-06T21:24:03.1666682Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1666689Z 2024-08-06T21:24:03.1804584Z 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. 2024-08-06T21:24:03.1805751Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1806146Z 2024-08-06T21:24:03.1806375Z RemoteModule instance can only be created after RPC initialization. 2024-08-06T21:24:03.1806711Z 2024-08-06T21:24:03.1806916Z It creates a user-specified module on a specified remote node. 2024-08-06T21:24:03.1807448Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-06T21:24:03.1807909Z executed on the remote node. 2024-08-06T21:24:03.1808336Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-06T21:24:03.1808836Z gradients back to the corresponding remote module. 2024-08-06T21:24:03.1809458Z It can be shared across processors using `RPC framework `__, 2024-08-06T21:24:03.1810244Z without incurring any overheads of copying the actual module, 2024-08-06T21:24:03.1810743Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-08-06T21:24:03.1811175Z pointing to the remote module. 2024-08-06T21:24:03.1811373Z 2024-08-06T21:24:03.1811573Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-08-06T21:24:03.1812095Z the ``forward`` method of the module returned by the ``module_cls``. 2024-08-06T21:24:03.1812407Z 2024-08-06T21:24:03.1812739Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-08-06T21:24:03.1813394Z 2024-08-06T21:24:03.1813724Z Particularly, to create a hybrid model, typically the local modules should be 2024-08-06T21:24:03.1814889Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-08-06T21:24:03.1815599Z Hybrid Example: 2024-08-06T21:24:03.1815865Z >>> class HybridModel(nn.Module): 2024-08-06T21:24:03.1816211Z >>> def __init__(self) -> None: 2024-08-06T21:24:03.1816541Z >>> nn.Module.__init__(self) 2024-08-06T21:24:03.1816904Z >>> self.remote_embedding = RemoteModule(...) 2024-08-06T21:24:03.1817284Z >>> self.local_linear = nn.Linear(...) 2024-08-06T21:24:03.1817517Z 2024-08-06T21:24:03.1817713Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-06T21:24:03.1818282Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-08-06T21:24:03.1818859Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-08-06T21:24:03.1819302Z ``def forward(input: Tensor) -> Tensor:`` and 2024-08-06T21:24:03.1819704Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-08-06T21:24:03.1819988Z 2024-08-06T21:24:03.1820121Z .. note:: 2024-08-06T21:24:03.1820387Z If the remote module is placed on a cuda device, 2024-08-06T21:24:03.1820883Z any input CPU tensors will be automatically moved to the same cuda device, 2024-08-06T21:24:03.1821862Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-08-06T21:24:03.1822501Z 2024-08-06T21:24:03.1822586Z Args: 2024-08-06T21:24:03.1823076Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-06T21:24:03.1824234Z The device can be a local device or a remote device specified by one of the following remote 2024-08-06T21:24:03.1824725Z formats: 2024-08-06T21:24:03.1824873Z 2024-08-06T21:24:03.1825012Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-06T21:24:03.1825420Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-06T21:24:03.1825685Z 2024-08-06T21:24:03.1825930Z In addition, the device field can be optional and the default value is "cpu". 2024-08-06T21:24:03.1826414Z module_cls (nn.Module): For example, 2024-08-06T21:24:03.1826814Z >>> class MyModule(nn.Module): 2024-08-06T21:24:03.1827126Z >>> def forward(input): 2024-08-06T21:24:03.1827431Z >>> return input + 1 2024-08-06T21:24:03.1827741Z >>> 2024-08-06T21:24:03.1827961Z >>> module_cls = MyModule 2024-08-06T21:24:03.1828360Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-06T21:24:03.1828871Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-06T21:24:03.1829447Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-06T21:24:03.1830085Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-06T21:24:03.1830664Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-06T21:24:03.1831234Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-06T21:24:03.1831641Z 2024-08-06T21:24:03.1831728Z Returns: 2024-08-06T21:24:03.1832096Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-06T21:24:03.1832689Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-06T21:24:03.1833311Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-06T21:24:03.1833853Z on the user-provided module on the remote side. 2024-08-06T21:24:03.1834108Z 2024-08-06T21:24:03.1834206Z Example:: 2024-08-06T21:24:03.1834486Z Run the following code in two different processes: 2024-08-06T21:24:03.1834745Z 2024-08-06T21:24:03.1834864Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:03.1835167Z >>> # On worker 0: 2024-08-06T21:24:03.1835420Z >>> import torch 2024-08-06T21:24:03.1835702Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:03.1836071Z >>> from torch import nn, Tensor 2024-08-06T21:24:03.1836500Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-06T21:24:03.1836928Z >>> 2024-08-06T21:24:03.1837176Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-06T21:24:03.1837555Z >>> remote_linear_module = RemoteModule( 2024-08-06T21:24:03.1837910Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-06T21:24:03.1838223Z >>> ) 2024-08-06T21:24:03.1838450Z >>> input = torch.randn(128, 20) 2024-08-06T21:24:03.1838810Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-06T21:24:03.1839167Z >>> ret = ret_fut.wait() 2024-08-06T21:24:03.1839441Z >>> rpc.shutdown() 2024-08-06T21:24:03.1839595Z 2024-08-06T21:24:03.1839697Z >>> # On worker 1: 2024-08-06T21:24:03.1839934Z >>> import torch 2024-08-06T21:24:03.1840208Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:03.1840528Z >>> 2024-08-06T21:24:03.1840775Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-06T21:24:03.1841121Z >>> rpc.shutdown() 2024-08-06T21:24:03.1841273Z 2024-08-06T21:24:03.1841539Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1841909Z 2024-08-06T21:24:03.1842930Z 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. 2024-08-06T21:24:03.1844014Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1844402Z 2024-08-06T21:24:03.1844721Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-08-06T21:24:03.1845161Z 2024-08-06T21:24:03.1845497Z This alternate initialization method can be particularly useful if we want to create multiple 2024-08-06T21:24:03.1846331Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-08-06T21:24:03.1846773Z 2024-08-06T21:24:03.1847049Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-08-06T21:24:03.1847629Z which is not supported. The recommended way is as follows: 2024-08-06T21:24:03.1847913Z 2024-08-06T21:24:03.1848036Z 1. the sender creates a RemoteModule; 2024-08-06T21:24:03.1848406Z 2. the sender sends its ``module_rref`` over RPC; 2024-08-06T21:24:03.1848997Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-08-06T21:24:03.1849446Z 2024-08-06T21:24:03.1849551Z Example:: 2024-08-06T21:24:03.1849818Z Run the following code in two different processes: 2024-08-06T21:24:03.1850088Z 2024-08-06T21:24:03.1850204Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:03.1850522Z >>> # On worker 0: 2024-08-06T21:24:03.1850760Z >>> import torch 2024-08-06T21:24:03.1851040Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:03.1851382Z >>> from torch import nn, Tensor 2024-08-06T21:24:03.1851798Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-06T21:24:03.1852662Z >>> 2024-08-06T21:24:03.1852923Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-06T21:24:03.1853276Z >>> remote_module = RemoteModule( 2024-08-06T21:24:03.1853621Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-06T21:24:03.1853933Z >>> ) 2024-08-06T21:24:03.1854136Z >>> 2024-08-06T21:24:03.1854361Z >>> remote_module1 = rpc.rpc_sync( 2024-08-06T21:24:03.1854661Z >>> "worker1/cpu", 2024-08-06T21:24:03.1854959Z >>> RemoteModule.init_from_module_rref, 2024-08-06T21:24:03.1855342Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-08-06T21:24:03.1855682Z >>> ) 2024-08-06T21:24:03.1855897Z >>> rpc.shutdown() 2024-08-06T21:24:03.1856054Z 2024-08-06T21:24:03.1856164Z >>> # On worker 1: 2024-08-06T21:24:03.1856488Z >>> import torch 2024-08-06T21:24:03.1856829Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:03.1857153Z >>> 2024-08-06T21:24:03.1857395Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-06T21:24:03.1857742Z >>> rpc.shutdown() 2024-08-06T21:24:03.1857894Z 2024-08-06T21:24:03.1857990Z Args: 2024-08-06T21:24:03.1858397Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-06T21:24:03.1859101Z The device can be a local device or a remote device specified by one of the following remote 2024-08-06T21:24:03.1859601Z formats: 2024-08-06T21:24:03.1859735Z 2024-08-06T21:24:03.1859874Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-06T21:24:03.1860278Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-06T21:24:03.1860551Z 2024-08-06T21:24:03.1860797Z In addition, the device field can be optional and the default value is "cpu". 2024-08-06T21:24:03.1861416Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-08-06T21:24:03.1861891Z the created remote module. 2024-08-06T21:24:03.1862373Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-06T21:24:03.1863081Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-06T21:24:03.1863648Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-06T21:24:03.1864229Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-06T21:24:03.1864580Z 2024-08-06T21:24:03.1864681Z Returns: 2024-08-06T21:24:03.1865037Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-06T21:24:03.1865633Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-08-06T21:24:03.1866258Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-06T21:24:03.1866866Z on the user-provided module on the remote side. 2024-08-06T21:24:03.1867137Z 2024-08-06T21:24:03.1867388Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1867771Z 2024-08-06T21:24:03.1868418Z 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. 2024-08-06T21:24:03.1869407Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1869783Z 2024-08-06T21:24:03.1870030Z A RemoteModule instance can only be created after RPC initialization. 2024-08-06T21:24:03.1870373Z 2024-08-06T21:24:03.1870570Z It creates a user-specified module on a specified remote node. 2024-08-06T21:24:03.1871121Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-06T21:24:03.1871586Z executed on the remote node. 2024-08-06T21:24:03.1872014Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-06T21:24:03.1872533Z gradients back to the corresponding remote module. 2024-08-06T21:24:03.1872799Z 2024-08-06T21:24:03.1873036Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-08-06T21:24:03.1873620Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-08-06T21:24:03.1874214Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-08-06T21:24:03.1874789Z and ``forward`` are the same as the ``forward`` method of the module 2024-08-06T21:24:03.1875223Z returned by the ``module_cls``. 2024-08-06T21:24:03.1875428Z 2024-08-06T21:24:03.1875629Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-06T21:24:03.1876217Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-08-06T21:24:03.1876824Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-08-06T21:24:03.1877165Z 2024-08-06T21:24:03.1877290Z | ``def forward(input: Tensor) -> Tensor:`` 2024-08-06T21:24:03.1877729Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-08-06T21:24:03.1878004Z 2024-08-06T21:24:03.1878105Z Args: 2024-08-06T21:24:03.1878508Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-06T21:24:03.1879275Z The format should be "/", where the device field can be parsed as torch.device type. 2024-08-06T21:24:03.1879876Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-08-06T21:24:03.1880366Z In addition, the device field can be optional and the default value is "cpu". 2024-08-06T21:24:03.1880982Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-08-06T21:24:03.1881361Z 2024-08-06T21:24:03.1881471Z >>> class MyModule(nn.Module): 2024-08-06T21:24:03.1881789Z >>> def forward(input): 2024-08-06T21:24:03.1882080Z >>> return input + 1 2024-08-06T21:24:03.1882368Z >>> 2024-08-06T21:24:03.1882579Z >>> module_cls = MyModule 2024-08-06T21:24:03.1882778Z 2024-08-06T21:24:03.1882973Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-06T21:24:03.1883483Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-06T21:24:03.1883787Z 2024-08-06T21:24:03.1883943Z Returns: 2024-08-06T21:24:03.1884297Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-06T21:24:03.1884887Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-06T21:24:03.1885509Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-06T21:24:03.1886036Z on the user-provided module on the remote side. 2024-08-06T21:24:03.1886298Z 2024-08-06T21:24:03.1886396Z Example:: 2024-08-06T21:24:03.1886682Z Run the following code in two different processes: 2024-08-06T21:24:03.1886943Z 2024-08-06T21:24:03.1887056Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:03.1887373Z >>> # On worker 0: 2024-08-06T21:24:03.1887628Z >>> import torch 2024-08-06T21:24:03.1887898Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:03.1888251Z >>> from torch import nn, Tensor 2024-08-06T21:24:03.1888685Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-06T21:24:03.1889097Z >>> 2024-08-06T21:24:03.1889355Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-06T21:24:03.1889723Z >>> remote_linear_module = RemoteModule( 2024-08-06T21:24:03.1890065Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-06T21:24:03.1890389Z >>> ) 2024-08-06T21:24:03.1890617Z >>> input = torch.randn(128, 20) 2024-08-06T21:24:03.1890964Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-06T21:24:03.1891337Z >>> ret = ret_fut.wait() 2024-08-06T21:24:03.1891617Z >>> rpc.shutdown() 2024-08-06T21:24:03.1891771Z 2024-08-06T21:24:03.1891861Z >>> # On worker 1: 2024-08-06T21:24:03.1892113Z >>> import torch 2024-08-06T21:24:03.1892391Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:03.1892699Z >>> 2024-08-06T21:24:03.1892991Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-06T21:24:03.1893337Z >>> rpc.shutdown() 2024-08-06T21:24:03.1893489Z 2024-08-06T21:24:03.1893689Z Furthermore, a more practical example that is combined with 2024-08-06T21:24:03.1894490Z `DistributedDataParallel `__ (DDP) 2024-08-06T21:24:03.1895407Z can be found in this `tutorial `__. 2024-08-06T21:24:03.1895849Z 2024-08-06T21:24:03.1896101Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1896476Z 2024-08-06T21:24:03.1974003Z 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. 2024-08-06T21:24:03.1975112Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.1975490Z 2024-08-06T21:24:03.1975740Z Custom reducer class that can be used to specify a custom operation that 2024-08-06T21:24:03.1976265Z reduces losses of multiple microbatches into one value. 2024-08-06T21:24:03.1976553Z 2024-08-06T21:24:03.1976641Z Example: 2024-08-06T21:24:03.1976860Z >>> # xdoctest: +SKIP 2024-08-06T21:24:03.1977113Z >>> sum_reducer = _CustomReducer( 2024-08-06T21:24:03.1977416Z >>> torch.tensor(0.0), 2024-08-06T21:24:03.1977687Z >>> lambda a, b: a + b 2024-08-06T21:24:03.1977937Z >>> ) 2024-08-06T21:24:03.1978057Z 2024-08-06T21:24:03.1978307Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.1978673Z 2024-08-06T21:24:03.2368837Z 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. 2024-08-06T21:24:03.2370171Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.2370553Z 2024-08-06T21:24:03.2370808Z A decorator for a function indicating that the return value of the function 2024-08-06T21:24:03.2371669Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-08-06T21:24:03.2372247Z function can run asynchronously on the RPC callee. More specifically, the 2024-08-06T21:24:03.2372849Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-08-06T21:24:03.2373446Z function and installs subsequent processing steps as a callback to that 2024-08-06T21:24:03.2374025Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-08-06T21:24:03.2374585Z from the :class:`~torch.futures.Future` when completed and send the 2024-08-06T21:24:03.2375089Z value back as the RPC response. That also means the returned 2024-08-06T21:24:03.2375609Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-08-06T21:24:03.2376193Z sent through RPC. This decorator is useful when the wrapped function's 2024-08-06T21:24:03.2376744Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-08-06T21:24:03.2377286Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-08-06T21:24:03.2377641Z 2024-08-06T21:24:03.2377871Z .. note:: To enable asynchronous execution, applications must pass the 2024-08-06T21:24:03.2378435Z function object returned by this decorator to RPC APIs. If RPC detected 2024-08-06T21:24:03.2378999Z attributes installed by this decorator, it knows that this function 2024-08-06T21:24:03.2379523Z returns a ``Future`` object and will handle that accordingly. 2024-08-06T21:24:03.2380041Z However, this does not mean this decorator has to be outmost one when 2024-08-06T21:24:03.2380588Z defining a function. For example, when combined with ``@staticmethod`` 2024-08-06T21:24:03.2381146Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-08-06T21:24:03.2381706Z inner decorator to allow the target function be recognized as a static 2024-08-06T21:24:03.2382356Z or class function. This target function can still execute asynchronously 2024-08-06T21:24:03.2382935Z because, when accessed, the static or class method preserves attributes 2024-08-06T21:24:03.2383451Z installed by ``@rpc.functions.async_execution``. 2024-08-06T21:24:03.2383716Z 2024-08-06T21:24:03.2383721Z 2024-08-06T21:24:03.2383829Z Example:: 2024-08-06T21:24:03.2384153Z The returned :class:`~torch.futures.Future` object can come from 2024-08-06T21:24:03.2384612Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-08-06T21:24:03.2385089Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-08-06T21:24:03.2385604Z constructor. The example below shows directly using the 2024-08-06T21:24:03.2386035Z :class:`~torch.futures.Future` returned by 2024-08-06T21:24:03.2386405Z :meth:`~torch.futures.Future.then`. 2024-08-06T21:24:03.2386675Z 2024-08-06T21:24:03.2386889Z >>> from torch.distributed import rpc 2024-08-06T21:24:03.2387213Z >>> 2024-08-06T21:24:03.2387456Z >>> # omitting setup and shutdown RPC 2024-08-06T21:24:03.2387759Z >>> 2024-08-06T21:24:03.2387978Z >>> # On all workers 2024-08-06T21:24:03.2388273Z >>> @rpc.functions.async_execution 2024-08-06T21:24:03.2388600Z >>> def async_add_chained(to, x, y, z): 2024-08-06T21:24:03.2389022Z >>> # This function runs on "worker1" and returns immediately when 2024-08-06T21:24:03.2389537Z >>> # the callback is installed through the `then(cb)` API. In the 2024-08-06T21:24:03.2390019Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-08-06T21:24:03.2390480Z >>> # When the return value of that `rpc_async` arrives at 2024-08-06T21:24:03.2390941Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-08-06T21:24:03.2391424Z >>> # and set the value for the previously returned `Future`, which 2024-08-06T21:24:03.2391915Z >>> # will then trigger RPC to send the result back to "worker0". 2024-08-06T21:24:03.2392391Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-06T21:24:03.2392776Z >>> lambda fut: fut.wait() + z 2024-08-06T21:24:03.2393161Z >>> ) 2024-08-06T21:24:03.2393380Z >>> 2024-08-06T21:24:03.2393575Z >>> # On worker0 2024-08-06T21:24:03.2393827Z >>> # xdoctest: +SKIP 2024-08-06T21:24:03.2394107Z >>> ret = rpc.rpc_sync( 2024-08-06T21:24:03.2394365Z >>> "worker1", 2024-08-06T21:24:03.2394620Z >>> async_add_chained, 2024-08-06T21:24:03.2394926Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-08-06T21:24:03.2395237Z >>> ) 2024-08-06T21:24:03.2395480Z >>> print(ret) # prints tensor([3., 3.]) 2024-08-06T21:24:03.2395709Z 2024-08-06T21:24:03.2395952Z When combined with TorchScript decorators, this decorator must be the 2024-08-06T21:24:03.2396383Z outmost one. 2024-08-06T21:24:03.2396536Z 2024-08-06T21:24:03.2396638Z >>> from torch import Tensor 2024-08-06T21:24:03.2396953Z >>> from torch.futures import Future 2024-08-06T21:24:03.2397287Z >>> from torch.distributed import rpc 2024-08-06T21:24:03.2397599Z >>> 2024-08-06T21:24:03.2397835Z >>> # omitting setup and shutdown RPC 2024-08-06T21:24:03.2398130Z >>> 2024-08-06T21:24:03.2398339Z >>> # On all workers 2024-08-06T21:24:03.2398601Z >>> @torch.jit.script 2024-08-06T21:24:03.2398907Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-08-06T21:24:03.2399256Z >>> return x + y 2024-08-06T21:24:03.2399506Z >>> 2024-08-06T21:24:03.2399727Z >>> @rpc.functions.async_execution 2024-08-06T21:24:03.2400048Z >>> @torch.jit.script 2024-08-06T21:24:03.2400409Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-08-06T21:24:03.2400846Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-08-06T21:24:03.2401188Z >>> 2024-08-06T21:24:03.2401382Z >>> # On worker0 2024-08-06T21:24:03.2401632Z >>> ret = rpc.rpc_sync( 2024-08-06T21:24:03.2401897Z >>> "worker1", 2024-08-06T21:24:03.2402173Z >>> async_add, 2024-08-06T21:24:03.2402446Z >>> args=("worker2", torch.ones(2), 1) 2024-08-06T21:24:03.2402775Z >>> ) 2024-08-06T21:24:03.2403004Z >>> print(ret) # prints tensor([2., 2.]) 2024-08-06T21:24:03.2403246Z 2024-08-06T21:24:03.2403467Z When combined with static or class method, this decorator must be the 2024-08-06T21:24:03.2403903Z inner one. 2024-08-06T21:24:03.2404035Z 2024-08-06T21:24:03.2404154Z >>> from torch.distributed import rpc 2024-08-06T21:24:03.2404474Z >>> 2024-08-06T21:24:03.2404709Z >>> # omitting setup and shutdown RPC 2024-08-06T21:24:03.2405010Z >>> 2024-08-06T21:24:03.2405220Z >>> # On all workers 2024-08-06T21:24:03.2405497Z >>> class AsyncExecutionClass: 2024-08-06T21:24:03.2405777Z >>> 2024-08-06T21:24:03.2405989Z >>> @staticmethod 2024-08-06T21:24:03.2406316Z >>> @rpc.functions.async_execution 2024-08-06T21:24:03.2406737Z >>> def static_async_add(to, x, y, z): 2024-08-06T21:24:03.2407187Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-06T21:24:03.2407656Z >>> lambda fut: fut.wait() + z 2024-08-06T21:24:03.2407956Z >>> ) 2024-08-06T21:24:03.2408180Z >>> 2024-08-06T21:24:03.2408391Z >>> @classmethod 2024-08-06T21:24:03.2408660Z >>> @rpc.functions.async_execution 2024-08-06T21:24:03.2409008Z >>> def class_async_add(cls, to, x, y, z): 2024-08-06T21:24:03.2409365Z >>> ret_fut = torch.futures.Future() 2024-08-06T21:24:03.2409736Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-06T21:24:03.2410154Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-08-06T21:24:03.2410510Z >>> ) 2024-08-06T21:24:03.2410738Z >>> return ret_fut 2024-08-06T21:24:03.2411010Z >>> 2024-08-06T21:24:03.2411248Z >>> @rpc.functions.async_execution 2024-08-06T21:24:03.2411586Z >>> def bound_async_add(self, to, x, y, z): 2024-08-06T21:24:03.2412000Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-06T21:24:03.2412482Z >>> lambda fut: fut.wait() + z 2024-08-06T21:24:03.2412783Z >>> ) 2024-08-06T21:24:03.2413008Z >>> 2024-08-06T21:24:03.2413216Z >>> # On worker0 2024-08-06T21:24:03.2413457Z >>> ret = rpc.rpc_sync( 2024-08-06T21:24:03.2413731Z >>> "worker1", 2024-08-06T21:24:03.2414009Z >>> AsyncExecutionClass.static_async_add, 2024-08-06T21:24:03.2414379Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-06T21:24:03.2414701Z >>> ) 2024-08-06T21:24:03.2414932Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:03.2415256Z >>> 2024-08-06T21:24:03.2415483Z >>> ret = rpc.rpc_sync( 2024-08-06T21:24:03.2415743Z >>> "worker1", 2024-08-06T21:24:03.2416038Z >>> AsyncExecutionClass.class_async_add, 2024-08-06T21:24:03.2416407Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-06T21:24:03.2416719Z >>> ) 2024-08-06T21:24:03.2416965Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:03.2417195Z 2024-08-06T21:24:03.2417378Z This decorator also works with RRef helpers, i.e., . 2024-08-06T21:24:03.2417785Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-08-06T21:24:03.2418206Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-08-06T21:24:03.2418621Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-08-06T21:24:03.2418867Z 2024-08-06T21:24:03.2418988Z >>> from torch.distributed import rpc 2024-08-06T21:24:03.2419310Z >>> 2024-08-06T21:24:03.2419567Z >>> # reuse the AsyncExecutionClass class above 2024-08-06T21:24:03.2419955Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-06T21:24:03.2420437Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-08-06T21:24:03.2420896Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:03.2421202Z >>> 2024-08-06T21:24:03.2421473Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-06T21:24:03.2422032Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-08-06T21:24:03.2422498Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:03.2422817Z >>> 2024-08-06T21:24:03.2423086Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-06T21:24:03.2423573Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-08-06T21:24:03.2424051Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:03.2424284Z 2024-08-06T21:24:03.2424534Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.2424903Z 2024-08-06T21:24:03.2425658Z 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. 2024-08-06T21:24:03.2426849Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.2427233Z 2024-08-06T21:24:03.2427437Z Set device mapping between each RPC caller and callee pair. This 2024-08-06T21:24:03.2427953Z function can be called multiple times to incrementally add 2024-08-06T21:24:03.2428370Z device placement configurations. 2024-08-06T21:24:03.2428573Z 2024-08-06T21:24:03.2428656Z Args: 2024-08-06T21:24:03.2428881Z to (str): Callee name. 2024-08-06T21:24:03.2429257Z device_map (Dict of int, str, or torch.device): Device placement 2024-08-06T21:24:03.2429745Z mappings from this worker to the callee. This map must be 2024-08-06T21:24:03.2430135Z invertible. 2024-08-06T21:24:03.2430273Z 2024-08-06T21:24:03.2430371Z Example: 2024-08-06T21:24:03.2430598Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:03.2430913Z >>> # both workers 2024-08-06T21:24:03.2431163Z >>> def add(x, y): 2024-08-06T21:24:03.2431443Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-08-06T21:24:03.2431801Z >>> return x + y, (x + y).to(2) 2024-08-06T21:24:03.2432101Z >>> 2024-08-06T21:24:03.2432298Z >>> # on worker 0 2024-08-06T21:24:03.2432654Z >>> options = TensorPipeRpcBackendOptions( 2024-08-06T21:24:03.2432996Z >>> num_worker_threads=8, 2024-08-06T21:24:03.2433308Z >>> device_maps={"worker1": {0: 1}} 2024-08-06T21:24:03.2433668Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-08-06T21:24:03.2433992Z >>> ) 2024-08-06T21:24:03.2434258Z >>> options.set_device_map("worker1", {1: 2}) 2024-08-06T21:24:03.2434631Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-08-06T21:24:03.2434950Z >>> 2024-08-06T21:24:03.2435163Z >>> rpc.init_rpc( 2024-08-06T21:24:03.2435411Z >>> "worker0", 2024-08-06T21:24:03.2435639Z >>> rank=0, 2024-08-06T21:24:03.2435873Z >>> world_size=2, 2024-08-06T21:24:03.2436168Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-08-06T21:24:03.2436510Z >>> rpc_backend_options=options 2024-08-06T21:24:03.2436812Z >>> ) 2024-08-06T21:24:03.2437018Z >>> 2024-08-06T21:24:03.2437218Z >>> x = torch.ones(2) 2024-08-06T21:24:03.2437548Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-08-06T21:24:03.2438007Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-08-06T21:24:03.2438488Z >>> # sending the return value back, it will follow the invert of 2024-08-06T21:24:03.2438968Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-08-06T21:24:03.2439362Z >>> # cuda:1 on worker0 2024-08-06T21:24:03.2439673Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-08-06T21:24:03.2440078Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-08-06T21:24:03.2440332Z 2024-08-06T21:24:03.2440594Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.2440966Z 2024-08-06T21:24:03.2615328Z 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. 2024-08-06T21:24:03.2616623Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.2617084Z 2024-08-06T21:24:03.2617480Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-08-06T21:24:03.2618412Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-08-06T21:24:03.2618847Z 2024-08-06T21:24:03.2618952Z Keyword Args: 2024-08-06T21:24:03.2619334Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-06T21:24:03.2620104Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-08-06T21:24:03.2621319Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-08-06T21:24:03.2622017Z as a placeholder. default: None. 2024-08-06T21:24:03.2622475Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-06T21:24:03.2623217Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-06T21:24:03.2624100Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-08-06T21:24:03.2624757Z input_kwarg_layouts (Dict[str, Placement]): 2024-08-06T21:24:03.2625386Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-08-06T21:24:03.2625978Z default: None 2024-08-06T21:24:03.2626285Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-08-06T21:24:03.2627029Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-06T21:24:03.2627674Z have the desired DTensor layouts. default: None. 2024-08-06T21:24:03.2628038Z use_local_output (bool, optional): 2024-08-06T21:24:03.2628625Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-08-06T21:24:03.2629303Z Returns: 2024-08-06T21:24:03.2629735Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-08-06T21:24:03.2630189Z 2024-08-06T21:24:03.2630302Z Example:: 2024-08-06T21:24:03.2630537Z >>> # xdoctest: +SKIP(failing) 2024-08-06T21:24:03.2631045Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-08-06T21:24:03.2631674Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-06T21:24:03.2632073Z >>> ... 2024-08-06T21:24:03.2632494Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-06T21:24:03.2633049Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-06T21:24:03.2633379Z >>> 2024-08-06T21:24:03.2633829Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-08-06T21:24:03.2634442Z >>> # and then redistributed to Replicated DTensor. 2024-08-06T21:24:03.2634813Z >>> parallelize_module( 2024-08-06T21:24:03.2635118Z >>> block, # this can be a submodule or module 2024-08-06T21:24:03.2635461Z >>> tp_mesh, 2024-08-06T21:24:03.2635722Z >>> parallelize_plan={ 2024-08-06T21:24:03.2636019Z >>> "attn": PrepareModuleInput( 2024-08-06T21:24:03.2636390Z >>> input_layouts=(Shard(0), None, None, ...), 2024-08-06T21:24:03.2636809Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-08-06T21:24:03.2637177Z >>> ), 2024-08-06T21:24:03.2637408Z >>> } 2024-08-06T21:24:03.2637626Z >>> ) 2024-08-06T21:24:03.2637743Z 2024-08-06T21:24:03.2637998Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.2638378Z 2024-08-06T21:24:03.2639061Z 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. 2024-08-06T21:24:03.2640155Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.2640530Z 2024-08-06T21:24:03.2640934Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-08-06T21:24:03.2641766Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-08-06T21:24:03.2642229Z 2024-08-06T21:24:03.2642321Z Keyword Args: 2024-08-06T21:24:03.2642846Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-06T21:24:03.2643467Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-08-06T21:24:03.2644358Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-08-06T21:24:03.2645000Z ``None`` need to be specified as a placeholder. 2024-08-06T21:24:03.2645458Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-06T21:24:03.2646154Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-08-06T21:24:03.2646782Z have the desired DTensor layouts. 2024-08-06T21:24:03.2647123Z use_local_output (bool, optional): 2024-08-06T21:24:03.2647693Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-08-06T21:24:03.2648262Z Returns: 2024-08-06T21:24:03.2648685Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-08-06T21:24:03.2649096Z 2024-08-06T21:24:03.2649190Z Example:: 2024-08-06T21:24:03.2649422Z >>> # xdoctest: +SKIP(failing) 2024-08-06T21:24:03.2649945Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-08-06T21:24:03.2650564Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-06T21:24:03.2650951Z >>> ... 2024-08-06T21:24:03.2651458Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-06T21:24:03.2651998Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-06T21:24:03.2652322Z >>> 2024-08-06T21:24:03.2652843Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-08-06T21:24:03.2653487Z >>> # and then redistributed to Sharded DTensor. 2024-08-06T21:24:03.2653847Z >>> parallelize_module( 2024-08-06T21:24:03.2654169Z >>> block, # this can be a submodule or module 2024-08-06T21:24:03.2654495Z >>> tp_mesh, 2024-08-06T21:24:03.2654783Z >>> parallelize_plan = PrepareModuleOutput( 2024-08-06T21:24:03.2655157Z >>> output_layouts=Replicate(), 2024-08-06T21:24:03.2655490Z >>> desired_output_layouts=Shard(0) 2024-08-06T21:24:03.2655810Z >>> ) 2024-08-06T21:24:03.2656032Z >>> ) 2024-08-06T21:24:03.2656147Z 2024-08-06T21:24:03.2656403Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.2656780Z 2024-08-06T21:24:03.4810247Z 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=230. 2024-08-06T21:24:03.4811336Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.4812230Z Return a new dict with new, potentially nested, key value pair 2024-08-06T21:24:03.4812715Z 2024-08-06T21:24:03.4812836Z >>> purchase = {'name': 'Alice', 2024-08-06T21:24:03.4813161Z ... 'order': {'items': ['Apple', 'Orange'], 2024-08-06T21:24:03.4813543Z ... 'costs': [0.50, 1.25]}, 2024-08-06T21:24:03.4813889Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-06T21:24:03.4814665Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-08-06T21:24:03.4815477Z {'credit card': '5555-1234-1234-1234', 2024-08-06T21:24:03.4815844Z 'name': 'Alice', 2024-08-06T21:24:03.4816152Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-08-06T21:24:03.4816520Z 2024-08-06T21:24:03.4816930Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.4817298Z 2024-08-06T21:24:03.4818051Z 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=245. 2024-08-06T21:24:03.4819173Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.4819706Z Update value in a (potentially) nested dictionary 2024-08-06T21:24:03.4820053Z 2024-08-06T21:24:03.4820142Z inputs: 2024-08-06T21:24:03.4820389Z d - dictionary on which to operate 2024-08-06T21:24:03.4820834Z keys - list or tuple giving the location of the value to be changed in d 2024-08-06T21:24:03.4821284Z func - function to operate on that value 2024-08-06T21:24:03.4821533Z 2024-08-06T21:24:03.4821723Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-08-06T21:24:03.4822264Z original dictionary with v replaced by func(v), but does not mutate the 2024-08-06T21:24:03.4822718Z original dictionary. 2024-08-06T21:24:03.4822880Z 2024-08-06T21:24:03.4823087Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-08-06T21:24:03.4823633Z specified by the keys, with the innermost value set to func(default). 2024-08-06T21:24:03.4823638Z 2024-08-06T21:24:03.4823739Z >>> inc = lambda x: x + 1 2024-08-06T21:24:03.4823860Z >>> update_in({'a': 0}, ['a'], inc) 2024-08-06T21:24:03.4823951Z {'a': 1} 2024-08-06T21:24:03.4823956Z 2024-08-06T21:24:03.4824065Z >>> transaction = {'name': 'Alice', 2024-08-06T21:24:03.4824216Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-06T21:24:03.4824334Z ... 'costs': [0.50, 1.25]}, 2024-08-06T21:24:03.4824562Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-06T21:24:03.4824792Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-08-06T21:24:03.4824897Z {'credit card': '5555-1234-1234-1234', 2024-08-06T21:24:03.4825003Z 'name': 'Alice', 2024-08-06T21:24:03.4825168Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-08-06T21:24:03.4825173Z 2024-08-06T21:24:03.4825290Z >>> # updating a value when k0 is not in d 2024-08-06T21:24:03.4825428Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-08-06T21:24:03.4825515Z {1: {2: {3: 'bar'}}} 2024-08-06T21:24:03.4825628Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-08-06T21:24:03.4825737Z {1: 'foo', 2: {3: {4: 1}}} 2024-08-06T21:24:03.4825820Z 2024-08-06T21:24:03.4826073Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.4826081Z 2024-08-06T21:24:03.4827037Z 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=303. 2024-08-06T21:24:03.4827312Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.4827489Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-08-06T21:24:03.4827494Z 2024-08-06T21:24:03.4827673Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-08-06T21:24:03.4827875Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-08-06T21:24:03.4827879Z 2024-08-06T21:24:03.4828099Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-08-06T21:24:03.4828235Z structures such as dictionaries and lists. 2024-08-06T21:24:03.4828239Z 2024-08-06T21:24:03.4828361Z >>> transaction = {'name': 'Alice', 2024-08-06T21:24:03.4828542Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-06T21:24:03.4828657Z ... 'costs': [0.50, 1.25]}, 2024-08-06T21:24:03.4828793Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-06T21:24:03.4828930Z >>> get_in(['purchase', 'items', 0], transaction) 2024-08-06T21:24:03.4829017Z 'Apple' 2024-08-06T21:24:03.4829137Z >>> get_in(['name'], transaction) 2024-08-06T21:24:03.4829223Z 'Alice' 2024-08-06T21:24:03.4829353Z >>> get_in(['purchase', 'total'], transaction) 2024-08-06T21:24:03.4829514Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-08-06T21:24:03.4829649Z >>> get_in(['purchase', 'items', 10], transaction) 2024-08-06T21:24:03.4829786Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-08-06T21:24:03.4829886Z 0 2024-08-06T21:24:03.4830033Z >>> get_in(['y'], {}, no_default=True) 2024-08-06T21:24:03.4830146Z Traceback (most recent call last): 2024-08-06T21:24:03.4830247Z ... 2024-08-06T21:24:03.4830342Z KeyError: 'y' 2024-08-06T21:24:03.4830346Z 2024-08-06T21:24:03.4830435Z See Also: 2024-08-06T21:24:03.4830544Z itertoolz.get 2024-08-06T21:24:03.4830648Z operator.getitem 2024-08-06T21:24:03.4830732Z 2024-08-06T21:24:03.4830999Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.4831004Z 2024-08-06T21:24:03.4831658Z 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=355. 2024-08-06T21:24:03.4831936Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.4832048Z Group a collection by a key function 2024-08-06T21:24:03.4832052Z 2024-08-06T21:24:03.4832217Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-08-06T21:24:03.4832355Z >>> groupby(len, names) # doctest: +SKIP 2024-08-06T21:24:03.4832517Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-08-06T21:24:03.4832523Z 2024-08-06T21:24:03.4832628Z >>> iseven = lambda x: x % 2 == 0 2024-08-06T21:24:03.4832873Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-08-06T21:24:03.4832987Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-08-06T21:24:03.4832991Z 2024-08-06T21:24:03.4833141Z Non-callable keys imply grouping on a member. 2024-08-06T21:24:03.4833145Z 2024-08-06T21:24:03.4833295Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-08-06T21:24:03.4833419Z ... {'name': 'Bob', 'gender': 'M'}, 2024-08-06T21:24:03.4833582Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-08-06T21:24:03.4833692Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-08-06T21:24:03.4833799Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-08-06T21:24:03.4833922Z {'gender': 'M', 'name': 'Charlie'}]} 2024-08-06T21:24:03.4833927Z 2024-08-06T21:24:03.4834065Z Not to be confused with ``itertools.groupby`` 2024-08-06T21:24:03.4834072Z 2024-08-06T21:24:03.4834173Z See Also: 2024-08-06T21:24:03.4834260Z countby 2024-08-06T21:24:03.4834346Z 2024-08-06T21:24:03.4834614Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.4834618Z 2024-08-06T21:24:03.7364124Z 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. 2024-08-06T21:24:03.7365540Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.7366510Z Applies Batch Normalization over a N-Dimensional input. 2024-08-06T21:24:03.7367024Z 2024-08-06T21:24:03.7367584Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-08-06T21:24:03.7368787Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-08-06T21:24:03.7369840Z Internal Covariate Shift `__ . 2024-08-06T21:24:03.7370840Z 2024-08-06T21:24:03.7371034Z .. math:: 2024-08-06T21:24:03.7371283Z 2024-08-06T21:24:03.7371711Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-08-06T21:24:03.7372333Z 2024-08-06T21:24:03.7372693Z The mean and standard-deviation are calculated per-dimension over all 2024-08-06T21:24:03.7373750Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-08-06T21:24:03.7374886Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-08-06T21:24:03.7375899Z By default, the elements of :math:`\gamma` are sampled from 2024-08-06T21:24:03.7376813Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-08-06T21:24:03.7377903Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-08-06T21:24:03.7378977Z `torch.var(input, unbiased=False)`. 2024-08-06T21:24:03.7379384Z 2024-08-06T21:24:03.7379850Z Also by default, during training this layer keeps running estimates of its 2024-08-06T21:24:03.7380950Z computed mean and variance, which are then used for normalization during 2024-08-06T21:24:03.7382089Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-08-06T21:24:03.7382957Z of 0.1. 2024-08-06T21:24:03.7383178Z 2024-08-06T21:24:03.7383596Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-08-06T21:24:03.7384668Z keep running estimates, and batch statistics are instead used during 2024-08-06T21:24:03.7385510Z evaluation time as well. 2024-08-06T21:24:03.7385843Z 2024-08-06T21:24:03.7386006Z .. note:: 2024-08-06T21:24:03.7386645Z This :attr:`momentum` argument is different from one used in optimizer 2024-08-06T21:24:03.7387809Z classes and the conventional notion of momentum. Mathematically, the 2024-08-06T21:24:03.7388687Z update rule for running statistics here is 2024-08-06T21:24:03.7389652Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-08-06T21:24:03.7390970Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-08-06T21:24:03.7391901Z new observed value. 2024-08-06T21:24:03.7392245Z 2024-08-06T21:24:03.7392816Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-08-06T21:24:03.7394082Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-08-06T21:24:03.7395107Z Normalization or Spatio-temporal Batch Normalization. 2024-08-06T21:24:03.7395643Z 2024-08-06T21:24:03.7395909Z Currently :class:`SyncBatchNorm` only supports 2024-08-06T21:24:03.7396925Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-08-06T21:24:03.7398095Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-08-06T21:24:03.7399089Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-08-06T21:24:03.7399884Z Network with DDP. 2024-08-06T21:24:03.7400173Z 2024-08-06T21:24:03.7400348Z Args: 2024-08-06T21:24:03.7400852Z num_features: :math:`C` from an expected input of size 2024-08-06T21:24:03.7401544Z :math:`(N, C, +)` 2024-08-06T21:24:03.7402204Z eps: a value added to the denominator for numerical stability. 2024-08-06T21:24:03.7402948Z Default: ``1e-5`` 2024-08-06T21:24:03.7403634Z momentum: the value used for the running_mean and running_var 2024-08-06T21:24:03.7404610Z computation. Can be set to ``None`` for cumulative moving average 2024-08-06T21:24:03.7405418Z (i.e. simple average). Default: 0.1 2024-08-06T21:24:03.7406221Z affine: a boolean value that when set to ``True``, this module has 2024-08-06T21:24:03.7407104Z learnable affine parameters. Default: ``True`` 2024-08-06T21:24:03.7407989Z track_running_stats: a boolean value that when set to ``True``, this 2024-08-06T21:24:03.7409161Z module tracks the running mean and variance, and when set to ``False``, 2024-08-06T21:24:03.7410272Z this module does not track such statistics, and initializes statistics 2024-08-06T21:24:03.7411288Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-08-06T21:24:03.7412289Z When these buffers are ``None``, this module always uses batch statistics. 2024-08-06T21:24:03.7413238Z in both training and eval modes. Default: ``True`` 2024-08-06T21:24:03.7414200Z process_group: synchronization of stats happen within each process group 2024-08-06T21:24:03.7415302Z individually. Default behavior is synchronization across the whole 2024-08-06T21:24:03.7416110Z world 2024-08-06T21:24:03.7416444Z 2024-08-06T21:24:03.7416619Z Shape: 2024-08-06T21:24:03.7417007Z - Input: :math:`(N, C, +)` 2024-08-06T21:24:03.7417656Z - Output: :math:`(N, C, +)` (same shape as input) 2024-08-06T21:24:03.7418142Z 2024-08-06T21:24:03.7418321Z .. note:: 2024-08-06T21:24:03.7419017Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-08-06T21:24:03.7420111Z synchronization is disabled when ``model.eval()`` is set or if 2024-08-06T21:24:03.7420935Z ``self.training`` is otherwise ``False``. 2024-08-06T21:24:03.7421399Z 2024-08-06T21:24:03.7421561Z Examples:: 2024-08-06T21:24:03.7421802Z 2024-08-06T21:24:03.7422009Z >>> # xdoctest: +SKIP 2024-08-06T21:24:03.7422527Z >>> # With Learnable Parameters 2024-08-06T21:24:03.7423128Z >>> m = nn.SyncBatchNorm(100) 2024-08-06T21:24:03.7423743Z >>> # creating process group (optional) 2024-08-06T21:24:03.7424430Z >>> # ranks is a list of int identifying rank ids. 2024-08-06T21:24:03.7425088Z >>> ranks = list(range(8)) 2024-08-06T21:24:03.7425630Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-06T21:24:03.7426214Z >>> # Note: every rank calls into new_group for every 2024-08-06T21:24:03.7427103Z >>> # process group created, even if that rank is not 2024-08-06T21:24:03.7427927Z >>> # part of the group. 2024-08-06T21:24:03.7428728Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-06T21:24:03.7429795Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-06T21:24:03.7430625Z >>> # Without Learnable Parameters 2024-08-06T21:24:03.7431402Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-08-06T21:24:03.7432215Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-08-06T21:24:03.7432824Z >>> output = m(input) 2024-08-06T21:24:03.7433151Z 2024-08-06T21:24:03.7433365Z >>> # network is nn.BatchNorm layer 2024-08-06T21:24:03.7434294Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-08-06T21:24:03.7435346Z >>> # only single gpu per process is currently supported 2024-08-06T21:24:03.7436235Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-08-06T21:24:03.7437072Z >>> sync_bn_network, 2024-08-06T21:24:03.7437722Z >>> device_ids=[args.local_rank], 2024-08-06T21:24:03.7438401Z >>> output_device=args.local_rank) 2024-08-06T21:24:03.7438991Z 2024-08-06T21:24:03.7439668Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.7440366Z 2024-08-06T21:24:03.7441639Z 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. 2024-08-06T21:24:03.7443792Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.7445085Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-08-06T21:24:03.7446018Z 2024-08-06T21:24:03.7446183Z Args: 2024-08-06T21:24:03.7446853Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-08-06T21:24:03.7447914Z process_group (optional): process group to scope synchronization, 2024-08-06T21:24:03.7448759Z default is the whole world 2024-08-06T21:24:03.7449153Z 2024-08-06T21:24:03.7449310Z Returns: 2024-08-06T21:24:03.7450014Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-08-06T21:24:03.7451121Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-08-06T21:24:03.7452157Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-08-06T21:24:03.7452918Z instead. 2024-08-06T21:24:03.7453313Z 2024-08-06T21:24:03.7453485Z Example:: 2024-08-06T21:24:03.7453739Z 2024-08-06T21:24:03.7453962Z >>> # Network with nn.BatchNorm layer 2024-08-06T21:24:03.7454651Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:03.7455334Z >>> module = torch.nn.Sequential( 2024-08-06T21:24:03.7455965Z >>> torch.nn.Linear(20, 100), 2024-08-06T21:24:03.7456577Z >>> torch.nn.BatchNorm1d(100), 2024-08-06T21:24:03.7457186Z >>> ).cuda() 2024-08-06T21:24:03.7457753Z >>> # creating process group (optional) 2024-08-06T21:24:03.7458421Z >>> # ranks is a list of int identifying rank ids. 2024-08-06T21:24:03.7459094Z >>> ranks = list(range(8)) 2024-08-06T21:24:03.7459679Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-06T21:24:03.7460335Z >>> # Note: every rank calls into new_group for every 2024-08-06T21:24:03.7461116Z >>> # process group created, even if that rank is not 2024-08-06T21:24:03.7461811Z >>> # part of the group. 2024-08-06T21:24:03.7462360Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:03.7463247Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-06T21:24:03.7464510Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-06T21:24:03.7465684Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-08-06T21:24:03.7466495Z 2024-08-06T21:24:03.7466644Z 2024-08-06T21:24:03.7467449Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.7468147Z 2024-08-06T21:24:03.7616070Z 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. 2024-08-06T21:24:03.7616987Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.7617395Z 2024-08-06T21:24:03.7617704Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-08-06T21:24:03.7618143Z 2024-08-06T21:24:03.7618434Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-08-06T21:24:03.7619058Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-08-06T21:24:03.7619412Z 2024-08-06T21:24:03.7619729Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-08-06T21:24:03.7620419Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-08-06T21:24:03.7620962Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-08-06T21:24:03.7621238Z 2024-08-06T21:24:03.7621324Z Shape: 2024-08-06T21:24:03.7621666Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-08-06T21:24:03.7622249Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-08-06T21:24:03.7622828Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-08-06T21:24:03.7623441Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-08-06T21:24:03.7623677Z 2024-08-06T21:24:03.7623779Z Args: 2024-08-06T21:24:03.7624034Z dim (Union[int, str]): Dimension to be unflattened 2024-08-06T21:24:03.7624645Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-08-06T21:24:03.7625103Z 2024-08-06T21:24:03.7625207Z Examples: 2024-08-06T21:24:03.7625427Z >>> input = torch.randn(2, 50) 2024-08-06T21:24:03.7625736Z >>> # With tuple of ints 2024-08-06T21:24:03.7626016Z >>> m = nn.Sequential( 2024-08-06T21:24:03.7626293Z >>> nn.Linear(50, 50), 2024-08-06T21:24:03.7626588Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-08-06T21:24:03.7626965Z >>> ) 2024-08-06T21:24:03.7627179Z >>> output = m(input) 2024-08-06T21:24:03.7627519Z >>> output.size() 2024-08-06T21:24:03.7627778Z torch.Size([2, 2, 5, 5]) 2024-08-06T21:24:03.7628043Z >>> # With torch.Size 2024-08-06T21:24:03.7628315Z >>> m = nn.Sequential( 2024-08-06T21:24:03.7628587Z >>> nn.Linear(50, 50), 2024-08-06T21:24:03.7628884Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-08-06T21:24:03.7629208Z >>> ) 2024-08-06T21:24:03.7629427Z >>> output = m(input) 2024-08-06T21:24:03.7629680Z >>> output.size() 2024-08-06T21:24:03.7629936Z torch.Size([2, 2, 5, 5]) 2024-08-06T21:24:03.7630235Z >>> # With namedshape (tuple of tuples) 2024-08-06T21:24:03.7630603Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-08-06T21:24:03.7631077Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-08-06T21:24:03.7631516Z >>> output = unflatten(input) 2024-08-06T21:24:03.7631803Z >>> output.size() 2024-08-06T21:24:03.7632064Z torch.Size([2, 2, 5, 5]) 2024-08-06T21:24:03.7632242Z 2024-08-06T21:24:03.7632514Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.7632883Z 2024-08-06T21:24:03.7960886Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-08-06T21:24:03.7961975Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.7962615Z Creates a criterion that measures the triplet loss given input 2024-08-06T21:24:03.7963155Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-08-06T21:24:03.7963713Z positive, and negative examples, respectively), and a nonnegative, 2024-08-06T21:24:03.7964355Z real-valued function ("distance function") used to compute the relationship 2024-08-06T21:24:03.7965005Z between the anchor and positive example ("positive distance") and the 2024-08-06T21:24:03.7965539Z anchor and negative example ("negative distance"). 2024-08-06T21:24:03.7965822Z 2024-08-06T21:24:03.7966034Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-08-06T21:24:03.7966533Z can be described as: 2024-08-06T21:24:03.7966703Z 2024-08-06T21:24:03.7966808Z .. math:: 2024-08-06T21:24:03.7967135Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-08-06T21:24:03.7981471Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-08-06T21:24:03.7981843Z 2024-08-06T21:24:03.7982100Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-08-06T21:24:03.7982817Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-08-06T21:24:03.7983524Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-08-06T21:24:03.7984180Z between the positive and negative distances that is required for the loss to 2024-08-06T21:24:03.7984807Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-08-06T21:24:03.7985273Z that the distance function can handle. 2024-08-06T21:24:03.7985554Z 2024-08-06T21:24:03.7985670Z If :attr:`reduction` is not ``'none'`` 2024-08-06T21:24:03.7986155Z (default ``'mean'``), then: 2024-08-06T21:24:03.7986359Z 2024-08-06T21:24:03.7986459Z .. math:: 2024-08-06T21:24:03.7986677Z \ell(x, y) = 2024-08-06T21:24:03.7987020Z \begin{cases} 2024-08-06T21:24:03.7987408Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-08-06T21:24:03.7987978Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-08-06T21:24:03.7988398Z \end{cases} 2024-08-06T21:24:03.7988577Z 2024-08-06T21:24:03.7988816Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-08-06T21:24:03.7989474Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-08-06T21:24:03.7989842Z 2024-08-06T21:24:03.7989974Z Args: 2024-08-06T21:24:03.7990456Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-08-06T21:24:03.7991111Z quantifies the closeness of two tensors. If not specified, 2024-08-06T21:24:03.7991646Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-08-06T21:24:03.7992192Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-08-06T21:24:03.7992901Z between the positive and negative distances required for the loss to be 0. Larger 2024-08-06T21:24:03.7993625Z margins penalize cases where the negative examples are not distant enough from the 2024-08-06T21:24:03.7994249Z anchors, relative to the positives. Default: :math:`1`. 2024-08-06T21:24:03.7994823Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-08-06T21:24:03.7995513Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-08-06T21:24:03.7996185Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-08-06T21:24:03.7996816Z negative example than the anchor is, swaps the positive example and the anchor in 2024-08-06T21:24:03.7997379Z the loss computation. Default: ``False``. 2024-08-06T21:24:03.7998047Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-08-06T21:24:03.7998628Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-08-06T21:24:03.7999128Z ``'mean'``: the sum of the output will be divided by the number of 2024-08-06T21:24:03.7999719Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-08-06T21:24:03.8000075Z 2024-08-06T21:24:03.8000079Z 2024-08-06T21:24:03.8000182Z Shape: 2024-08-06T21:24:03.8000545Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-08-06T21:24:03.8001041Z as supported by the distance function. 2024-08-06T21:24:03.8001595Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-08-06T21:24:03.8002054Z otherwise. 2024-08-06T21:24:03.8002218Z 2024-08-06T21:24:03.8002317Z Examples:: 2024-08-06T21:24:03.8002456Z 2024-08-06T21:24:03.8002579Z >>> # Initialize embeddings 2024-08-06T21:24:03.8002878Z >>> embedding = nn.Embedding(1000, 128) 2024-08-06T21:24:03.8003235Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-08-06T21:24:03.8003605Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-08-06T21:24:03.8003967Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-08-06T21:24:03.8004324Z >>> anchor = embedding(anchor_ids) 2024-08-06T21:24:03.8004664Z >>> positive = embedding(positive_ids) 2024-08-06T21:24:03.8004995Z >>> negative = embedding(negative_ids) 2024-08-06T21:24:03.8005368Z >>> 2024-08-06T21:24:03.8005599Z >>> # Built-in Distance Function 2024-08-06T21:24:03.8005899Z >>> triplet_loss = \ 2024-08-06T21:24:03.8006352Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-08-06T21:24:03.8006955Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-06T21:24:03.8007316Z >>> output.backward() 2024-08-06T21:24:03.8007582Z >>> 2024-08-06T21:24:03.8007814Z >>> # Custom Distance Function 2024-08-06T21:24:03.8008108Z >>> def l_infinity(x1, x2): 2024-08-06T21:24:03.8008456Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-08-06T21:24:03.8008814Z >>> 2024-08-06T21:24:03.8009117Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-08-06T21:24:03.8009526Z >>> triplet_loss = ( 2024-08-06T21:24:03.8009982Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-08-06T21:24:03.8010525Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-06T21:24:03.8010898Z >>> output.backward() 2024-08-06T21:24:03.8011194Z >>> 2024-08-06T21:24:03.8011421Z >>> # Custom Distance Function (Lambda) 2024-08-06T21:24:03.8011748Z >>> triplet_loss = ( 2024-08-06T21:24:03.8012066Z >>> nn.TripletMarginWithDistanceLoss( 2024-08-06T21:24:03.8012576Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-08-06T21:24:03.8013064Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-06T21:24:03.8013425Z >>> output.backward() 2024-08-06T21:24:03.8013606Z 2024-08-06T21:24:03.8013698Z Reference: 2024-08-06T21:24:03.8014145Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-08-06T21:24:03.8014772Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-08-06T21:24:03.8015146Z 2024-08-06T21:24:03.8015525Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-08-06T21:24:03.8015897Z 2024-08-06T21:24:03.8541509Z 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. 2024-08-06T21:24:03.8542668Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.8543228Z Computes a partial inverse of :class:`MaxPool2d`. 2024-08-06T21:24:03.8543556Z 2024-08-06T21:24:03.8544065Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-08-06T21:24:03.8544470Z 2024-08-06T21:24:03.8544712Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-08-06T21:24:03.8545354Z including the indices of the maximal values and computes a partial inverse 2024-08-06T21:24:03.8545927Z in which all non-maximal values are set to zero. 2024-08-06T21:24:03.8546180Z 2024-08-06T21:24:03.8546286Z Note: 2024-08-06T21:24:03.8546847Z This operation may behave nondeterministically when the input indices has repeat values. 2024-08-06T21:24:03.8547736Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-08-06T21:24:03.8548288Z 2024-08-06T21:24:03.8548548Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-08-06T21:24:03.8549123Z sizes. Hence, the inversion process can get ambiguous. 2024-08-06T21:24:03.8549668Z To accommodate this, you can provide the needed output size 2024-08-06T21:24:03.8550243Z as an additional argument :attr:`output_size` in the forward call. 2024-08-06T21:24:03.8550702Z See the Inputs and Example below. 2024-08-06T21:24:03.8551002Z 2024-08-06T21:24:03.8551092Z Args: 2024-08-06T21:24:03.8551406Z kernel_size (int or tuple): Size of the max pooling window. 2024-08-06T21:24:03.8551929Z stride (int or tuple): Stride of the max pooling window. 2024-08-06T21:24:03.8552341Z It is set to :attr:`kernel_size` by default. 2024-08-06T21:24:03.8552835Z padding (int or tuple): Padding that was added to the input 2024-08-06T21:24:03.8553156Z 2024-08-06T21:24:03.8553292Z Inputs: 2024-08-06T21:24:03.8553551Z - `input`: the input Tensor to invert 2024-08-06T21:24:03.8554111Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-08-06T21:24:03.8554602Z - `output_size` (optional): the targeted output size 2024-08-06T21:24:03.8554943Z 2024-08-06T21:24:03.8555032Z Shape: 2024-08-06T21:24:03.8555351Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-08-06T21:24:03.8555912Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-08-06T21:24:03.8556260Z 2024-08-06T21:24:03.8556396Z .. math:: 2024-08-06T21:24:03.8556810Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-08-06T21:24:03.8557246Z 2024-08-06T21:24:03.8557334Z .. math:: 2024-08-06T21:24:03.8557736Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-08-06T21:24:03.8558218Z 2024-08-06T21:24:03.8558396Z or as given by :attr:`output_size` in the call operator 2024-08-06T21:24:03.8558730Z 2024-08-06T21:24:03.8558821Z Example:: 2024-08-06T21:24:03.8558964Z 2024-08-06T21:24:03.8559133Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-08-06T21:24:03.8559590Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-08-06T21:24:03.8559959Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-08-06T21:24:03.8560360Z [ 5., 6., 7., 8.], 2024-08-06T21:24:03.8560695Z [ 9., 10., 11., 12.], 2024-08-06T21:24:03.8561081Z [13., 14., 15., 16.]]]]) 2024-08-06T21:24:03.8561411Z >>> output, indices = pool(input) 2024-08-06T21:24:03.8561792Z >>> unpool(output, indices) 2024-08-06T21:24:03.8562104Z tensor([[[[ 0., 0., 0., 0.], 2024-08-06T21:24:03.8562441Z [ 0., 6., 0., 8.], 2024-08-06T21:24:03.8562766Z [ 0., 0., 0., 0.], 2024-08-06T21:24:03.8563076Z [ 0., 14., 0., 16.]]]]) 2024-08-06T21:24:03.8563625Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-08-06T21:24:03.8564150Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-08-06T21:24:03.8564517Z [ 6., 7., 8., 9., 10.], 2024-08-06T21:24:03.8564896Z [11., 12., 13., 14., 15.], 2024-08-06T21:24:03.8565239Z [16., 17., 18., 19., 20.]]]]) 2024-08-06T21:24:03.8565634Z >>> output, indices = pool(input) 2024-08-06T21:24:03.8566015Z >>> # This call will not work without specifying output_size 2024-08-06T21:24:03.8566517Z >>> unpool(output, indices, output_size=input.size()) 2024-08-06T21:24:03.8566895Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-08-06T21:24:03.8567260Z [ 0., 7., 0., 9., 0.], 2024-08-06T21:24:03.8567568Z [ 0., 0., 0., 0., 0.], 2024-08-06T21:24:03.8567930Z [ 0., 17., 0., 19., 0.]]]]) 2024-08-06T21:24:03.8568138Z 2024-08-06T21:24:03.8568143Z 2024-08-06T21:24:03.8568231Z 2024-08-06T21:24:03.8568661Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.8569029Z 2024-08-06T21:24:03.8822276Z 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. 2024-08-06T21:24:03.8823259Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.8824009Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-08-06T21:24:03.8824514Z 2024-08-06T21:24:03.8824841Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-08-06T21:24:03.8825459Z and with 2D inputs, this class 2024-08-06T21:24:03.8825659Z 2024-08-06T21:24:03.8826024Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-08-06T21:24:03.8827028Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-08-06T21:24:03.8827824Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-08-06T21:24:03.8828294Z 2024-08-06T21:24:03.8828686Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-08-06T21:24:03.8829314Z operations. 2024-08-06T21:24:03.8829451Z 2024-08-06T21:24:03.8829762Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-08-06T21:24:03.8830386Z pass. This scales the output of the Embedding before performing a weighted 2024-08-06T21:24:03.8831125Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-08-06T21:24:03.8831781Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-08-06T21:24:03.8832309Z :attr:`per_sample_weights`. 2024-08-06T21:24:03.8832504Z 2024-08-06T21:24:03.8832605Z Args: 2024-08-06T21:24:03.8832961Z num_embeddings (int): size of the dictionary of embeddings 2024-08-06T21:24:03.8833427Z embedding_dim (int): the size of each embedding vector 2024-08-06T21:24:03.8834083Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-08-06T21:24:03.8834721Z is renormalized to have norm :attr:`max_norm`. 2024-08-06T21:24:03.8835392Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-08-06T21:24:03.8836234Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-08-06T21:24:03.8836910Z the words in the mini-batch. Default ``False``. 2024-08-06T21:24:03.8837424Z Note: this option is not supported when ``mode="max"``. 2024-08-06T21:24:03.8838091Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-08-06T21:24:03.8838726Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-08-06T21:24:03.8839338Z into consideration. ``"mean"`` computes the average of the values 2024-08-06T21:24:03.8839919Z in the bag, ``"max"`` computes the max value over each bag. 2024-08-06T21:24:03.8840334Z Default: ``"mean"`` 2024-08-06T21:24:03.8840932Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-08-06T21:24:03.8841694Z Notes for more details regarding sparse gradients. Note: this option is not 2024-08-06T21:24:03.8842260Z supported when ``mode="max"``. 2024-08-06T21:24:03.8843089Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-08-06T21:24:03.8843857Z is equivalent to the size of `indices`. This matches the CSR format. 2024-08-06T21:24:03.8844593Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-08-06T21:24:03.8845385Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-08-06T21:24:03.8846074Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-08-06T21:24:03.8846770Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-08-06T21:24:03.8847466Z zeros, but can be updated to another value to be used as the padding vector. 2024-08-06T21:24:03.8848211Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-08-06T21:24:03.8848730Z reduction. 2024-08-06T21:24:03.8848966Z 2024-08-06T21:24:03.8849060Z Attributes: 2024-08-06T21:24:03.8849589Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-08-06T21:24:03.8850177Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-08-06T21:24:03.8850480Z 2024-08-06T21:24:03.8850587Z Examples:: 2024-08-06T21:24:03.8850723Z 2024-08-06T21:24:03.8850915Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-08-06T21:24:03.8851393Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-08-06T21:24:03.8851894Z >>> # a batch of 2 samples of 4 indices each 2024-08-06T21:24:03.8852322Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-08-06T21:24:03.8852827Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-06T21:24:03.8853270Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-06T21:24:03.8853665Z >>> embedding_sum(input, offsets) 2024-08-06T21:24:03.8854016Z tensor([[-0.8861, -5.4350, -0.0523], 2024-08-06T21:24:03.8854389Z [ 1.1306, -2.5798, -1.0044]]) 2024-08-06T21:24:03.8854600Z 2024-08-06T21:24:03.8854713Z >>> # Example with padding_idx 2024-08-06T21:24:03.8855199Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-08-06T21:24:03.8855770Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-08-06T21:24:03.8856232Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-06T21:24:03.8856667Z >>> embedding_sum(input, offsets) 2024-08-06T21:24:03.8856996Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-08-06T21:24:03.8857379Z [-0.7082, 3.2145, -2.6251]]) 2024-08-06T21:24:03.8857590Z 2024-08-06T21:24:03.8857767Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-08-06T21:24:03.8858361Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-08-06T21:24:03.8858804Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-08-06T21:24:03.8859211Z embedding.weight, 2024-08-06T21:24:03.8859547Z padding_idx=embedding.padding_idx, 2024-08-06T21:24:03.8859892Z mode='sum') 2024-08-06T21:24:03.8860161Z 2024-08-06T21:24:03.8860577Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.8860946Z 2024-08-06T21:24:03.9171000Z 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=1745. 2024-08-06T21:24:03.9172140Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.9172550Z 2024-08-06T21:24:03.9172830Z Context manager for training with uneven inputs across processes in DDP. 2024-08-06T21:24:03.9173198Z 2024-08-06T21:24:03.9173487Z This context manager will keep track of already-joined DDP processes, 2024-08-06T21:24:03.9174042Z and "shadow" the forward and backward passes by inserting collective 2024-08-06T21:24:03.9174641Z communication operations to match with the ones created by non-joined 2024-08-06T21:24:03.9175284Z DDP processes. This will ensure each collective call has a corresponding 2024-08-06T21:24:03.9175907Z call by already-joined DDP processes, preventing hangs or errors that 2024-08-06T21:24:03.9176480Z would otherwise happen when training with uneven inputs across 2024-08-06T21:24:03.9177036Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-08-06T21:24:03.9177662Z specified to be ``True``, all trainers will throw an error once one rank 2024-08-06T21:24:03.9178230Z runs out of inputs, allowing these errors to be caught and handled 2024-08-06T21:24:03.9178840Z according to application logic. 2024-08-06T21:24:03.9179034Z 2024-08-06T21:24:03.9179277Z Once all DDP processes have joined, the context manager will broadcast 2024-08-06T21:24:03.9179930Z the model corresponding to the last joined process to all processes to 2024-08-06T21:24:03.9180464Z ensure the model is the same across all processes 2024-08-06T21:24:03.9180834Z (which is guaranteed by DDP). 2024-08-06T21:24:03.9181062Z 2024-08-06T21:24:03.9181283Z To use this to enable training with uneven inputs across processes, 2024-08-06T21:24:03.9181864Z simply wrap this context manager around your training loop. No further 2024-08-06T21:24:03.9182381Z modifications to the model or data loading is required. 2024-08-06T21:24:03.9182732Z 2024-08-06T21:24:03.9182842Z .. warning:: 2024-08-06T21:24:03.9183186Z If the model or training loop this context manager is wrapped around 2024-08-06T21:24:03.9183813Z has additional distributed collective operations, such as 2024-08-06T21:24:03.9184357Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-08-06T21:24:03.9184920Z ``throw_on_early_termination`` must be enabled. This is because this 2024-08-06T21:24:03.9185452Z context manager is not aware of non-DDP collective communication. 2024-08-06T21:24:03.9186012Z This flag will cause all ranks to throw when any one rank 2024-08-06T21:24:03.9186563Z exhausts inputs, allowing these errors to be caught and recovered 2024-08-06T21:24:03.9187066Z from across all ranks. 2024-08-06T21:24:03.9187313Z 2024-08-06T21:24:03.9187400Z Args: 2024-08-06T21:24:03.9187709Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-08-06T21:24:03.9188271Z gradients by the initial ``world_size`` DDP training was launched 2024-08-06T21:24:03.9188823Z with. If ``False``, will compute the effective world size 2024-08-06T21:24:03.9189306Z (number of ranks that have not depleted their inputs yet) and 2024-08-06T21:24:03.9189804Z divide gradients by that during allreduce. Set 2024-08-06T21:24:03.9190304Z ``divide_by_initial_world_size=True`` to ensure every input 2024-08-06T21:24:03.9190915Z sample including the uneven inputs have equal weight in terms of 2024-08-06T21:24:03.9191463Z how much they contribute to the global gradient. This is 2024-08-06T21:24:03.9191982Z achieved by always dividing the gradient by the initial 2024-08-06T21:24:03.9192472Z ``world_size`` even when we encounter uneven inputs. If you set 2024-08-06T21:24:03.9192987Z this to ``False``, we divide the gradient by the remaining 2024-08-06T21:24:03.9193525Z number of nodes. This ensures parity with training on a smaller 2024-08-06T21:24:03.9194053Z ``world_size`` although it also means the uneven inputs would 2024-08-06T21:24:03.9194565Z contribute more towards the global gradient. Typically, you 2024-08-06T21:24:03.9195123Z would want to set this to ``True`` for cases where the last few 2024-08-06T21:24:03.9195690Z inputs of your training job are uneven. In extreme cases, where 2024-08-06T21:24:03.9196198Z there is a large discrepancy in the number of inputs, setting 2024-08-06T21:24:03.9196688Z this to ``False`` might provide better results. 2024-08-06T21:24:03.9197215Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-08-06T21:24:03.9197718Z in ``enable=False`` to disable in cases where you know that 2024-08-06T21:24:03.9198327Z inputs are even across participating processes. Default is 2024-08-06T21:24:03.9198855Z ``True``. 2024-08-06T21:24:03.9199187Z throw_on_early_termination (bool): Whether to throw an error 2024-08-06T21:24:03.9199769Z or continue training when at least one rank has exhausted 2024-08-06T21:24:03.9200248Z inputs. If ``True``, will throw upon the first rank reaching end 2024-08-06T21:24:03.9200727Z of data. If ``False``, will continue training with a smaller 2024-08-06T21:24:03.9201251Z effective world size until all ranks are joined. Note that if 2024-08-06T21:24:03.9201691Z this flag is specified, then the flag 2024-08-06T21:24:03.9202099Z ``divide_by_initial_world_size`` would be ignored. Default 2024-08-06T21:24:03.9202470Z is ``False``. 2024-08-06T21:24:03.9202634Z 2024-08-06T21:24:03.9202638Z 2024-08-06T21:24:03.9202734Z Example:: 2024-08-06T21:24:03.9202854Z 2024-08-06T21:24:03.9202979Z >>> # xdoctest: +SKIP("Distributed") 2024-08-06T21:24:03.9203282Z >>> import torch 2024-08-06T21:24:03.9203553Z >>> import torch.distributed as dist 2024-08-06T21:24:03.9203870Z >>> import os 2024-08-06T21:24:03.9204129Z >>> import torch.multiprocessing as mp 2024-08-06T21:24:03.9204467Z >>> import torch.nn as nn 2024-08-06T21:24:03.9204762Z >>> # On each spawned worker 2024-08-06T21:24:03.9205080Z >>> def worker(rank): 2024-08-06T21:24:03.9205425Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-08-06T21:24:03.9205830Z >>> torch.cuda.set_device(rank) 2024-08-06T21:24:03.9206173Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-08-06T21:24:03.9206599Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-08-06T21:24:03.9207027Z >>> model, device_ids=[rank], output_device=rank 2024-08-06T21:24:03.9207357Z >>> ) 2024-08-06T21:24:03.9207618Z >>> # Rank 1 gets one more input than rank 0. 2024-08-06T21:24:03.9208047Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-08-06T21:24:03.9208448Z >>> with model.join(): 2024-08-06T21:24:03.9208733Z >>> for _ in range(5): 2024-08-06T21:24:03.9209035Z >>> for inp in inputs: 2024-08-06T21:24:03.9209349Z >>> loss = model(inp).sum() 2024-08-06T21:24:03.9209686Z >>> loss.backward() 2024-08-06T21:24:03.9210092Z >>> # Without the join() API, the below synchronization will hang 2024-08-06T21:24:03.9210536Z >>> # blocking for rank 1's allreduce to complete. 2024-08-06T21:24:03.9210924Z >>> torch.cuda.synchronize(device=rank) 2024-08-06T21:24:03.9211223Z 2024-08-06T21:24:03.9211493Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.9211860Z 2024-08-06T21:24:03.9212631Z 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=2036. 2024-08-06T21:24:03.9213713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.9214108Z 2024-08-06T21:24:03.9214418Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-08-06T21:24:03.9214841Z 2024-08-06T21:24:03.9215062Z Registers an optimizer with DDP such that the optimization for a 2024-08-06T21:24:03.9215587Z parameter will run immediately when that parameter's gradient is 2024-08-06T21:24:03.9216125Z finished with reduction, instead of waiting for all parameters' 2024-08-06T21:24:03.9216673Z gradients to finish reduction. This can result in a training speedup 2024-08-06T21:24:03.9217230Z depending on your workload since the optimizer can run while gradient 2024-08-06T21:24:03.9217789Z reduction for other parameters are still ongoing. In addition, this has 2024-08-06T21:24:03.9218359Z the potential to reduce peak memory consumption during training, as it 2024-08-06T21:24:03.9218908Z only needs to load the per-parameter optimizer states of a single 2024-08-06T21:24:03.9219423Z parameter at a time, instead of loading all per-parameter optimizer 2024-08-06T21:24:03.9219846Z states at once. 2024-08-06T21:24:03.9219982Z 2024-08-06T21:24:03.9220082Z Args: 2024-08-06T21:24:03.9220384Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-08-06T21:24:03.9220801Z as a fused optimizer. 2024-08-06T21:24:03.9221143Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-08-06T21:24:03.9221686Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-08-06T21:24:03.9222246Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-08-06T21:24:03.9222795Z Optimizers. If this is omitted, all DDP model parameters will be 2024-08-06T21:24:03.9223196Z optimized. 2024-08-06T21:24:03.9223534Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-08-06T21:24:03.9223843Z 2024-08-06T21:24:03.9223950Z .. warning :: 2024-08-06T21:24:03.9224286Z _register_fused_optim should only be called once on a DDP instance, 2024-08-06T21:24:03.9224829Z and registering multiple fused optimizers for the same DDP model 2024-08-06T21:24:03.9225295Z is not currently supported. Please ping 2024-08-06T21:24:03.9225762Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-06T21:24:03.9226247Z for your use case. 2024-08-06T21:24:03.9226406Z 2024-08-06T21:24:03.9226510Z .. warning :: 2024-08-06T21:24:03.9226958Z _register_fused_optim and register_comm_hook currently do not 2024-08-06T21:24:03.9227488Z compose together, meaning that custom DDP communication hooks are 2024-08-06T21:24:03.9227992Z not supported with overlapped optimizers. Please ping 2024-08-06T21:24:03.9228493Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-06T21:24:03.9228935Z for your use case. 2024-08-06T21:24:03.9229090Z 2024-08-06T21:24:03.9229193Z .. warning :: 2024-08-06T21:24:03.9229540Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-08-06T21:24:03.9230013Z with overlapped optimizer. Please ping 2024-08-06T21:24:03.9230473Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-06T21:24:03.9230568Z for your use case. 2024-08-06T21:24:03.9230575Z 2024-08-06T21:24:03.9230675Z Example:: 2024-08-06T21:24:03.9230680Z 2024-08-06T21:24:03.9230809Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-08-06T21:24:03.9231112Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-08-06T21:24:03.9231388Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-08-06T21:24:03.9231479Z >>> lr = 1e-2 2024-08-06T21:24:03.9231575Z >>> betas = (0.9, 0.99) 2024-08-06T21:24:03.9231683Z >>> eps = 1e-6 2024-08-06T21:24:03.9231906Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-08-06T21:24:03.9232023Z >>> # Example with subset of parameters 2024-08-06T21:24:03.9232176Z >>> params_to_opt = [list(net.parameters())[0]] 2024-08-06T21:24:03.9232284Z >>> net._register_fused_optim( 2024-08-06T21:24:03.9232514Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-08-06T21:24:03.9232619Z ... ) 2024-08-06T21:24:03.9232625Z 2024-08-06T21:24:03.9232877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.9232881Z 2024-08-06T21:24:03.9454575Z 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. 2024-08-06T21:24:03.9454842Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.9455123Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-08-06T21:24:03.9455129Z 2024-08-06T21:24:03.9455420Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-06T21:24:03.9455698Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-06T21:24:03.9456019Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-06T21:24:03.9456328Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-06T21:24:03.9456342Z 2024-08-06T21:24:03.9456436Z .. note:: 2024-08-06T21:24:03.9456740Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-06T21:24:03.9457109Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-08-06T21:24:03.9457378Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-06T21:24:03.9457639Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-06T21:24:03.9457860Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-06T21:24:03.9458143Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-08-06T21:24:03.9458344Z even in cases where we have to apply permutation to input tensors. 2024-08-06T21:24:03.9458349Z 2024-08-06T21:24:03.9458572Z Hence our strategy here is to convert only the weight of convolution to 2024-08-06T21:24:03.9458699Z channels_last. This ensures that; 2024-08-06T21:24:03.9459027Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-06T21:24:03.9459260Z outweigh overhead of permutation (if input is not in the same format) 2024-08-06T21:24:03.9459536Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-06T21:24:03.9459712Z from memory_format conversion. 2024-08-06T21:24:03.9459719Z 2024-08-06T21:24:03.9460075Z The optimal case is that, layers between convolution layers are channels 2024-08-06T21:24:03.9460395Z last compatible. Input tensor would be permuted to channels last when it 2024-08-06T21:24:03.9460698Z encounters the first convolution layer and stay in that memory format. 2024-08-06T21:24:03.9460953Z Hence following convolutions will not need to permute its input tensor. 2024-08-06T21:24:03.9460960Z 2024-08-06T21:24:03.9461260Z In case where a channels last incompatible layer is between convolution 2024-08-06T21:24:03.9461541Z layers, we need to permute the input tensor back to contiguous format 2024-08-06T21:24:03.9461842Z for that layer. The input tensor will go through the remaining layers in 2024-08-06T21:24:03.9462110Z contiguous format and be permuted to channels last when it encounters 2024-08-06T21:24:03.9462435Z another convolution layer. There's no point in propagating that 2024-08-06T21:24:03.9462706Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-06T21:24:03.9462808Z ``memory_format``. 2024-08-06T21:24:03.9462813Z 2024-08-06T21:24:03.9463057Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-06T21:24:03.9463276Z there might have been a better spot to fuse the permutation other than 2024-08-06T21:24:03.9463422Z immediately before a convolution. 2024-08-06T21:24:03.9463429Z 2024-08-06T21:24:03.9463542Z Args: 2024-08-06T21:24:03.9463755Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-08-06T21:24:03.9463860Z ``nn.Module`` 2024-08-06T21:24:03.9464027Z memory_format: user specified ``memory_format``, 2024-08-06T21:24:03.9464259Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-06T21:24:03.9464269Z 2024-08-06T21:24:03.9464369Z Returns: 2024-08-06T21:24:03.9464510Z The original module with updated ``nn.Conv2d`` 2024-08-06T21:24:03.9464515Z 2024-08-06T21:24:03.9464601Z Example: 2024-08-06T21:24:03.9464748Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:03.9464913Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-06T21:24:03.9465180Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-08-06T21:24:03.9465296Z >>> model = nn.Sequential( 2024-08-06T21:24:03.9465407Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-08-06T21:24:03.9465515Z >>> # This is identical to: 2024-08-06T21:24:03.9465827Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-06T21:24:03.9466149Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-06T21:24:03.9466252Z >>> out = model(input) 2024-08-06T21:24:03.9466351Z 2024-08-06T21:24:03.9466666Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.9466671Z 2024-08-06T21:24:03.9467494Z 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. 2024-08-06T21:24:03.9467762Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.9468022Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-08-06T21:24:03.9468313Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-06T21:24:03.9468629Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-06T21:24:03.9468957Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-06T21:24:03.9469270Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-06T21:24:03.9469275Z 2024-08-06T21:24:03.9469371Z .. note:: 2024-08-06T21:24:03.9469673Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-06T21:24:03.9469897Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-08-06T21:24:03.9470105Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-06T21:24:03.9470396Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-06T21:24:03.9470620Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-06T21:24:03.9470849Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-08-06T21:24:03.9471086Z even in cases where we have to apply permutation to input tensors. 2024-08-06T21:24:03.9471097Z 2024-08-06T21:24:03.9471330Z Hence our strategy here is to convert only the weight of convolution to 2024-08-06T21:24:03.9471517Z channels_last. This ensures that; 2024-08-06T21:24:03.9471732Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-06T21:24:03.9472021Z outweigh overhead of permutation (if input is not in the same format) 2024-08-06T21:24:03.9472268Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-06T21:24:03.9472379Z from memory_format conversion. 2024-08-06T21:24:03.9472384Z 2024-08-06T21:24:03.9472670Z The optimal case is that, layers between convolution layers are channels 2024-08-06T21:24:03.9472909Z last compatible. Input tensor would be permuted to channels last when it 2024-08-06T21:24:03.9473142Z encounters the first convolution layer and stay in that memory format. 2024-08-06T21:24:03.9473454Z Hence following convolutions will not need to permute its input tensor. 2024-08-06T21:24:03.9473461Z 2024-08-06T21:24:03.9473686Z In case where a channels last incompatible layer is between convolution 2024-08-06T21:24:03.9473896Z layers, we need to permute the input tensor back to contiguous format 2024-08-06T21:24:03.9474184Z for that layer. The input tensor will go through the remaining layers in 2024-08-06T21:24:03.9474411Z contiguous format and be permuted to channels last when it encounters 2024-08-06T21:24:03.9474620Z another convolution layer. There's no point in propagating that 2024-08-06T21:24:03.9474864Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-06T21:24:03.9475003Z ``memory_format``. 2024-08-06T21:24:03.9475008Z 2024-08-06T21:24:03.9475253Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-06T21:24:03.9475476Z there might have been a better spot to fuse the permutation other than 2024-08-06T21:24:03.9475660Z immediately before a convolution. 2024-08-06T21:24:03.9475668Z 2024-08-06T21:24:03.9475786Z Args: 2024-08-06T21:24:03.9476005Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-08-06T21:24:03.9476107Z ``nn.Module`` 2024-08-06T21:24:03.9476273Z memory_format: user specified ``memory_format``, 2024-08-06T21:24:03.9476503Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-06T21:24:03.9476508Z 2024-08-06T21:24:03.9476598Z Returns: 2024-08-06T21:24:03.9476758Z The original module with updated ``nn.Conv3d`` 2024-08-06T21:24:03.9476762Z 2024-08-06T21:24:03.9476852Z Example: 2024-08-06T21:24:03.9477005Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:03.9477176Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-06T21:24:03.9477466Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-08-06T21:24:03.9477589Z >>> model = nn.Sequential( 2024-08-06T21:24:03.9477702Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-08-06T21:24:03.9477809Z >>> # This is identical to: 2024-08-06T21:24:03.9478122Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-08-06T21:24:03.9478398Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-06T21:24:03.9478499Z >>> out = model(input) 2024-08-06T21:24:03.9478600Z 2024-08-06T21:24:03.9478914Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.9478919Z 2024-08-06T21:24:03.9679892Z 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=937. 2024-08-06T21:24:03.9680171Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.9680400Z Prune tensor by removing random channels along the specified dimension. 2024-08-06T21:24:03.9680409Z 2024-08-06T21:24:03.9680940Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-06T21:24:03.9681161Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-06T21:24:03.9681386Z along the specified ``dim`` selected at random. 2024-08-06T21:24:03.9681586Z Modifies module in place (and also return the modified module) 2024-08-06T21:24:03.9681674Z by: 2024-08-06T21:24:03.9681679Z 2024-08-06T21:24:03.9681905Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-06T21:24:03.9682180Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-06T21:24:03.9682392Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-06T21:24:03.9682613Z original (unpruned) parameter is stored in a new parameter named 2024-08-06T21:24:03.9682707Z ``name+'_orig'``. 2024-08-06T21:24:03.9682712Z 2024-08-06T21:24:03.9682867Z Args: 2024-08-06T21:24:03.9683120Z module (nn.Module): module containing the tensor to prune 2024-08-06T21:24:03.9683343Z name (str): parameter name within ``module`` on which pruning 2024-08-06T21:24:03.9683477Z will act. 2024-08-06T21:24:03.9683726Z amount (int or float): quantity of parameters to prune. 2024-08-06T21:24:03.9683927Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-06T21:24:03.9684141Z fraction of parameters to prune. If ``int``, it represents the 2024-08-06T21:24:03.9684345Z absolute number of parameters to prune. 2024-08-06T21:24:03.9684570Z dim (int): index of the dim along which we define channels to prune. 2024-08-06T21:24:03.9684575Z 2024-08-06T21:24:03.9684675Z Returns: 2024-08-06T21:24:03.9684898Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-06T21:24:03.9684902Z 2024-08-06T21:24:03.9685003Z Examples: 2024-08-06T21:24:03.9685167Z >>> # xdoctest: +SKIP 2024-08-06T21:24:03.9685280Z >>> m = prune.random_structured( 2024-08-06T21:24:03.9685438Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-08-06T21:24:03.9685522Z ... ) 2024-08-06T21:24:03.9685705Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-08-06T21:24:03.9685824Z >>> print(columns_pruned) 2024-08-06T21:24:03.9685911Z 3 2024-08-06T21:24:03.9685993Z 2024-08-06T21:24:03.9686257Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.9686261Z 2024-08-06T21:24:03.9686787Z 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=978. 2024-08-06T21:24:03.9687067Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.9687424Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-08-06T21:24:03.9687431Z 2024-08-06T21:24:03.9687664Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-06T21:24:03.9687899Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-06T21:24:03.9688074Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-08-06T21:24:03.9688269Z Modifies module in place (and also return the modified module) 2024-08-06T21:24:03.9688369Z by: 2024-08-06T21:24:03.9688373Z 2024-08-06T21:24:03.9688581Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-06T21:24:03.9688812Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-06T21:24:03.9689019Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-06T21:24:03.9689225Z original (unpruned) parameter is stored in a new parameter named 2024-08-06T21:24:03.9689334Z ``name+'_orig'``. 2024-08-06T21:24:03.9689338Z 2024-08-06T21:24:03.9689429Z Args: 2024-08-06T21:24:03.9689704Z module (nn.Module): module containing the tensor to prune 2024-08-06T21:24:03.9690099Z name (str): parameter name within ``module`` on which pruning 2024-08-06T21:24:03.9690192Z will act. 2024-08-06T21:24:03.9690362Z amount (int or float): quantity of parameters to prune. 2024-08-06T21:24:03.9690550Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-06T21:24:03.9690814Z fraction of parameters to prune. If ``int``, it represents the 2024-08-06T21:24:03.9690979Z absolute number of parameters to prune. 2024-08-06T21:24:03.9691192Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-08-06T21:24:03.9691344Z entries for argument ``p`` in :func:`torch.norm`. 2024-08-06T21:24:03.9691660Z dim (int): index of the dim along which we define channels to prune. 2024-08-06T21:24:03.9691902Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-08-06T21:24:03.9692093Z shape as module parameter) used to compute mask for pruning. 2024-08-06T21:24:03.9692330Z The values in this tensor indicate the importance of the corresponding 2024-08-06T21:24:03.9692457Z elements in the parameter being pruned. 2024-08-06T21:24:03.9692683Z If unspecified or None, the module parameter will be used in its place. 2024-08-06T21:24:03.9692687Z 2024-08-06T21:24:03.9692787Z Returns: 2024-08-06T21:24:03.9693004Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-06T21:24:03.9693008Z 2024-08-06T21:24:03.9693097Z Examples: 2024-08-06T21:24:03.9693231Z >>> from torch.nn.utils import prune 2024-08-06T21:24:03.9693340Z >>> m = prune.ln_structured( 2024-08-06T21:24:03.9693538Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-08-06T21:24:03.9693624Z ... ) 2024-08-06T21:24:03.9693706Z 2024-08-06T21:24:03.9694017Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.9694021Z 2024-08-06T21:24:03.9694616Z 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=1025. 2024-08-06T21:24:03.9694914Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.9694921Z 2024-08-06T21:24:03.9695429Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-08-06T21:24:03.9695434Z 2024-08-06T21:24:03.9695569Z Modifies modules in place by: 2024-08-06T21:24:03.9695575Z 2024-08-06T21:24:03.9695855Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-06T21:24:03.9696073Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-06T21:24:03.9696322Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-06T21:24:03.9696542Z original (unpruned) parameter is stored in a new parameter named 2024-08-06T21:24:03.9696634Z ``name+'_orig'``. 2024-08-06T21:24:03.9696639Z 2024-08-06T21:24:03.9696739Z Args: 2024-08-06T21:24:03.9696940Z parameters (Iterable of (module, name) tuples): parameters of 2024-08-06T21:24:03.9697130Z the model to prune in a global fashion, i.e. by aggregating all 2024-08-06T21:24:03.9697349Z weights prior to deciding which ones to prune. module must be of 2024-08-06T21:24:03.9697498Z type :class:`nn.Module`, and name must be a string. 2024-08-06T21:24:03.9697723Z pruning_method (function): a valid pruning function from this module, 2024-08-06T21:24:03.9697914Z or a custom one implemented by the user that satisfies the 2024-08-06T21:24:03.9698143Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-08-06T21:24:03.9698375Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-08-06T21:24:03.9698610Z the corresponding parameter's importance scores tensor. The tensor 2024-08-06T21:24:03.9698880Z should be the same shape as the parameter, and is used for computing 2024-08-06T21:24:03.9698980Z mask for pruning. 2024-08-06T21:24:03.9699198Z If unspecified or None, the parameter will be used in place of its 2024-08-06T21:24:03.9699299Z importance scores. 2024-08-06T21:24:03.9699439Z kwargs: other keyword arguments such as: 2024-08-06T21:24:03.9699634Z amount (int or float): quantity of parameters to prune across the 2024-08-06T21:24:03.9699738Z specified parameters. 2024-08-06T21:24:03.9699922Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-06T21:24:03.9700121Z fraction of parameters to prune. If ``int``, it represents the 2024-08-06T21:24:03.9700251Z absolute number of parameters to prune. 2024-08-06T21:24:03.9700256Z 2024-08-06T21:24:03.9700355Z Raises: 2024-08-06T21:24:03.9700505Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-08-06T21:24:03.9700512Z 2024-08-06T21:24:03.9700595Z Note: 2024-08-06T21:24:03.9700828Z Since global structured pruning doesn't make much sense unless the 2024-08-06T21:24:03.9701025Z norm is normalized by the size of the parameter, we now limit the 2024-08-06T21:24:03.9701171Z scope of global pruning to unstructured methods. 2024-08-06T21:24:03.9701189Z 2024-08-06T21:24:03.9701276Z Examples: 2024-08-06T21:24:03.9701392Z >>> from torch.nn.utils import prune 2024-08-06T21:24:03.9701523Z >>> from collections import OrderedDict 2024-08-06T21:24:03.9701637Z >>> net = nn.Sequential(OrderedDict([ 2024-08-06T21:24:03.9701740Z ... ('first', nn.Linear(10, 4)), 2024-08-06T21:24:03.9701860Z ... ('second', nn.Linear(4, 1)), 2024-08-06T21:24:03.9701944Z ... ])) 2024-08-06T21:24:03.9702050Z >>> parameters_to_prune = ( 2024-08-06T21:24:03.9702165Z ... (net.first, 'weight'), 2024-08-06T21:24:03.9702267Z ... (net.second, 'weight'), 2024-08-06T21:24:03.9702382Z ... ) 2024-08-06T21:24:03.9702504Z >>> prune.global_unstructured( 2024-08-06T21:24:03.9702613Z ... parameters_to_prune, 2024-08-06T21:24:03.9702741Z ... pruning_method=prune.L1Unstructured, 2024-08-06T21:24:03.9702845Z ... amount=10, 2024-08-06T21:24:03.9702930Z ... ) 2024-08-06T21:24:03.9703147Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-08-06T21:24:03.9703247Z tensor(10) 2024-08-06T21:24:03.9703251Z 2024-08-06T21:24:03.9703255Z 2024-08-06T21:24:03.9703507Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.9703512Z 2024-08-06T21:24:03.9704059Z 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=1144. 2024-08-06T21:24:03.9704352Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:03.9704745Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-08-06T21:24:03.9704752Z 2024-08-06T21:24:03.9704982Z Modifies module in place (and also return the modified module) by: 2024-08-06T21:24:03.9704986Z 2024-08-06T21:24:03.9705196Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-06T21:24:03.9705425Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-06T21:24:03.9705636Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-06T21:24:03.9705842Z original (unpruned) parameter is stored in a new parameter named 2024-08-06T21:24:03.9705952Z ``name+'_orig'``. 2024-08-06T21:24:03.9705957Z 2024-08-06T21:24:03.9706041Z Args: 2024-08-06T21:24:03.9706219Z module (nn.Module): module containing the tensor to prune 2024-08-06T21:24:03.9706416Z name (str): parameter name within ``module`` on which pruning 2024-08-06T21:24:03.9706504Z will act. 2024-08-06T21:24:03.9706684Z mask (Tensor): binary mask to be applied to the parameter. 2024-08-06T21:24:03.9706688Z 2024-08-06T21:24:03.9706931Z Returns: 2024-08-06T21:24:03.9707152Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-06T21:24:03.9707157Z 2024-08-06T21:24:03.9707258Z Examples: 2024-08-06T21:24:03.9707379Z >>> from torch.nn.utils import prune 2024-08-06T21:24:03.9707487Z >>> m = prune.custom_from_mask( 2024-08-06T21:24:03.9707671Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-08-06T21:24:03.9707756Z ... ) 2024-08-06T21:24:03.9707856Z >>> print(m.bias_mask) 2024-08-06T21:24:03.9707964Z tensor([0., 1., 0.]) 2024-08-06T21:24:03.9707968Z 2024-08-06T21:24:03.9708051Z 2024-08-06T21:24:03.9708303Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:03.9708310Z 2024-08-06T21:24:04.1580850Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=106. 2024-08-06T21:24:04.1581362Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.1582083Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-08-06T21:24:04.1582134Z 2024-08-06T21:24:04.1582584Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-08-06T21:24:04.1582874Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-08-06T21:24:04.1583094Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-08-06T21:24:04.1583185Z (UAI 2018). 2024-08-06T21:24:04.1583189Z 2024-08-06T21:24:04.1583420Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-08-06T21:24:04.1583666Z but using exponential weights instead of equal weights across iterations. 2024-08-06T21:24:04.1583670Z 2024-08-06T21:24:04.1584057Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-08-06T21:24:04.1584300Z on the device :attr:`device` and allows to compute running averages of the 2024-08-06T21:24:04.1584413Z parameters of the :attr:`model`. 2024-08-06T21:24:04.1584417Z 2024-08-06T21:24:04.1584517Z Args: 2024-08-06T21:24:04.1584671Z model (torch.nn.Module): model to use with SWA/EMA 2024-08-06T21:24:04.1584907Z device (torch.device, optional): if provided, the averaged model will be 2024-08-06T21:24:04.1585032Z stored on the :attr:`device` 2024-08-06T21:24:04.1585238Z avg_fn (function, optional): the averaging function used to update 2024-08-06T21:24:04.1585440Z parameters; the function must take in the current value of the 2024-08-06T21:24:04.1585676Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-08-06T21:24:04.1585924Z parameter, and the number of models already averaged; if None, 2024-08-06T21:24:04.1586088Z an equally weighted average is used (default: None) 2024-08-06T21:24:04.1586332Z multi_avg_fn (function, optional): the averaging function used to update 2024-08-06T21:24:04.1586574Z parameters inplace; the function must take in the current values of the 2024-08-06T21:24:04.1586931Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-08-06T21:24:04.1587160Z parameters as a list, and the number of models already averaged; if None, 2024-08-06T21:24:04.1587320Z an equally weighted average is used (default: None) 2024-08-06T21:24:04.1587542Z use_buffers (bool): if ``True``, it will compute running averages for 2024-08-06T21:24:04.1587766Z both the parameters and the buffers of the model. (default: ``False``) 2024-08-06T21:24:04.1587773Z 2024-08-06T21:24:04.1587867Z Example: 2024-08-06T21:24:04.1588014Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:04.1588146Z >>> loader, optimizer, model, loss_fn = ... 2024-08-06T21:24:04.1588319Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-08-06T21:24:04.1588681Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-08-06T21:24:04.1588796Z >>> T_max=300) 2024-08-06T21:24:04.1588894Z >>> swa_start = 160 2024-08-06T21:24:04.1589051Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-08-06T21:24:04.1589153Z >>> for i in range(300): 2024-08-06T21:24:04.1589280Z >>> for input, target in loader: 2024-08-06T21:24:04.1589392Z >>> optimizer.zero_grad() 2024-08-06T21:24:04.1589527Z >>> loss_fn(model(input), target).backward() 2024-08-06T21:24:04.1589649Z >>> optimizer.step() 2024-08-06T21:24:04.1589752Z >>> if i > swa_start: 2024-08-06T21:24:04.1589879Z >>> swa_model.update_parameters(model) 2024-08-06T21:24:04.1590039Z >>> swa_scheduler.step() 2024-08-06T21:24:04.1590170Z >>> else: 2024-08-06T21:24:04.1590340Z >>> scheduler.step() 2024-08-06T21:24:04.1590445Z >>> 2024-08-06T21:24:04.1590608Z >>> # Update bn statistics for the swa_model at the end 2024-08-06T21:24:04.1590820Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-08-06T21:24:04.1590825Z 2024-08-06T21:24:04.1591136Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-08-06T21:24:04.1591400Z If no averaging function is provided, the default is to compute 2024-08-06T21:24:04.1591639Z equally-weighted average of the weights (SWA). 2024-08-06T21:24:04.1591658Z 2024-08-06T21:24:04.1591812Z Example: 2024-08-06T21:24:04.1592053Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:04.1592464Z >>> # Compute exponential moving averages of the weights and buffers 2024-08-06T21:24:04.1592684Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-08-06T21:24:04.1592904Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-08-06T21:24:04.1592909Z 2024-08-06T21:24:04.1593029Z .. note:: 2024-08-06T21:24:04.1593249Z When using SWA/EMA with models containing Batch Normalization you may 2024-08-06T21:24:04.1593452Z need to update the activation statistics for Batch Normalization. 2024-08-06T21:24:04.1593698Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-08-06T21:24:04.1593922Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-08-06T21:24:04.1594161Z statistics in a post-training step by passing data through the model. The 2024-08-06T21:24:04.1594412Z second does it during the parameter update phase by averaging all buffers. 2024-08-06T21:24:04.1594693Z Empirical evidence has shown that updating the statistics in normalization 2024-08-06T21:24:04.1594935Z layers increases accuracy, but you may wish to empirically test which 2024-08-06T21:24:04.1595093Z approach yields the best results in your problem. 2024-08-06T21:24:04.1595098Z 2024-08-06T21:24:04.1595188Z .. note:: 2024-08-06T21:24:04.1595456Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-08-06T21:24:04.1595460Z 2024-08-06T21:24:04.1595547Z .. note:: 2024-08-06T21:24:04.1595746Z When :meth:`update_parameters` is called for the first time (i.e. 2024-08-06T21:24:04.1595940Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-08-06T21:24:04.1596141Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-08-06T21:24:04.1596343Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-08-06T21:24:04.1596456Z to update the parameters. 2024-08-06T21:24:04.1596460Z 2024-08-06T21:24:04.1596683Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-06T21:24:04.1596828Z https://arxiv.org/abs/1803.05407 2024-08-06T21:24:04.1597123Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-08-06T21:24:04.1597212Z Average: 2024-08-06T21:24:04.1597339Z https://arxiv.org/abs/1806.05594 2024-08-06T21:24:04.1597537Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-08-06T21:24:04.1597652Z https://arxiv.org/abs/1904.11943 2024-08-06T21:24:04.1597885Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-08-06T21:24:04.1597984Z Generalizes Well: 2024-08-06T21:24:04.1598099Z https://arxiv.org/abs/2001.02312 2024-08-06T21:24:04.1598210Z .. _Polyak averaging: 2024-08-06T21:24:04.1598376Z https://paperswithcode.com/method/polyak-averaging 2024-08-06T21:24:04.1598461Z 2024-08-06T21:24:04.1598727Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.1598733Z 2024-08-06T21:24:04.1599239Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=357. 2024-08-06T21:24:04.1599512Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.1599724Z Anneals the learning rate in each parameter group to a fixed value. 2024-08-06T21:24:04.1599729Z 2024-08-06T21:24:04.1599970Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-08-06T21:24:04.1600180Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-08-06T21:24:04.1600185Z 2024-08-06T21:24:04.1600272Z Args: 2024-08-06T21:24:04.1600456Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-08-06T21:24:04.1600662Z swa_lrs (float or list): the learning rate value for all param groups 2024-08-06T21:24:04.1600792Z together or separately for each group. 2024-08-06T21:24:04.1601008Z annealing_epochs (int): number of epochs in the annealing phase 2024-08-06T21:24:04.1601132Z (default: 10) 2024-08-06T21:24:04.1601353Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-08-06T21:24:04.1601574Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-08-06T21:24:04.1601672Z (default: "cos") 2024-08-06T21:24:04.1601851Z last_epoch (int): the index of the last epoch (default: -1) 2024-08-06T21:24:04.1601868Z 2024-08-06T21:24:04.1602044Z The :class:`SWALR` scheduler can be used together with other 2024-08-06T21:24:04.1602260Z schedulers to switch to a constant learning rate late in the training 2024-08-06T21:24:04.1602376Z as in the example below. 2024-08-06T21:24:04.1602380Z 2024-08-06T21:24:04.1602468Z Example: 2024-08-06T21:24:04.1602671Z >>> # xdoctest: +SKIP("Undefined variables") 2024-08-06T21:24:04.1602801Z >>> loader, optimizer, model = ... 2024-08-06T21:24:04.1602912Z >>> lr_lambda = lambda epoch: 0.9 2024-08-06T21:24:04.1603133Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-08-06T21:24:04.1603258Z >>> lr_lambda=lr_lambda) 2024-08-06T21:24:04.1603430Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-08-06T21:24:04.1603609Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-08-06T21:24:04.1603718Z >>> swa_start = 160 2024-08-06T21:24:04.1603819Z >>> for i in range(300): 2024-08-06T21:24:04.1603933Z >>> for input, target in loader: 2024-08-06T21:24:04.1604058Z >>> optimizer.zero_grad() 2024-08-06T21:24:04.1604191Z >>> loss_fn(model(input), target).backward() 2024-08-06T21:24:04.1604311Z >>> optimizer.step() 2024-08-06T21:24:04.1604411Z >>> if i > swa_start: 2024-08-06T21:24:04.1604516Z >>> swa_scheduler.step() 2024-08-06T21:24:04.1604616Z >>> else: 2024-08-06T21:24:04.1604720Z >>> scheduler.step() 2024-08-06T21:24:04.1604724Z 2024-08-06T21:24:04.1605001Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-06T21:24:04.1605129Z https://arxiv.org/abs/1803.05407 2024-08-06T21:24:04.1605211Z 2024-08-06T21:24:04.1605463Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.1605468Z 2024-08-06T21:24:04.1722846Z 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=111. 2024-08-06T21:24:04.1723115Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.1723268Z Register a container-like type as pytree node. 2024-08-06T21:24:04.1723273Z 2024-08-06T21:24:04.1723367Z Args: 2024-08-06T21:24:04.1723554Z cls (type): A Python type to treat as an internal pytree node. 2024-08-06T21:24:04.1723838Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-08-06T21:24:04.1724096Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-08-06T21:24:04.1724391Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-08-06T21:24:04.1724519Z passed to the ``unflatten_fn``. 2024-08-06T21:24:04.1724796Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-08-06T21:24:04.1725065Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-08-06T21:24:04.1725218Z The function should return an instance of ``cls``. 2024-08-06T21:24:04.1725483Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-08-06T21:24:04.1725663Z qualified name used when serializing the tree spec. 2024-08-06T21:24:04.1725971Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-08-06T21:24:04.1726363Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-08-06T21:24:04.1726646Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-08-06T21:24:04.1726947Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-08-06T21:24:04.1727206Z how to convert the custom json dumpable representation of the context back to the 2024-08-06T21:24:04.1727483Z original context. This is used for json deserialization, which is being used in 2024-08-06T21:24:04.1727598Z :mod:`torch.export` right now. 2024-08-06T21:24:04.1727602Z 2024-08-06T21:24:04.1727718Z Example:: 2024-08-06T21:24:04.1727764Z 2024-08-06T21:24:04.1727866Z >>> # xdoctest: +SKIP 2024-08-06T21:24:04.1728010Z >>> # Registry a Python type with lambda functions 2024-08-06T21:24:04.1728128Z >>> register_pytree_node( 2024-08-06T21:24:04.1728215Z ... set, 2024-08-06T21:24:04.1728338Z ... lambda s: (sorted(s), None, None), 2024-08-06T21:24:04.1728475Z ... lambda children, _: set(children), 2024-08-06T21:24:04.1728560Z ... ) 2024-08-06T21:24:04.1728641Z 2024-08-06T21:24:04.1728910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.1728915Z 2024-08-06T21:24:04.2219580Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1199. 2024-08-06T21:24:04.2220594Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.2221022Z 2024-08-06T21:24:04.2221242Z Context passed to policy function during selective checkpointing. 2024-08-06T21:24:04.2221583Z 2024-08-06T21:24:04.2221826Z This class is used to pass relevant metadata to the policy function during 2024-08-06T21:24:04.2222442Z selective checkpointing. The metadata includes whether the current invocation 2024-08-06T21:24:04.2223224Z of the policy function is during recomputation or not. 2024-08-06T21:24:04.2223513Z 2024-08-06T21:24:04.2223604Z Example: 2024-08-06T21:24:04.2223841Z >>> # xdoctest: +SKIP(stub) 2024-08-06T21:24:04.2224106Z >>> 2024-08-06T21:24:04.2224359Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-06T21:24:04.2224821Z >>> print(ctx.is_recompute) 2024-08-06T21:24:04.2225253Z >>> 2024-08-06T21:24:04.2225754Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-06T21:24:04.2226305Z >>> 2024-08-06T21:24:04.2226632Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-06T21:24:04.2227160Z >>> fn, x, y, 2024-08-06T21:24:04.2227418Z >>> use_reentrant=False, 2024-08-06T21:24:04.2227722Z >>> context_fn=context_fn, 2024-08-06T21:24:04.2228078Z >>> ) 2024-08-06T21:24:04.2228200Z 2024-08-06T21:24:04.2228461Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.2228901Z 2024-08-06T21:24:04.2229633Z 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=1333. 2024-08-06T21:24:04.2230699Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.2231135Z 2024-08-06T21:24:04.2231389Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-08-06T21:24:04.2231802Z 2024-08-06T21:24:04.2232033Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-08-06T21:24:04.2232568Z operations are recomputed during the backward pass. 2024-08-06T21:24:04.2232852Z 2024-08-06T21:24:04.2232937Z Args: 2024-08-06T21:24:04.2233203Z policy_fn_or_list (Callable or List): 2024-08-06T21:24:04.2233611Z - If a policy function is provided, it should accept a 2024-08-06T21:24:04.2234296Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-08-06T21:24:04.2234930Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-08-06T21:24:04.2235522Z indicating whether the execution of the op should be recomputed or not. 2024-08-06T21:24:04.2236104Z - If a list of operations is provided, it is equivalent to a policy 2024-08-06T21:24:04.2236677Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-08-06T21:24:04.2237244Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-08-06T21:24:04.2237676Z operations. 2024-08-06T21:24:04.2238096Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-08-06T21:24:04.2238695Z raised if any tensors cached by selective activation checkpoint are 2024-08-06T21:24:04.2239389Z mutated in order to ensure correctness. If set to `True`, this check 2024-08-06T21:24:04.2239822Z is disabled. 2024-08-06T21:24:04.2240073Z Returns: 2024-08-06T21:24:04.2240346Z A tuple of two context managers. 2024-08-06T21:24:04.2240557Z 2024-08-06T21:24:04.2240658Z Example: 2024-08-06T21:24:04.2240921Z >>> # xdoctest: +REQUIRES(LINUX) 2024-08-06T21:24:04.2241238Z >>> import functools 2024-08-06T21:24:04.2241492Z >>> 2024-08-06T21:24:04.2241777Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-08-06T21:24:04.2242141Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-08-06T21:24:04.2242678Z >>> 2024-08-06T21:24:04.2242886Z >>> ops_to_save = [ 2024-08-06T21:24:04.2243169Z >>> torch.ops.aten.mm.default, 2024-08-06T21:24:04.2243541Z >>> ] 2024-08-06T21:24:04.2243741Z >>> 2024-08-06T21:24:04.2243987Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-06T21:24:04.2244396Z >>> if op in ops_to_save: 2024-08-06T21:24:04.2244711Z >>> return CheckpointPolicy.MUST_SAVE 2024-08-06T21:24:04.2245099Z >>> else: 2024-08-06T21:24:04.2245371Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-08-06T21:24:04.2245774Z >>> 2024-08-06T21:24:04.2246280Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-06T21:24:04.2246836Z >>> 2024-08-06T21:24:04.2247058Z >>> # or equivalently 2024-08-06T21:24:04.2247568Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-08-06T21:24:04.2248095Z >>> 2024-08-06T21:24:04.2248308Z >>> def fn(x, y): 2024-08-06T21:24:04.2248700Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-08-06T21:24:04.2249116Z >>> 2024-08-06T21:24:04.2249375Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-06T21:24:04.2249773Z >>> fn, x, y, 2024-08-06T21:24:04.2250018Z >>> use_reentrant=False, 2024-08-06T21:24:04.2250366Z >>> context_fn=context_fn, 2024-08-06T21:24:04.2250658Z >>> ) 2024-08-06T21:24:04.2250775Z 2024-08-06T21:24:04.2251084Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.2251470Z 2024-08-06T21:24:04.2424416Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=925. 2024-08-06T21:24:04.2425339Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.2425714Z 2024-08-06T21:24:04.2425875Z Create a :class:`setuptools.Extension` for C++. 2024-08-06T21:24:04.2426125Z 2024-08-06T21:24:04.2426366Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-06T21:24:04.2427001Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-08-06T21:24:04.2427337Z 2024-08-06T21:24:04.2427557Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-06T21:24:04.2428015Z constructor. Full list arguments can be found at 2024-08-06T21:24:04.2428616Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-06T21:24:04.2429178Z 2024-08-06T21:24:04.2429268Z Example: 2024-08-06T21:24:04.2429493Z >>> # xdoctest: +SKIP 2024-08-06T21:24:04.2429878Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-06T21:24:04.2430439Z >>> from setuptools import setup 2024-08-06T21:24:04.2431098Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-08-06T21:24:04.2431603Z >>> setup( 2024-08-06T21:24:04.2431877Z ... name='extension', 2024-08-06T21:24:04.2432202Z ... ext_modules=[ 2024-08-06T21:24:04.2432471Z ... CppExtension( 2024-08-06T21:24:04.2432795Z ... name='extension', 2024-08-06T21:24:04.2433122Z ... sources=['extension.cpp'], 2024-08-06T21:24:04.2433460Z ... extra_compile_args=['-g'], 2024-08-06T21:24:04.2433846Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-08-06T21:24:04.2434305Z ... ], 2024-08-06T21:24:04.2434521Z ... cmdclass={ 2024-08-06T21:24:04.2434802Z ... 'build_ext': BuildExtension 2024-08-06T21:24:04.2435125Z ... }) 2024-08-06T21:24:04.2435258Z 2024-08-06T21:24:04.2435520Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.2435904Z 2024-08-06T21:24:04.2436454Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=976. 2024-08-06T21:24:04.2437381Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.2437759Z 2024-08-06T21:24:04.2437932Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-08-06T21:24:04.2438200Z 2024-08-06T21:24:04.2438441Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-06T21:24:04.2439007Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-08-06T21:24:04.2439573Z extension. This includes the CUDA include path, library path and runtime 2024-08-06T21:24:04.2440125Z library. 2024-08-06T21:24:04.2440324Z 2024-08-06T21:24:04.2440537Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-06T21:24:04.2441209Z constructor. Full list arguments can be found at 2024-08-06T21:24:04.2441942Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-06T21:24:04.2442553Z 2024-08-06T21:24:04.2442646Z Example: 2024-08-06T21:24:04.2442876Z >>> # xdoctest: +SKIP 2024-08-06T21:24:04.2443189Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-06T21:24:04.2443565Z >>> from setuptools import setup 2024-08-06T21:24:04.2444010Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-08-06T21:24:04.2444430Z >>> setup( 2024-08-06T21:24:04.2444673Z ... name='cuda_extension', 2024-08-06T21:24:04.2444970Z ... ext_modules=[ 2024-08-06T21:24:04.2445232Z ... CUDAExtension( 2024-08-06T21:24:04.2445533Z ... name='cuda_extension', 2024-08-06T21:24:04.2445925Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-06T21:24:04.2446333Z ... extra_compile_args={'cxx': ['-g'], 2024-08-06T21:24:04.2446700Z ... 'nvcc': ['-O2']}, 2024-08-06T21:24:04.2447095Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-08-06T21:24:04.2447452Z ... ], 2024-08-06T21:24:04.2447676Z ... cmdclass={ 2024-08-06T21:24:04.2447944Z ... 'build_ext': BuildExtension 2024-08-06T21:24:04.2448242Z ... }) 2024-08-06T21:24:04.2448382Z 2024-08-06T21:24:04.2448482Z Compute capabilities: 2024-08-06T21:24:04.2448643Z 2024-08-06T21:24:04.2448958Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-08-06T21:24:04.2449800Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-08-06T21:24:04.2450579Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-08-06T21:24:04.2451434Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-08-06T21:24:04.2452158Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-08-06T21:24:04.2452679Z support (see below for details on PTX). 2024-08-06T21:24:04.2452912Z 2024-08-06T21:24:04.2453224Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-08-06T21:24:04.2453773Z CCs you want the extension to support: 2024-08-06T21:24:04.2453992Z 2024-08-06T21:24:04.2454181Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-08-06T21:24:04.2454718Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-08-06T21:24:04.2455069Z 2024-08-06T21:24:04.2455403Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-08-06T21:24:04.2456207Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-08-06T21:24:04.2456952Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-08-06T21:24:04.2457672Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-08-06T21:24:04.2458416Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-08-06T21:24:04.2459129Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-08-06T21:24:04.2459856Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-08-06T21:24:04.2460617Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-08-06T21:24:04.2461137Z "8.0 8.6" would be better. 2024-08-06T21:24:04.2461320Z 2024-08-06T21:24:04.2461622Z Note that while it's possible to include all supported archs, the more archs get included the 2024-08-06T21:24:04.2462335Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-08-06T21:24:04.2462746Z 2024-08-06T21:24:04.2463175Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-08-06T21:24:04.2463833Z To workaround the issue, move python binding logic to pure C++ file. 2024-08-06T21:24:04.2464168Z 2024-08-06T21:24:04.2464259Z Example use: 2024-08-06T21:24:04.2464496Z #include 2024-08-06T21:24:04.2464836Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-08-06T21:24:04.2465107Z 2024-08-06T21:24:04.2465197Z Instead of: 2024-08-06T21:24:04.2465440Z #include 2024-08-06T21:24:04.2465785Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-08-06T21:24:04.2466060Z 2024-08-06T21:24:04.2466335Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-08-06T21:24:04.2467334Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-08-06T21:24:04.2467961Z 2024-08-06T21:24:04.2468073Z Relocatable device code linking: 2024-08-06T21:24:04.2468285Z 2024-08-06T21:24:04.2468567Z If you want to reference device symbols across compilation units (across object files), 2024-08-06T21:24:04.2469234Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-08-06T21:24:04.2469980Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-08-06T21:24:04.2470782Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-08-06T21:24:04.2471561Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-08-06T21:24:04.2472176Z help reduce the protentional perf degradation of `-rdc`. 2024-08-06T21:24:04.2472622Z Note that it needs to be used at both steps to be useful. 2024-08-06T21:24:04.2472908Z 2024-08-06T21:24:04.2473311Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-08-06T21:24:04.2473971Z There is also a case where `-dlink` is used without `-rdc`: 2024-08-06T21:24:04.2474503Z when an extension is linked against a static lib containing rdc-compiled objects 2024-08-06T21:24:04.2475092Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-08-06T21:24:04.2475427Z 2024-08-06T21:24:04.2475629Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-08-06T21:24:04.2475941Z 2024-08-06T21:24:04.2476043Z Example: 2024-08-06T21:24:04.2476254Z >>> # xdoctest: +SKIP 2024-08-06T21:24:04.2476577Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-06T21:24:04.2476938Z >>> CUDAExtension( 2024-08-06T21:24:04.2477195Z ... name='cuda_extension', 2024-08-06T21:24:04.2477605Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-06T21:24:04.2477984Z ... dlink=True, 2024-08-06T21:24:04.2478267Z ... dlink_libraries=["dlink_lib"], 2024-08-06T21:24:04.2478624Z ... extra_compile_args={'cxx': ['-g'], 2024-08-06T21:24:04.2478994Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-08-06T21:24:04.2479231Z 2024-08-06T21:24:04.2479485Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.2479867Z 2024-08-06T21:24:04.2480429Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1234. 2024-08-06T21:24:04.2481294Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.2481681Z 2024-08-06T21:24:04.2481823Z Load a PyTorch C++ extension just-in-time (JIT). 2024-08-06T21:24:04.2482074Z 2024-08-06T21:24:04.2482294Z To load an extension, a Ninja build file is emitted, which is used to 2024-08-06T21:24:04.2482813Z compile the given sources into a dynamic library. This library is 2024-08-06T21:24:04.2483365Z subsequently loaded into the current Python process as a module and 2024-08-06T21:24:04.2483836Z returned from this function, ready for use. 2024-08-06T21:24:04.2484148Z 2024-08-06T21:24:04.2484362Z By default, the directory to which the build file is emitted and the 2024-08-06T21:24:04.2484930Z resulting library compiled to is ``/torch_extensions/``, where 2024-08-06T21:24:04.2485495Z ```` is the temporary folder on the current platform and ```` 2024-08-06T21:24:04.2486023Z the name of the extension. This location can be overridden in two ways. 2024-08-06T21:24:04.2486566Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-08-06T21:24:04.2487116Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-08-06T21:24:04.2487677Z into subfolders of this directory. Second, if the ``build_directory`` 2024-08-06T21:24:04.2488245Z argument to this function is supplied, it overrides the entire path, i.e. 2024-08-06T21:24:04.2488769Z the library will be compiled into that folder directly. 2024-08-06T21:24:04.2489042Z 2024-08-06T21:24:04.2489274Z To compile the sources, the default system compiler (``c++``) is used, 2024-08-06T21:24:04.2489834Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-08-06T21:24:04.2490428Z additional arguments to the compilation process, ``extra_cflags`` or 2024-08-06T21:24:04.2491003Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-08-06T21:24:04.2491549Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-08-06T21:24:04.2492040Z ``extra_cflags`` to pass further include directories. 2024-08-06T21:24:04.2492302Z 2024-08-06T21:24:04.2492552Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-08-06T21:24:04.2493083Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-08-06T21:24:04.2493633Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-08-06T21:24:04.2494263Z passing the CUDA lib64 directory as a library directory, and linking 2024-08-06T21:24:04.2494736Z ``cudart``. You can pass additional flags to nvcc via 2024-08-06T21:24:04.2495212Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-08-06T21:24:04.2495776Z heuristics for finding the CUDA install directory are used, which usually 2024-08-06T21:24:04.2496347Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-08-06T21:24:04.2496764Z safest option. 2024-08-06T21:24:04.2496915Z 2024-08-06T21:24:04.2496996Z Args: 2024-08-06T21:24:04.2497327Z name: The name of the extension to build. This MUST be the same as the 2024-08-06T21:24:04.2497751Z name of the pybind11 module! 2024-08-06T21:24:04.2498163Z sources: A list of relative or absolute paths to C++ source files. 2024-08-06T21:24:04.2498740Z extra_cflags: optional list of compiler flags to forward to the build. 2024-08-06T21:24:04.2499290Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-08-06T21:24:04.2499740Z when building CUDA sources. 2024-08-06T21:24:04.2500169Z extra_ldflags: optional list of linker flags to forward to the build. 2024-08-06T21:24:04.2500719Z extra_include_paths: optional list of include directories to forward 2024-08-06T21:24:04.2501152Z to the build. 2024-08-06T21:24:04.2501496Z build_directory: optional path to use as build workspace. 2024-08-06T21:24:04.2501959Z verbose: If ``True``, turns on verbose logging of load steps. 2024-08-06T21:24:04.2502481Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-06T21:24:04.2502983Z the build. If set to ``None`` (default), this value is 2024-08-06T21:24:04.2503455Z automatically determined based on the existence of ``.cu`` or 2024-08-06T21:24:04.2503954Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-08-06T21:24:04.2504355Z and libraries to be included. 2024-08-06T21:24:04.2504763Z is_python_module: If ``True`` (default), imports the produced shared 2024-08-06T21:24:04.2505332Z library as a Python module. If ``False``, behavior depends on 2024-08-06T21:24:04.2505741Z ``is_standalone``. 2024-08-06T21:24:04.2506114Z is_standalone: If ``False`` (default) loads the constructed extension 2024-08-06T21:24:04.2506636Z into the process as a plain dynamic library. If ``True``, build a 2024-08-06T21:24:04.2507154Z standalone executable. 2024-08-06T21:24:04.2507342Z 2024-08-06T21:24:04.2507430Z Returns: 2024-08-06T21:24:04.2507670Z If ``is_python_module`` is ``True``: 2024-08-06T21:24:04.2508076Z Returns the loaded PyTorch extension as a Python module. 2024-08-06T21:24:04.2508369Z 2024-08-06T21:24:04.2508576Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-08-06T21:24:04.2509121Z Returns nothing. (The shared library is loaded into the process as 2024-08-06T21:24:04.2509549Z a side effect.) 2024-08-06T21:24:04.2509705Z 2024-08-06T21:24:04.2509826Z If ``is_standalone`` is ``True``. 2024-08-06T21:24:04.2510232Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-08-06T21:24:04.2510730Z added to the PATH environment variable as a side effect.) 2024-08-06T21:24:04.2511015Z 2024-08-06T21:24:04.2511114Z Example: 2024-08-06T21:24:04.2511323Z >>> # xdoctest: +SKIP 2024-08-06T21:24:04.2511637Z >>> from torch.utils.cpp_extension import load 2024-08-06T21:24:04.2511980Z >>> module = load( 2024-08-06T21:24:04.2512224Z ... name='extension', 2024-08-06T21:24:04.2512563Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-06T21:24:04.2512941Z ... extra_cflags=['-O2'], 2024-08-06T21:24:04.2513221Z ... verbose=True) 2024-08-06T21:24:04.2513398Z 2024-08-06T21:24:04.2513650Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.2514018Z 2024-08-06T21:24:04.2514605Z 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=1523. 2024-08-06T21:24:04.2515500Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.2515890Z 2024-08-06T21:24:04.2516100Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-08-06T21:24:04.2516418Z 2024-08-06T21:24:04.2516668Z This function behaves exactly like :func:`load`, but takes its sources as 2024-08-06T21:24:04.2517234Z strings rather than filenames. These strings are stored to files in the 2024-08-06T21:24:04.2517796Z build directory, after which the behavior of :func:`load_inline` is 2024-08-06T21:24:04.2518228Z identical to :func:`load`. 2024-08-06T21:24:04.2518401Z 2024-08-06T21:24:04.2518499Z See `the 2024-08-06T21:24:04.2518965Z tests `_ 2024-08-06T21:24:04.2519531Z for good examples of using this function. 2024-08-06T21:24:04.2519757Z 2024-08-06T21:24:04.2520000Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-08-06T21:24:04.2520589Z the necessary header includes, as well as the (pybind11) binding code. More 2024-08-06T21:24:04.2521193Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-08-06T21:24:04.2521747Z single ``.cpp`` file. This file is then prepended with ``#include 2024-08-06T21:24:04.2522141Z ``. 2024-08-06T21:24:04.2522313Z 2024-08-06T21:24:04.2522539Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-08-06T21:24:04.2523123Z automatically generated for each function specified. ``functions`` can 2024-08-06T21:24:04.2523692Z either be a list of function names, or a dictionary mapping from function 2024-08-06T21:24:04.2524265Z names to docstrings. If a list is given, the name of each function is used 2024-08-06T21:24:04.2524714Z as its docstring. 2024-08-06T21:24:04.2524856Z 2024-08-06T21:24:04.2525077Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-08-06T21:24:04.2525653Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-08-06T21:24:04.2526156Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-08-06T21:24:04.2526695Z separately, but ultimately linked into a single library. Note that no 2024-08-06T21:24:04.2537628Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-08-06T21:24:04.2538352Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-08-06T21:24:04.2538908Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-08-06T21:24:04.2539363Z include its name in ``functions``). 2024-08-06T21:24:04.2539573Z 2024-08-06T21:24:04.2539774Z See :func:`load` for a description of arguments omitted below. 2024-08-06T21:24:04.2540077Z 2024-08-06T21:24:04.2540165Z Args: 2024-08-06T21:24:04.2540513Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-08-06T21:24:04.2541092Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-08-06T21:24:04.2541656Z functions: A list of function names for which to generate function 2024-08-06T21:24:04.2542186Z bindings. If a dictionary is given, it should map function names to 2024-08-06T21:24:04.2542956Z docstrings (which are otherwise just the function names). 2024-08-06T21:24:04.2543488Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-06T21:24:04.2543982Z the build. If set to ``None`` (default), this value is 2024-08-06T21:24:04.2544469Z automatically determined based on whether ``cuda_sources`` is 2024-08-06T21:24:04.2544951Z provided. Set it to ``True`` to force CUDA headers 2024-08-06T21:24:04.2545321Z and libraries to be included. 2024-08-06T21:24:04.2545751Z with_pytorch_error_handling: Determines whether pytorch error and 2024-08-06T21:24:04.2546286Z warning macros are handled by pytorch instead of pybind. To do 2024-08-06T21:24:04.2547007Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-08-06T21:24:04.2547563Z function. This redirection might cause issues in obscure cases 2024-08-06T21:24:04.2548073Z of cpp. This flag should be set to ``False`` when this redirect 2024-08-06T21:24:04.2548461Z causes issues. 2024-08-06T21:24:04.2548632Z 2024-08-06T21:24:04.2548725Z Example: 2024-08-06T21:24:04.2549006Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-06T21:24:04.2549413Z >>> from torch.utils.cpp_extension import load_inline 2024-08-06T21:24:04.2549779Z >>> source = """ 2024-08-06T21:24:04.2550096Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-08-06T21:24:04.2550440Z return x.sin() + y.sin(); 2024-08-06T21:24:04.2550776Z } 2024-08-06T21:24:04.2550983Z """ 2024-08-06T21:24:04.2551233Z >>> module = load_inline(name='inline_extension', 2024-08-06T21:24:04.2551608Z ... cpp_sources=[source], 2024-08-06T21:24:04.2551965Z ... functions=['sin_add']) 2024-08-06T21:24:04.2552194Z 2024-08-06T21:24:04.2552286Z .. note:: 2024-08-06T21:24:04.2552613Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-08-06T21:24:04.2553141Z extension. This may use up too many resources on some systems. One 2024-08-06T21:24:04.2553680Z can control the number of workers by setting the `MAX_JOBS` environment 2024-08-06T21:24:04.2554145Z variable to a non-negative number. 2024-08-06T21:24:04.2554360Z 2024-08-06T21:24:04.2554627Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.2554992Z 2024-08-06T21:24:04.2633153Z 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. 2024-08-06T21:24:04.2634145Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.2634541Z 2024-08-06T21:24:04.2634838Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-08-06T21:24:04.2635370Z 2024-08-06T21:24:04.2635686Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-08-06T21:24:04.2636346Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-08-06T21:24:04.2636956Z server like load. It can emulate multiple calling threads to a single module 2024-08-06T21:24:04.2637554Z provided. In the future we plan to enhance this component to support inter and 2024-08-06T21:24:04.2638167Z intra-op parallelism as well as multiple models running in a single process. 2024-08-06T21:24:04.2638530Z 2024-08-06T21:24:04.2638785Z Please note that even though nn.Module is supported, it might incur an overhead 2024-08-06T21:24:04.2639382Z from the need to hold GIL every time we execute Python code or pass around 2024-08-06T21:24:04.2639967Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-08-06T21:24:04.2640557Z model for inference deployment it is better to switch to using it in this 2024-08-06T21:24:04.2641010Z benchmark. 2024-08-06T21:24:04.2641147Z 2024-08-06T21:24:04.2641249Z Example:: 2024-08-06T21:24:04.2641368Z 2024-08-06T21:24:04.2641486Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:04.2641857Z >>> from torch.utils import ThroughputBenchmark 2024-08-06T21:24:04.2642242Z >>> bench = ThroughputBenchmark(my_module) 2024-08-06T21:24:04.2642829Z >>> # Pre-populate benchmark's data set with the inputs 2024-08-06T21:24:04.2643215Z >>> for input in inputs: 2024-08-06T21:24:04.2643624Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-08-06T21:24:04.2644083Z ... bench.add_input(input[0], x2=input[1]) 2024-08-06T21:24:04.2644532Z >>> # Inputs supplied above are randomly used during the execution 2024-08-06T21:24:04.2644957Z >>> stats = bench.benchmark( 2024-08-06T21:24:04.2645339Z ... num_calling_threads=4, 2024-08-06T21:24:04.2645651Z ... num_warmup_iters = 100, 2024-08-06T21:24:04.2645963Z ... num_iters = 1000, 2024-08-06T21:24:04.2646225Z ... ) 2024-08-06T21:24:04.2646531Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-08-06T21:24:04.2647006Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-08-06T21:24:04.2647293Z 2024-08-06T21:24:04.2647562Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.2647929Z 2024-08-06T21:24:04.3474001Z 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. 2024-08-06T21:24:04.3474973Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.3475686Z Sampler that restricts data loading to a subset of the dataset. 2024-08-06T21:24:04.3476001Z 2024-08-06T21:24:04.3476173Z It is especially useful in conjunction with 2024-08-06T21:24:04.3476657Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-08-06T21:24:04.3477295Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-08-06T21:24:04.3477908Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-08-06T21:24:04.3478377Z original dataset that is exclusive to it. 2024-08-06T21:24:04.3478633Z 2024-08-06T21:24:04.3478738Z .. note:: 2024-08-06T21:24:04.3479112Z Dataset is assumed to be of constant size and that any instance of it always 2024-08-06T21:24:04.3479597Z returns the same elements in the same order. 2024-08-06T21:24:04.3479856Z 2024-08-06T21:24:04.3479944Z Args: 2024-08-06T21:24:04.3480189Z dataset: Dataset used for sampling. 2024-08-06T21:24:04.3480630Z num_replicas (int, optional): Number of processes participating in 2024-08-06T21:24:04.3481218Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-08-06T21:24:04.3481709Z current distributed group. 2024-08-06T21:24:04.3482297Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-08-06T21:24:04.3482867Z By default, :attr:`rank` is retrieved from the current distributed 2024-08-06T21:24:04.3483283Z group. 2024-08-06T21:24:04.3483647Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-08-06T21:24:04.3484092Z indices. 2024-08-06T21:24:04.3484443Z seed (int, optional): random seed used to shuffle the sampler if 2024-08-06T21:24:04.3484944Z :attr:`shuffle=True`. This number should be identical across all 2024-08-06T21:24:04.3485429Z processes in the distributed group. Default: ``0``. 2024-08-06T21:24:04.3485932Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-08-06T21:24:04.3486474Z tail of the data to make it evenly divisible across the number of 2024-08-06T21:24:04.3487007Z replicas. If ``False``, the sampler will add extra indices to make 2024-08-06T21:24:04.3487530Z the data evenly divisible across the replicas. Default: ``False``. 2024-08-06T21:24:04.3487868Z 2024-08-06T21:24:04.3487961Z .. warning:: 2024-08-06T21:24:04.3488298Z In distributed mode, calling the :meth:`set_epoch` method at 2024-08-06T21:24:04.3488847Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-08-06T21:24:04.3489487Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-08-06T21:24:04.3489996Z the same ordering will be always used. 2024-08-06T21:24:04.3490229Z 2024-08-06T21:24:04.3490325Z Example:: 2024-08-06T21:24:04.3490472Z 2024-08-06T21:24:04.3490574Z >>> # xdoctest: +SKIP 2024-08-06T21:24:04.3490988Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-08-06T21:24:04.3491540Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-08-06T21:24:04.3491952Z ... sampler=sampler) 2024-08-06T21:24:04.3492323Z >>> for epoch in range(start_epoch, n_epochs): 2024-08-06T21:24:04.3492681Z ... if is_distributed: 2024-08-06T21:24:04.3492986Z ... sampler.set_epoch(epoch) 2024-08-06T21:24:04.3493311Z ... train(loader) 2024-08-06T21:24:04.3493565Z 2024-08-06T21:24:04.3493936Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.3494315Z 2024-08-06T21:24:04.9246700Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40. 2024-08-06T21:24:04.9247674Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:04.9248282Z 2024-08-06T21:24:04.9248501Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-08-06T21:24:04.9249054Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-08-06T21:24:04.9249607Z pushes the map into PyTorch operations called by ``func``, effectively 2024-08-06T21:24:04.9250047Z vectorizing those operations. 2024-08-06T21:24:04.9250256Z 2024-08-06T21:24:04.9250472Z vmap is useful for handling batch dimensions: one can write a function 2024-08-06T21:24:04.9251015Z ``func`` that runs on examples and then lift it to a function that can 2024-08-06T21:24:04.9251541Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-08-06T21:24:04.9252047Z compute batched gradients when composed with autograd. 2024-08-06T21:24:04.9252337Z 2024-08-06T21:24:04.9252445Z .. note:: 2024-08-06T21:24:04.9252755Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-08-06T21:24:04.9253177Z convenience. Use whichever one you'd like. 2024-08-06T21:24:04.9253426Z 2024-08-06T21:24:04.9253510Z Args: 2024-08-06T21:24:04.9253839Z func (function): A Python function that takes one or more arguments. 2024-08-06T21:24:04.9254273Z Must return one or more Tensors. 2024-08-06T21:24:04.9254820Z in_dims (int or nested structure): Specifies which dimension of the 2024-08-06T21:24:04.9255327Z inputs should be mapped over. ``in_dims`` should have a 2024-08-06T21:24:04.9255804Z structure like the inputs. If the ``in_dim`` for a particular 2024-08-06T21:24:04.9256304Z input is None, then that indicates there is no map dimension. 2024-08-06T21:24:04.9256710Z Default: 0. 2024-08-06T21:24:04.9257046Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-08-06T21:24:04.9257564Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-08-06T21:24:04.9258035Z it should have one element per output. Default: 0. 2024-08-06T21:24:04.9258533Z randomness (str): Specifies whether the randomness in this 2024-08-06T21:24:04.9259047Z vmap should be the same or different across batches. If 'different', 2024-08-06T21:24:04.9259571Z the randomness for each batch will be different. If 'same', the 2024-08-06T21:24:04.9260108Z randomness will be the same across batches. If 'error', any calls to 2024-08-06T21:24:04.9260654Z random functions will error. Default: 'error'. WARNING: this flag 2024-08-06T21:24:04.9261176Z only applies to random PyTorch operations and does not apply to 2024-08-06T21:24:04.9261632Z Python's random module or numpy randomness. 2024-08-06T21:24:04.9262110Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-08-06T21:24:04.9262662Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-08-06T21:24:04.9263256Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-08-06T21:24:04.9263904Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-08-06T21:24:04.9264286Z 2024-08-06T21:24:04.9264437Z Returns: 2024-08-06T21:24:04.9264752Z Returns a new "batched" function. It takes the same inputs as 2024-08-06T21:24:04.9265247Z ``func``, except each input has an extra dimension at the index 2024-08-06T21:24:04.9265728Z specified by ``in_dims``. It takes returns the same outputs as 2024-08-06T21:24:04.9266223Z ``func``, except each output has an extra dimension at the index 2024-08-06T21:24:04.9266639Z specified by ``out_dims``. 2024-08-06T21:24:04.9266906Z 2024-08-06T21:24:04.9266996Z .. warning: 2024-08-06T21:24:04.9267324Z :func:`vmap` works best with functional-style code. Please do not 2024-08-06T21:24:04.9267830Z perform any side-effects in ``func``, with the exception of 2024-08-06T21:24:04.9268372Z in-place PyTorch operations. Examples of side-effects include mutating 2024-08-06T21:24:04.9268985Z Python data structures and assigning values to variables not captured 2024-08-06T21:24:04.9269417Z in ``func``. 2024-08-06T21:24:04.9269552Z 2024-08-06T21:24:04.9269805Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-08-06T21:24:04.9270369Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-08-06T21:24:04.9270932Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-08-06T21:24:04.9271264Z 2024-08-06T21:24:04.9271441Z >>> torch.dot # [D], [D] -> [] 2024-08-06T21:24:04.9271900Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-08-06T21:24:04.9272354Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-06T21:24:04.9272708Z >>> batched_dot(x, y) 2024-08-06T21:24:04.9272874Z 2024-08-06T21:24:04.9273105Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-08-06T21:24:04.9273566Z model authoring experience. 2024-08-06T21:24:04.9273754Z 2024-08-06T21:24:04.9273881Z >>> batch_size, feature_size = 3, 5 2024-08-06T21:24:04.9274267Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-08-06T21:24:04.9274651Z >>> 2024-08-06T21:24:04.9274873Z >>> def model(feature_vec): 2024-08-06T21:24:04.9275250Z >>> # Very simple linear model with activation 2024-08-06T21:24:04.9275626Z >>> return feature_vec.dot(weights).relu() 2024-08-06T21:24:04.9275977Z >>> 2024-08-06T21:24:04.9276238Z >>> examples = torch.randn(batch_size, feature_size) 2024-08-06T21:24:04.9276627Z >>> result = torch.vmap(model)(examples) 2024-08-06T21:24:04.9276854Z 2024-08-06T21:24:04.9277117Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-08-06T21:24:04.9277713Z or impossible to batch. One example is higher-order gradient computation. 2024-08-06T21:24:04.9278300Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-08-06T21:24:04.9278881Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-08-06T21:24:04.9279472Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-08-06T21:24:04.9280085Z we can vectorize the whole computation, computing the Jacobian in a single 2024-08-06T21:24:04.9280559Z call to ``autograd.grad``. 2024-08-06T21:24:04.9280734Z 2024-08-06T21:24:04.9280836Z >>> # Setup 2024-08-06T21:24:04.9281053Z >>> N = 5 2024-08-06T21:24:04.9281295Z >>> f = lambda x: x ** 2 2024-08-06T21:24:04.9281590Z >>> x = torch.randn(N, requires_grad=True) 2024-08-06T21:24:04.9281921Z >>> y = f(x) 2024-08-06T21:24:04.9282162Z >>> I_N = torch.eye(N) 2024-08-06T21:24:04.9282413Z >>> 2024-08-06T21:24:04.9282640Z >>> # Sequential approach 2024-08-06T21:24:04.9283049Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-08-06T21:24:04.9283490Z >>> for v in I_N.unbind()] 2024-08-06T21:24:04.9283840Z >>> jacobian = torch.stack(jacobian_rows) 2024-08-06T21:24:04.9284173Z >>> 2024-08-06T21:24:04.9284402Z >>> # vectorized gradient computation 2024-08-06T21:24:04.9284731Z >>> def get_vjp(v): 2024-08-06T21:24:04.9285066Z >>> return torch.autograd.grad(y, x, v) 2024-08-06T21:24:04.9285407Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-08-06T21:24:04.9285644Z 2024-08-06T21:24:04.9285906Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-08-06T21:24:04.9286283Z 2024-08-06T21:24:04.9286437Z >>> torch.dot # [D], [D] -> [] 2024-08-06T21:24:04.9286959Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-08-06T21:24:04.9287493Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-08-06T21:24:04.9287871Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-08-06T21:24:04.9288103Z 2024-08-06T21:24:04.9288356Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-08-06T21:24:04.9288902Z the dimension that each inputs are batched along as 2024-08-06T21:24:04.9289176Z 2024-08-06T21:24:04.9289316Z >>> torch.dot # [N], [N] -> [] 2024-08-06T21:24:04.9289801Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-08-06T21:24:04.9290265Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-06T21:24:04.9290752Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-08-06T21:24:04.9291107Z 2024-08-06T21:24:04.9291380Z If there are multiple inputs each of which is batched along different dimensions, 2024-08-06T21:24:04.9291953Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-08-06T21:24:04.9292279Z 2024-08-06T21:24:04.9292419Z >>> torch.dot # [D], [D] -> [] 2024-08-06T21:24:04.9292911Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-08-06T21:24:04.9293395Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-06T21:24:04.9293858Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-08-06T21:24:04.9294225Z 2024-08-06T21:24:04.9294462Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-08-06T21:24:04.9294995Z matching the shape of the input: 2024-08-06T21:24:04.9295193Z 2024-08-06T21:24:04.9295332Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-08-06T21:24:04.9295705Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-06T21:24:04.9296041Z >>> input = {'x': x, 'y': y} 2024-08-06T21:24:04.9296400Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-08-06T21:24:04.9296802Z >>> batched_dot(input) 2024-08-06T21:24:04.9296976Z 2024-08-06T21:24:04.9297265Z By default, the output is batched along the first dimension. However, it can be batched 2024-08-06T21:24:04.9297771Z along any dimension by using ``out_dims`` 2024-08-06T21:24:04.9298012Z 2024-08-06T21:24:04.9298111Z >>> f = lambda x: x ** 2 2024-08-06T21:24:04.9298394Z >>> x = torch.randn(2, 5) 2024-08-06T21:24:04.9298692Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-08-06T21:24:04.9299033Z >>> batched_pow(x) # [5, 2] 2024-08-06T21:24:04.9299213Z 2024-08-06T21:24:04.9299515Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-08-06T21:24:04.9300004Z accept kwargs 2024-08-06T21:24:04.9300146Z 2024-08-06T21:24:04.9300245Z >>> x = torch.randn([2, 5]) 2024-08-06T21:24:04.9300528Z >>> def fn(x, scale=4.): 2024-08-06T21:24:04.9300791Z >>> return x * scale 2024-08-06T21:24:04.9301044Z >>> 2024-08-06T21:24:04.9301269Z >>> batched_pow = torch.vmap(fn) 2024-08-06T21:24:04.9301607Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-08-06T21:24:04.9302078Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-08-06T21:24:04.9302414Z 2024-08-06T21:24:04.9302522Z .. note:: 2024-08-06T21:24:04.9302862Z vmap does not provide general autobatching or handle variable-length 2024-08-06T21:24:04.9303302Z sequences out of the box. 2024-08-06T21:24:04.9303521Z 2024-08-06T21:24:04.9303782Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:04.9304150Z 2024-08-06T21:24:05.0974880Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=14. 2024-08-06T21:24:05.0975788Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.0976419Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-08-06T21:24:05.0976802Z 2024-08-06T21:24:05.0977041Z Use this instead of :func:`torch.library.custom_op` when the implementation 2024-08-06T21:24:05.0977620Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-08-06T21:24:05.0978128Z custom operators as opaque (:func:`torch.compile` and 2024-08-06T21:24:05.0979354Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-08-06T21:24:05.0980011Z makes the implementation visible to these subsystems, allowing them 2024-08-06T21:24:05.0980695Z to optimize the triton kernel(s). 2024-08-06T21:24:05.0981112Z 2024-08-06T21:24:05.0981330Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-08-06T21:24:05.0981868Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-08-06T21:24:05.0982426Z must be wrapped in a call to :func:`torch._library.capture_triton``. 2024-08-06T21:24:05.0982745Z 2024-08-06T21:24:05.0982832Z Args: 2024-08-06T21:24:05.0983209Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-08-06T21:24:05.0983769Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-08-06T21:24:05.0984257Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-08-06T21:24:05.0984782Z To avoid name collisions, please use your project name as the namespace; 2024-08-06T21:24:05.0985347Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-08-06T21:24:05.0986075Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-08-06T21:24:05.0986790Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-08-06T21:24:05.0987416Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-08-06T21:24:05.0987990Z schema (None | str): A schema string for the operator. If None 2024-08-06T21:24:05.0988592Z (recommended) we'll infer a schema for the operator from its type 2024-08-06T21:24:05.0989124Z annotations. We recommend letting us infer a schema unless you 2024-08-06T21:24:05.0989562Z have a specific reason not to. 2024-08-06T21:24:05.0989933Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-08-06T21:24:05.0990209Z 2024-08-06T21:24:05.0990303Z Example:: 2024-08-06T21:24:05.0990433Z 2024-08-06T21:24:05.0990585Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:05.0990926Z >>> import torch 2024-08-06T21:24:05.0991268Z >>> from torch._library import triton_op, capture_triton 2024-08-06T21:24:05.0991641Z >>> 2024-08-06T21:24:05.0991853Z >>> import triton 2024-08-06T21:24:05.0992147Z >>> from triton import language as tl 2024-08-06T21:24:05.0992470Z >>> 2024-08-06T21:24:05.0992680Z >>> @triton.jit 2024-08-06T21:24:05.0992941Z >>> def add_kernel( 2024-08-06T21:24:05.0993212Z >>> in_ptr0, 2024-08-06T21:24:05.0993455Z >>> in_ptr1, 2024-08-06T21:24:05.0993711Z >>> out_ptr, 2024-08-06T21:24:05.0993970Z >>> n_elements, 2024-08-06T21:24:05.0994246Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-06T21:24:05.0994563Z >>> ): 2024-08-06T21:24:05.0994816Z >>> pid = tl.program_id(axis=0) 2024-08-06T21:24:05.0995141Z >>> block_start = pid * BLOCK_SIZE 2024-08-06T21:24:05.0995603Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-06T21:24:05.0995984Z >>> mask = offsets < n_elements 2024-08-06T21:24:05.0996325Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-06T21:24:05.0996733Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-06T21:24:05.0997095Z >>> output = x + y 2024-08-06T21:24:05.0997414Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-06T21:24:05.0997760Z >>> 2024-08-06T21:24:05.0998016Z >>> @triton_op("mylib::add", mutates_args={}) 2024-08-06T21:24:05.0998434Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-08-06T21:24:05.0998847Z >>> output = torch.empty_like(x) 2024-08-06T21:24:05.0999336Z >>> n_elements = output.numel() 2024-08-06T21:24:05.0999691Z >>> 2024-08-06T21:24:05.0999919Z >>> def grid(meta): 2024-08-06T21:24:05.1000266Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-06T21:24:05.1000686Z >>> 2024-08-06T21:24:05.1001013Z >>> # NB: we need to wrap the triton kernel in a call to capture_triton 2024-08-06T21:24:05.1001536Z >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-08-06T21:24:05.1001948Z >>> return output 2024-08-06T21:24:05.1002221Z >>> 2024-08-06T21:24:05.1002442Z >>> @torch.compile 2024-08-06T21:24:05.1002700Z >>> def f(x, y): 2024-08-06T21:24:05.1002968Z >>> return add(x, y) 2024-08-06T21:24:05.1003241Z >>> 2024-08-06T21:24:05.1003468Z >>> x = torch.randn(3, device="cuda") 2024-08-06T21:24:05.1003812Z >>> y = torch.randn(3, device="cuda") 2024-08-06T21:24:05.1004124Z >>> 2024-08-06T21:24:05.1004327Z >>> z = f(x, y) 2024-08-06T21:24:05.1004601Z >>> assert torch.allclose(z, x + y) 2024-08-06T21:24:05.1004822Z 2024-08-06T21:24:05.1004919Z 2024-08-06T21:24:05.1005279Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.1005662Z 2024-08-06T21:24:05.1006257Z msg = Cannot scrape callname=capture_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=124. 2024-08-06T21:24:05.1007150Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.1007717Z Allows capture of a triton kernel into a graph via make_fx or 2024-08-06T21:24:05.1008140Z non-strict export (coming soon). 2024-08-06T21:24:05.1008363Z 2024-08-06T21:24:05.1008548Z These technologies perform Dispatcher-based tracing (via 2024-08-06T21:24:05.1009050Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-08-06T21:24:05.1009565Z The ``capture_triton`` API returns a new callable that can actually 2024-08-06T21:24:05.1009995Z be traced into a graph. 2024-08-06T21:24:05.1010168Z 2024-08-06T21:24:05.1010269Z Examples: 2024-08-06T21:24:05.1010398Z 2024-08-06T21:24:05.1010500Z >>> # xdoctest: +SKIP 2024-08-06T21:24:05.1010777Z >>> import torch 2024-08-06T21:24:05.1011039Z >>> import triton 2024-08-06T21:24:05.1011316Z >>> from triton import language as tl 2024-08-06T21:24:05.1011728Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-06T21:24:05.1012255Z >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton 2024-08-06T21:24:05.1012683Z >>> 2024-08-06T21:24:05.1012900Z >>> @triton.jit 2024-08-06T21:24:05.1013153Z >>> def add_kernel( 2024-08-06T21:24:05.1013408Z >>> in_ptr0, 2024-08-06T21:24:05.1013664Z >>> in_ptr1, 2024-08-06T21:24:05.1013916Z >>> out_ptr, 2024-08-06T21:24:05.1014159Z >>> n_elements, 2024-08-06T21:24:05.1014444Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-06T21:24:05.1014746Z >>> ): 2024-08-06T21:24:05.1014991Z >>> pid = tl.program_id(axis=0) 2024-08-06T21:24:05.1015367Z >>> block_start = pid * BLOCK_SIZE 2024-08-06T21:24:05.1015728Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-06T21:24:05.1016110Z >>> mask = offsets < n_elements 2024-08-06T21:24:05.1016457Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-06T21:24:05.1016814Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-06T21:24:05.1017158Z >>> output = x + y 2024-08-06T21:24:05.1017488Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-06T21:24:05.1017822Z >>> 2024-08-06T21:24:05.1018043Z >>> def add(x, y): 2024-08-06T21:24:05.1018337Z >>> output = torch.empty_like(x) 2024-08-06T21:24:05.1018669Z >>> n_elements = output.numel() 2024-08-06T21:24:05.1018981Z >>> 2024-08-06T21:24:05.1019211Z >>> def grid_fn(meta): 2024-08-06T21:24:05.1019605Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-06T21:24:05.1019984Z >>> 2024-08-06T21:24:05.1020321Z >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-08-06T21:24:05.1020746Z >>> return output 2024-08-06T21:24:05.1021017Z >>> 2024-08-06T21:24:05.1021257Z >>> x = torch.randn(3, device="cuda") 2024-08-06T21:24:05.1021587Z >>> y = torch.randn(3, device="cuda") 2024-08-06T21:24:05.1021915Z >>> gm = make_fx(add)(x, y) 2024-08-06T21:24:05.1022214Z >>> print(gm.code) 2024-08-06T21:24:05.1022501Z >>> # def forward(self, x_1, y_1): 2024-08-06T21:24:05.1022963Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-08-06T21:24:05.1023567Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-08-06T21:24:05.1024052Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-08-06T21:24:05.1024421Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-08-06T21:24:05.1024813Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-08-06T21:24:05.1025207Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-08-06T21:24:05.1025642Z >>> # }) 2024-08-06T21:24:05.1025918Z >>> # return empty_like 2024-08-06T21:24:05.1026116Z 2024-08-06T21:24:05.1026200Z 2024-08-06T21:24:05.1026578Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.1027030Z 2024-08-06T21:24:05.1696916Z 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. 2024-08-06T21:24:05.1697900Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.1698434Z 2024-08-06T21:24:05.1698702Z Raises an AssertionError if two items are not equal up to desired 2024-08-06T21:24:05.1699127Z precision. 2024-08-06T21:24:05.1699337Z 2024-08-06T21:24:05.1699623Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-06T21:24:05.1700205Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-06T21:24:05.1700749Z instead of this function for more consistent floating point 2024-08-06T21:24:05.1701152Z comparisons. 2024-08-06T21:24:05.1701318Z 2024-08-06T21:24:05.1701592Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-08-06T21:24:05.1701925Z 2024-08-06T21:24:05.1702102Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-08-06T21:24:05.1702419Z 2024-08-06T21:24:05.1702647Z That is a looser test than originally documented, but agrees with what the 2024-08-06T21:24:05.1703363Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-08-06T21:24:05.1704219Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-08-06T21:24:05.1704820Z delegates to assert_array_almost_equal 2024-08-06T21:24:05.1705108Z 2024-08-06T21:24:05.1705209Z Parameters 2024-08-06T21:24:05.1705475Z ---------- 2024-08-06T21:24:05.1705873Z actual : array_like 2024-08-06T21:24:05.1706175Z The object to check. 2024-08-06T21:24:05.1706442Z desired : array_like 2024-08-06T21:24:05.1706696Z The expected object. 2024-08-06T21:24:05.1707082Z decimal : int, optional 2024-08-06T21:24:05.1707351Z Desired precision, default is 7. 2024-08-06T21:24:05.1707725Z err_msg : str, optional 2024-08-06T21:24:05.1708041Z The error message to be printed in case of failure. 2024-08-06T21:24:05.1708530Z verbose : bool, optional 2024-08-06T21:24:05.1708993Z If True, the conflicting values are appended to the error message. 2024-08-06T21:24:05.1709311Z 2024-08-06T21:24:05.1709406Z Raises 2024-08-06T21:24:05.1709640Z ------ 2024-08-06T21:24:05.1709855Z AssertionError 2024-08-06T21:24:05.1710241Z If actual and desired are not equal up to specified precision. 2024-08-06T21:24:05.1710665Z 2024-08-06T21:24:05.1710751Z See Also 2024-08-06T21:24:05.1710961Z -------- 2024-08-06T21:24:05.1711324Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-06T21:24:05.1711806Z relative and/or absolute precision. 2024-08-06T21:24:05.1712271Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-06T21:24:05.1712592Z 2024-08-06T21:24:05.1712692Z Examples 2024-08-06T21:24:05.1712888Z -------- 2024-08-06T21:24:05.1713174Z >>> from torch._numpy.testing import assert_almost_equal 2024-08-06T21:24:05.1713586Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-08-06T21:24:05.1713994Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-08-06T21:24:05.1714396Z Traceback (most recent call last): 2024-08-06T21:24:05.1714689Z ... 2024-08-06T21:24:05.1714889Z AssertionError: 2024-08-06T21:24:05.1715154Z Arrays are not almost equal to 10 decimals 2024-08-06T21:24:05.1715478Z ACTUAL: 2.3333333333333 2024-08-06T21:24:05.1715724Z DESIRED: 2.33333334 2024-08-06T21:24:05.1715881Z 2024-08-06T21:24:05.1716027Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-08-06T21:24:05.1716422Z ... np.array([1.0,2.33333334]), decimal=9) 2024-08-06T21:24:05.1716762Z Traceback (most recent call last): 2024-08-06T21:24:05.1717188Z ... 2024-08-06T21:24:05.1717408Z AssertionError: 2024-08-06T21:24:05.1717661Z Arrays are not almost equal to 9 decimals 2024-08-06T21:24:05.1717985Z 2024-08-06T21:24:05.1718227Z Mismatched elements: 1 / 2 (50%) 2024-08-06T21:24:05.1718542Z Max absolute difference: 6.666699636781459e-09 2024-08-06T21:24:05.1718914Z Max relative difference: 2.8571569790287484e-09 2024-08-06T21:24:05.1719293Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-06T21:24:05.1719665Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-06T21:24:05.1719919Z 2024-08-06T21:24:05.1719922Z 2024-08-06T21:24:05.1720170Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.1720538Z 2024-08-06T21:24:05.1721169Z 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. 2024-08-06T21:24:05.1722113Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.1722491Z 2024-08-06T21:24:05.1722724Z Raises an AssertionError if two items are not equal up to significant 2024-08-06T21:24:05.1723137Z digits. 2024-08-06T21:24:05.1723263Z 2024-08-06T21:24:05.1723441Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-06T21:24:05.1723905Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-06T21:24:05.1724393Z instead of this function for more consistent floating point 2024-08-06T21:24:05.1724777Z comparisons. 2024-08-06T21:24:05.1724942Z 2024-08-06T21:24:05.1725123Z Given two numbers, check that they are approximately equal. 2024-08-06T21:24:05.1725651Z Approximately equal is defined as the number of significant digits 2024-08-06T21:24:05.1726063Z that agree. 2024-08-06T21:24:05.1726202Z 2024-08-06T21:24:05.1726332Z Parameters 2024-08-06T21:24:05.1726553Z ---------- 2024-08-06T21:24:05.1726759Z actual : scalar 2024-08-06T21:24:05.1727003Z The object to check. 2024-08-06T21:24:05.1727255Z desired : scalar 2024-08-06T21:24:05.1727496Z The expected object. 2024-08-06T21:24:05.1727773Z significant : int, optional 2024-08-06T21:24:05.1728055Z Desired precision, default is 7. 2024-08-06T21:24:05.1728367Z err_msg : str, optional 2024-08-06T21:24:05.1728680Z The error message to be printed in case of failure. 2024-08-06T21:24:05.1729032Z verbose : bool, optional 2024-08-06T21:24:05.1729406Z If True, the conflicting values are appended to the error message. 2024-08-06T21:24:05.1729724Z 2024-08-06T21:24:05.1729820Z Raises 2024-08-06T21:24:05.1730009Z ------ 2024-08-06T21:24:05.1730226Z AssertionError 2024-08-06T21:24:05.1730594Z If actual and desired are not equal up to specified precision. 2024-08-06T21:24:05.1730896Z 2024-08-06T21:24:05.1730982Z See Also 2024-08-06T21:24:05.1731196Z -------- 2024-08-06T21:24:05.1731561Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-06T21:24:05.1732042Z relative and/or absolute precision. 2024-08-06T21:24:05.1732503Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-06T21:24:05.1732823Z 2024-08-06T21:24:05.1732925Z Examples 2024-08-06T21:24:05.1733127Z -------- 2024-08-06T21:24:05.1733529Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-08-06T21:24:05.1734179Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-08-06T21:24:05.1734663Z ... significant=8) 2024-08-06T21:24:05.1735148Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-08-06T21:24:05.1735644Z ... significant=8) 2024-08-06T21:24:05.1735979Z Traceback (most recent call last): 2024-08-06T21:24:05.1736280Z ... 2024-08-06T21:24:05.1736503Z AssertionError: 2024-08-06T21:24:05.1736761Z Items are not equal to 8 significant digits: 2024-08-06T21:24:05.1737095Z ACTUAL: 1.234567e-21 2024-08-06T21:24:05.1737415Z DESIRED: 1.2345672e-21 2024-08-06T21:24:05.1737570Z 2024-08-06T21:24:05.1737728Z the evaluated condition that raises the exception is 2024-08-06T21:24:05.1738007Z 2024-08-06T21:24:05.1738178Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-08-06T21:24:05.1738550Z True 2024-08-06T21:24:05.1738658Z 2024-08-06T21:24:05.1738662Z 2024-08-06T21:24:05.1738930Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.1739296Z 2024-08-06T21:24:05.1739851Z 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. 2024-08-06T21:24:05.1740784Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.1741163Z 2024-08-06T21:24:05.1741381Z Raises an AssertionError if two array_like objects are not equal. 2024-08-06T21:24:05.1741698Z 2024-08-06T21:24:05.1741903Z Given two array_like objects, check that the shape is equal and all 2024-08-06T21:24:05.1742680Z elements of these objects are equal (but see the Notes for the special 2024-08-06T21:24:05.1743229Z handling of a scalar). An exception is raised at shape mismatch or 2024-08-06T21:24:05.1743761Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-08-06T21:24:05.1744331Z are compared like numbers, no assertion is raised if both objects have 2024-08-06T21:24:05.1744775Z NaNs in the same positions. 2024-08-06T21:24:05.1744956Z 2024-08-06T21:24:05.1745193Z The usual caution for verifying equality with floating point numbers is 2024-08-06T21:24:05.1745615Z advised. 2024-08-06T21:24:05.1745744Z 2024-08-06T21:24:05.1745837Z Parameters 2024-08-06T21:24:05.1746053Z ---------- 2024-08-06T21:24:05.1746257Z x : array_like 2024-08-06T21:24:05.1746497Z The actual object to check. 2024-08-06T21:24:05.1746909Z y : array_like 2024-08-06T21:24:05.1747157Z The desired, expected object. 2024-08-06T21:24:05.1747458Z err_msg : str, optional 2024-08-06T21:24:05.1747766Z The error message to be printed in case of failure. 2024-08-06T21:24:05.1748138Z verbose : bool, optional 2024-08-06T21:24:05.1748515Z If True, the conflicting values are appended to the error message. 2024-08-06T21:24:05.1748927Z strict : bool, optional 2024-08-06T21:24:05.1749295Z If True, raise an AssertionError when either the shape or the data 2024-08-06T21:24:05.1749786Z type of the array_like objects does not match. The special 2024-08-06T21:24:05.1750269Z handling for scalars mentioned in the Notes section is disabled. 2024-08-06T21:24:05.1750595Z 2024-08-06T21:24:05.1750684Z Raises 2024-08-06T21:24:05.1750885Z ------ 2024-08-06T21:24:05.1751137Z AssertionError 2024-08-06T21:24:05.1751412Z If actual and desired objects are not equal. 2024-08-06T21:24:05.1751653Z 2024-08-06T21:24:05.1751752Z See Also 2024-08-06T21:24:05.1751949Z -------- 2024-08-06T21:24:05.1752312Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-06T21:24:05.1752803Z relative and/or absolute precision. 2024-08-06T21:24:05.1753240Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-06T21:24:05.1753572Z 2024-08-06T21:24:05.1753655Z Notes 2024-08-06T21:24:05.1753861Z ----- 2024-08-06T21:24:05.1754153Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-08-06T21:24:05.1754678Z function checks that each element of the array_like object is equal to 2024-08-06T21:24:05.1755243Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-08-06T21:24:05.1755579Z 2024-08-06T21:24:05.1755665Z Examples 2024-08-06T21:24:05.1755873Z -------- 2024-08-06T21:24:05.1756128Z The first assert does not raise an exception: 2024-08-06T21:24:05.1756366Z 2024-08-06T21:24:05.1756517Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-08-06T21:24:05.1756906Z ... [np.exp(0),2.33333, np.nan]) 2024-08-06T21:24:05.1757150Z 2024-08-06T21:24:05.1757453Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-08-06T21:24:05.1757914Z functions for these cases instead: 2024-08-06T21:24:05.1758115Z 2024-08-06T21:24:05.1758257Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-08-06T21:24:05.1758640Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-08-06T21:24:05.1758998Z ... rtol=1e-10, atol=0) 2024-08-06T21:24:05.1759218Z 2024-08-06T21:24:05.1759419Z As mentioned in the Notes section, `assert_array_equal` has special 2024-08-06T21:24:05.1759966Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-08-06T21:24:05.1760303Z 2024-08-06T21:24:05.1760460Z >>> x = np.full((2, 5), fill_value=3) 2024-08-06T21:24:05.1760870Z >>> np.testing.assert_array_equal(x, 3) 2024-08-06T21:24:05.1761100Z 2024-08-06T21:24:05.1761373Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-08-06T21:24:05.1761803Z array: 2024-08-06T21:24:05.1761924Z 2024-08-06T21:24:05.1762123Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-08-06T21:24:05.1762504Z Traceback (most recent call last): 2024-08-06T21:24:05.1762806Z ... 2024-08-06T21:24:05.1763008Z AssertionError: 2024-08-06T21:24:05.1763251Z Arrays are not equal 2024-08-06T21:24:05.1763498Z 2024-08-06T21:24:05.1763714Z (shapes (2, 5), () mismatch) 2024-08-06T21:24:05.1763995Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-08-06T21:24:05.1764290Z [3, 3, 3, 3, 3]]) 2024-08-06T21:24:05.1764536Z y: torch.ndarray(3) 2024-08-06T21:24:05.1764696Z 2024-08-06T21:24:05.1764908Z The `strict` parameter also ensures that the array data types match: 2024-08-06T21:24:05.1765231Z 2024-08-06T21:24:05.1765342Z >>> x = np.array([2, 2, 2]) 2024-08-06T21:24:05.1765628Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-08-06T21:24:05.1766017Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-08-06T21:24:05.1766464Z Traceback (most recent call last): 2024-08-06T21:24:05.1766753Z ... 2024-08-06T21:24:05.1766977Z AssertionError: 2024-08-06T21:24:05.1767225Z Arrays are not equal 2024-08-06T21:24:05.1767462Z 2024-08-06T21:24:05.1767739Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-08-06T21:24:05.1768104Z x: torch.ndarray([2, 2, 2]) 2024-08-06T21:24:05.1768386Z y: torch.ndarray([2., 2., 2.]) 2024-08-06T21:24:05.1768588Z 2024-08-06T21:24:05.1768842Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.1769209Z 2024-08-06T21:24:05.1769818Z 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. 2024-08-06T21:24:05.1770768Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.1771198Z 2024-08-06T21:24:05.1771417Z Raises an AssertionError if two objects are not equal up to desired 2024-08-06T21:24:05.1771850Z precision. 2024-08-06T21:24:05.1771977Z 2024-08-06T21:24:05.1772176Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-06T21:24:05.1772629Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-06T21:24:05.1773116Z instead of this function for more consistent floating point 2024-08-06T21:24:05.1773521Z comparisons. 2024-08-06T21:24:05.1773674Z 2024-08-06T21:24:05.1773917Z The test verifies identical shapes and that the elements of ``actual`` and 2024-08-06T21:24:05.1774375Z ``desired`` satisfy. 2024-08-06T21:24:05.1774526Z 2024-08-06T21:24:05.1774673Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-08-06T21:24:05.1774912Z 2024-08-06T21:24:05.1775139Z That is a looser test than originally documented, but agrees with what the 2024-08-06T21:24:05.1775744Z actual implementation did up to rounding vagaries. An exception is raised 2024-08-06T21:24:05.1776344Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-08-06T21:24:05.1776961Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-08-06T21:24:05.1777487Z objects have NaNs in the same positions. 2024-08-06T21:24:05.1777725Z 2024-08-06T21:24:05.1777815Z Parameters 2024-08-06T21:24:05.1778034Z ---------- 2024-08-06T21:24:05.1778242Z x : array_like 2024-08-06T21:24:05.1778483Z The actual object to check. 2024-08-06T21:24:05.1778766Z y : array_like 2024-08-06T21:24:05.1779000Z The desired, expected object. 2024-08-06T21:24:05.1779301Z decimal : int, optional 2024-08-06T21:24:05.1779578Z Desired precision, default is 6. 2024-08-06T21:24:05.1779876Z err_msg : str, optional 2024-08-06T21:24:05.1780189Z The error message to be printed in case of failure. 2024-08-06T21:24:05.1780554Z verbose : bool, optional 2024-08-06T21:24:05.1780920Z If True, the conflicting values are appended to the error message. 2024-08-06T21:24:05.1781246Z 2024-08-06T21:24:05.1781331Z Raises 2024-08-06T21:24:05.1781537Z ------ 2024-08-06T21:24:05.1781739Z AssertionError 2024-08-06T21:24:05.1782078Z If actual and desired are not equal up to specified precision. 2024-08-06T21:24:05.1782379Z 2024-08-06T21:24:05.1782476Z See Also 2024-08-06T21:24:05.1782670Z -------- 2024-08-06T21:24:05.1783030Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-06T21:24:05.1783512Z relative and/or absolute precision. 2024-08-06T21:24:05.1783947Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-06T21:24:05.1784278Z 2024-08-06T21:24:05.1784364Z Examples 2024-08-06T21:24:05.1784571Z -------- 2024-08-06T21:24:05.1784807Z the first assert does not raise an exception 2024-08-06T21:24:05.1785052Z 2024-08-06T21:24:05.1785221Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-08-06T21:24:05.1785621Z ... [1.0,2.333,np.nan]) 2024-08-06T21:24:05.1785848Z 2024-08-06T21:24:05.1786022Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-06T21:24:05.1786473Z ... [1.0,2.33339,np.nan], decimal=5) 2024-08-06T21:24:05.1786925Z Traceback (most recent call last): 2024-08-06T21:24:05.1787210Z ... 2024-08-06T21:24:05.1787427Z AssertionError: 2024-08-06T21:24:05.1787695Z Arrays are not almost equal to 5 decimals 2024-08-06T21:24:05.1788006Z 2024-08-06T21:24:05.1788243Z Mismatched elements: 1 / 3 (33.3%) 2024-08-06T21:24:05.1788575Z Max absolute difference: 5.999999999994898e-05 2024-08-06T21:24:05.1788930Z Max relative difference: 2.5713661239633743e-05 2024-08-06T21:24:05.1789330Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-06T21:24:05.1789763Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-08-06T21:24:05.1790029Z 2024-08-06T21:24:05.1790239Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-06T21:24:05.1790653Z ... [1.0,2.33333, 5], decimal=5) 2024-08-06T21:24:05.1791007Z Traceback (most recent call last): 2024-08-06T21:24:05.1791287Z ... 2024-08-06T21:24:05.1791502Z AssertionError: 2024-08-06T21:24:05.1791769Z Arrays are not almost equal to 5 decimals 2024-08-06T21:24:05.1792075Z 2024-08-06T21:24:05.1792312Z x and y nan location mismatch: 2024-08-06T21:24:05.1792664Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-06T21:24:05.1793085Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-08-06T21:24:05.1793365Z 2024-08-06T21:24:05.1793370Z 2024-08-06T21:24:05.1793623Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.1793991Z 2024-08-06T21:24:05.1794612Z 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. 2024-08-06T21:24:05.1795578Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.1796157Z Context manager that resets warning registry for catching warnings 2024-08-06T21:24:05.1796496Z 2024-08-06T21:24:05.1796818Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-08-06T21:24:05.1797411Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-08-06T21:24:05.1797982Z it impossible to retrigger the warning in this module, whatever you put in 2024-08-06T21:24:05.1798583Z the warnings filters. This context manager accepts a sequence of `modules` 2024-08-06T21:24:05.1799085Z as a keyword argument to its constructor and: 2024-08-06T21:24:05.1799331Z 2024-08-06T21:24:05.1799557Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-08-06T21:24:05.1799995Z on entry; 2024-08-06T21:24:05.1800323Z * resets ``__warningregistry__`` to its previous state on exit. 2024-08-06T21:24:05.1800626Z 2024-08-06T21:24:05.1800862Z This makes it possible to trigger any warning afresh inside the context 2024-08-06T21:24:05.1801381Z manager without disturbing the state of warnings outside. 2024-08-06T21:24:05.1801685Z 2024-08-06T21:24:05.1801923Z For compatibility with Python 3.0, please consider all arguments to be 2024-08-06T21:24:05.1802376Z keyword-only. 2024-08-06T21:24:05.1802518Z 2024-08-06T21:24:05.1802609Z Parameters 2024-08-06T21:24:05.1802841Z ---------- 2024-08-06T21:24:05.1803084Z record : bool, optional 2024-08-06T21:24:05.1803443Z Specifies whether warnings should be captured by a custom 2024-08-06T21:24:05.1803986Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-08-06T21:24:05.1804554Z returned by the context manager. Otherwise None is returned by the 2024-08-06T21:24:05.1805099Z context manager. The objects appended to the list are arguments whose 2024-08-06T21:24:05.1805624Z attributes mirror the arguments to ``showwarning()``. 2024-08-06T21:24:05.1806023Z modules : sequence, optional 2024-08-06T21:24:05.1806464Z Sequence of modules for which to reset warnings registry on entry and 2024-08-06T21:24:05.1807005Z restore on exit. To work correctly, all 'ignore' filters should 2024-08-06T21:24:05.1807431Z filter by one of these modules. 2024-08-06T21:24:05.1807639Z 2024-08-06T21:24:05.1807730Z Examples 2024-08-06T21:24:05.1807954Z -------- 2024-08-06T21:24:05.1808186Z >>> import warnings 2024-08-06T21:24:05.1808523Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-08-06T21:24:05.1808948Z ... modules=[np.core.fromnumeric]): 2024-08-06T21:24:05.1809306Z ... warnings.simplefilter('always') 2024-08-06T21:24:05.1809747Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-08-06T21:24:05.1810258Z ... # do something that raises a warning but ignore those in 2024-08-06T21:24:05.1810680Z ... # np.core.fromnumeric 2024-08-06T21:24:05.1810948Z 2024-08-06T21:24:05.1811312Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.1811680Z 2024-08-06T21:24:05.4213511Z 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=355. 2024-08-06T21:24:05.4214463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.4215067Z Applies a 1D convolution over a quantized input signal composed of 2024-08-06T21:24:05.4215534Z several quantized input planes. 2024-08-06T21:24:05.4215744Z 2024-08-06T21:24:05.4215972Z For details on input arguments, parameters, and implementation see 2024-08-06T21:24:05.4216400Z :class:`~torch.nn.Conv1d`. 2024-08-06T21:24:05.4216600Z 2024-08-06T21:24:05.4216705Z .. note:: 2024-08-06T21:24:05.4217047Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-08-06T21:24:05.4217371Z 2024-08-06T21:24:05.4217459Z .. note:: 2024-08-06T21:24:05.4217779Z Only `torch.quint8` is supported for the input data type. 2024-08-06T21:24:05.4218071Z 2024-08-06T21:24:05.4218076Z 2024-08-06T21:24:05.4218179Z Attributes: 2024-08-06T21:24:05.4218772Z weight (Tensor): packed tensor derived from the learnable weight 2024-08-06T21:24:05.4219211Z parameter. 2024-08-06T21:24:05.4219567Z scale (Tensor): scalar for the output scale 2024-08-06T21:24:05.4219973Z zero_point (Tensor): scalar for the output zero point 2024-08-06T21:24:05.4220260Z 2024-08-06T21:24:05.4220411Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-08-06T21:24:05.4220669Z 2024-08-06T21:24:05.4220776Z Examples:: 2024-08-06T21:24:05.4220909Z 2024-08-06T21:24:05.4221057Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-08-06T21:24:05.4221458Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-08-06T21:24:05.4221823Z >>> input = torch.randn(20, 16, 100) 2024-08-06T21:24:05.4222161Z >>> # quantize input to quint8 2024-08-06T21:24:05.4222470Z >>> # xdoctest: +SKIP 2024-08-06T21:24:05.4222870Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-08-06T21:24:05.4223330Z ... dtype=torch.quint8) 2024-08-06T21:24:05.4223668Z >>> output = m(q_input) 2024-08-06T21:24:05.4223862Z 2024-08-06T21:24:05.4223944Z 2024-08-06T21:24:05.4224315Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.4224680Z 2024-08-06T21:24:05.4406368Z 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. 2024-08-06T21:24:05.4407896Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.4408812Z A quantized long short-term memory (LSTM). 2024-08-06T21:24:05.4409240Z 2024-08-06T21:24:05.4409699Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-08-06T21:24:05.4410570Z 2024-08-06T21:24:05.4410716Z Attributes: 2024-08-06T21:24:05.4411054Z layers : instances of the `_LSTMLayer` 2024-08-06T21:24:05.4411297Z 2024-08-06T21:24:05.4411410Z .. note:: 2024-08-06T21:24:05.4411750Z To access the weights and biases, you need to access them per layer. 2024-08-06T21:24:05.4412260Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-08-06T21:24:05.4412543Z 2024-08-06T21:24:05.4412651Z Examples:: 2024-08-06T21:24:05.4412875Z >>> # xdoctest: +SKIP 2024-08-06T21:24:05.4413167Z >>> custom_module_config = { 2024-08-06T21:24:05.4413517Z ... 'float_to_observed_custom_module_class': { 2024-08-06T21:24:05.4413883Z ... nn.LSTM: nn.quantizable.LSTM, 2024-08-06T21:24:05.4414206Z ... }, 2024-08-06T21:24:05.4414600Z ... 'observed_to_quantized_custom_module_class': { 2024-08-06T21:24:05.4414996Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-08-06T21:24:05.4415347Z ... } 2024-08-06T21:24:05.4415574Z ... } 2024-08-06T21:24:05.4415920Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-08-06T21:24:05.4416477Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-08-06T21:24:05.4416893Z 2024-08-06T21:24:05.4417254Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.4417632Z 2024-08-06T21:24:05.5263494Z 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=229. 2024-08-06T21:24:05.5264597Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.5265148Z Squashes the sparse masks into the appropriate tensors. 2024-08-06T21:24:05.5265460Z 2024-08-06T21:24:05.5265672Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-08-06T21:24:05.5266197Z the module will have a `sparse_params` dict attached to it. 2024-08-06T21:24:05.5266492Z 2024-08-06T21:24:05.5266615Z Args: 2024-08-06T21:24:05.5267224Z params_to_keep: List of keys to save in the module or a dict 2024-08-06T21:24:05.5267700Z representing the modules and keys that will have 2024-08-06T21:24:05.5268104Z sparsity parameters saved 2024-08-06T21:24:05.5268548Z params_to_keep_per_layer: Dict to specify the params that should be 2024-08-06T21:24:05.5269045Z saved for specific layers. The keys in the dict 2024-08-06T21:24:05.5269479Z should be the module fqn, while the values should 2024-08-06T21:24:05.5269916Z be a list of strings with the names of the variables 2024-08-06T21:24:05.5270327Z to save in the `sparse_params` 2024-08-06T21:24:05.5270572Z 2024-08-06T21:24:05.5270668Z Examples: 2024-08-06T21:24:05.5270955Z >>> # xdoctest: +SKIP("locals are undefined") 2024-08-06T21:24:05.5271316Z >>> # Don't save any sparse params 2024-08-06T21:24:05.5271666Z >>> sparsifier.squash_mask() 2024-08-06T21:24:05.5272031Z >>> hasattr(model.submodule1, 'sparse_params') 2024-08-06T21:24:05.5272365Z False 2024-08-06T21:24:05.5272515Z 2024-08-06T21:24:05.5272635Z >>> # Keep sparse params per layer 2024-08-06T21:24:05.5272989Z >>> sparsifier.squash_mask( 2024-08-06T21:24:05.5273316Z ... params_to_keep_per_layer={ 2024-08-06T21:24:05.5273682Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-08-06T21:24:05.5274064Z ... 'submodule2.linear42': ('baz',) 2024-08-06T21:24:05.5274385Z ... }) 2024-08-06T21:24:05.5274707Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-06T21:24:05.5275084Z {'foo': 42, 'bar': 24} 2024-08-06T21:24:05.5275493Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-06T21:24:05.5275860Z {'baz': 0.1} 2024-08-06T21:24:05.5276026Z 2024-08-06T21:24:05.5276165Z >>> # Keep sparse params for all layers 2024-08-06T21:24:05.5276566Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-08-06T21:24:05.5277016Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-06T21:24:05.5277388Z {'foo': 42, 'bar': 24} 2024-08-06T21:24:05.5277730Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-06T21:24:05.5278101Z {'foo': 42, 'bar': 24} 2024-08-06T21:24:05.5278287Z 2024-08-06T21:24:05.5278495Z >>> # Keep some sparse params for all layers, and specific ones for 2024-08-06T21:24:05.5278898Z >>> # some other layers 2024-08-06T21:24:05.5279261Z >>> sparsifier.squash_mask( 2024-08-06T21:24:05.5279594Z ... params_to_keep=('foo', 'bar'), 2024-08-06T21:24:05.5279931Z ... params_to_keep_per_layer={ 2024-08-06T21:24:05.5280283Z ... 'submodule2.linear42': ('baz',) 2024-08-06T21:24:05.5280619Z ... }) 2024-08-06T21:24:05.5280915Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-06T21:24:05.5281289Z {'foo': 42, 'bar': 24} 2024-08-06T21:24:05.5281640Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-06T21:24:05.5282011Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-08-06T21:24:05.5282317Z 2024-08-06T21:24:05.5282693Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.5283059Z 2024-08-06T21:24:05.6036764Z 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. 2024-08-06T21:24:05.6037830Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.6038229Z 2024-08-06T21:24:05.6038481Z Config object that specifies the supported data types passed as arguments to 2024-08-06T21:24:05.6039323Z quantize ops in the reference model spec, for input and output activations, 2024-08-06T21:24:05.6039772Z weights, and biases. 2024-08-06T21:24:05.6039938Z 2024-08-06T21:24:05.6040093Z For example, consider the following reference model: 2024-08-06T21:24:05.6040359Z 2024-08-06T21:24:05.6040532Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-08-06T21:24:05.6040794Z 2024-08-06T21:24:05.6041023Z The pattern in the square brackets refers to the reference pattern of 2024-08-06T21:24:05.6041581Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-08-06T21:24:05.6042166Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-08-06T21:24:05.6042979Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-08-06T21:24:05.6043538Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-08-06T21:24:05.6043998Z the second quantize op (quant2). 2024-08-06T21:24:05.6044197Z 2024-08-06T21:24:05.6044438Z Note that the dtype here does not refer to the interface dtypes of the 2024-08-06T21:24:05.6044976Z op. For example, the "input dtype" here is not the dtype of the input 2024-08-06T21:24:05.6045518Z tensor passed to the quantized linear op. Though it can still be the 2024-08-06T21:24:05.6046050Z same as the interface dtype, this is not always the case, e.g. the 2024-08-06T21:24:05.6046569Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-08-06T21:24:05.6047117Z specified in the DTypeConfig would still be quint8. The semantics of 2024-08-06T21:24:05.6047657Z dtypes here are the same as the semantics of the dtypes specified in 2024-08-06T21:24:05.6048064Z the observers. 2024-08-06T21:24:05.6048214Z 2024-08-06T21:24:05.6048418Z These dtypes are matched against the ones specified in the user's 2024-08-06T21:24:05.6048952Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-08-06T21:24:05.6049573Z specified in the DTypeConfig (if any), then we will quantize the given 2024-08-06T21:24:05.6050147Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-08-06T21:24:05.6050609Z the pattern will not be quantized. 2024-08-06T21:24:05.6050811Z 2024-08-06T21:24:05.6050945Z Example usage:: 2024-08-06T21:24:05.6051083Z 2024-08-06T21:24:05.6051187Z >>> # xdoctest: +SKIP(failing) 2024-08-06T21:24:05.6051497Z >>> dtype_config1 = DTypeConfig( 2024-08-06T21:24:05.6051817Z ... input_dtype=torch.quint8, 2024-08-06T21:24:05.6052124Z ... output_dtype=torch.quint8, 2024-08-06T21:24:05.6052446Z ... weight_dtype=torch.qint8, 2024-08-06T21:24:05.6052753Z ... bias_dtype=torch.float) 2024-08-06T21:24:05.6052963Z 2024-08-06T21:24:05.6053130Z >>> dtype_config2 = DTypeConfig( 2024-08-06T21:24:05.6053471Z ... input_dtype=DTypeWithConstraints( 2024-08-06T21:24:05.6053814Z ... dtype=torch.quint8, 2024-08-06T21:24:05.6054111Z ... quant_min_lower_bound=0, 2024-08-06T21:24:05.6054464Z ... quant_max_upper_bound=255, 2024-08-06T21:24:05.6054770Z ... ), 2024-08-06T21:24:05.6055021Z ... output_dtype=DTypeWithConstraints( 2024-08-06T21:24:05.6055369Z ... dtype=torch.quint8, 2024-08-06T21:24:05.6055680Z ... quant_min_lower_bound=0, 2024-08-06T21:24:05.6055994Z ... quant_max_upper_bound=255, 2024-08-06T21:24:05.6056302Z ... ), 2024-08-06T21:24:05.6056564Z ... weight_dtype=DTypeWithConstraints( 2024-08-06T21:24:05.6056897Z ... dtype=torch.qint8, 2024-08-06T21:24:05.6057221Z ... quant_min_lower_bound=-128, 2024-08-06T21:24:05.6057556Z ... quant_max_upper_bound=127, 2024-08-06T21:24:05.6057851Z ... ), 2024-08-06T21:24:05.6058091Z ... bias_dtype=torch.float) 2024-08-06T21:24:05.6058287Z 2024-08-06T21:24:05.6058406Z >>> dtype_config1.input_dtype 2024-08-06T21:24:05.6058688Z torch.quint8 2024-08-06T21:24:05.6058837Z 2024-08-06T21:24:05.6058942Z >>> dtype_config2.input_dtype 2024-08-06T21:24:05.6070477Z torch.quint8 2024-08-06T21:24:05.6070673Z 2024-08-06T21:24:05.6070817Z >>> dtype_config2.input_dtype_with_constraints 2024-08-06T21:24:05.6071639Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-08-06T21:24:05.6072293Z 2024-08-06T21:24:05.6072564Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.6072934Z 2024-08-06T21:24:05.7092572Z 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. 2024-08-06T21:24:05.7093885Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.7094277Z 2024-08-06T21:24:05.7094564Z Takes in optional filter values and generates two tables with desired information. 2024-08-06T21:24:05.7094975Z 2024-08-06T21:24:05.7095206Z The generated tables are presented in both a list-of-lists format 2024-08-06T21:24:05.7095529Z 2024-08-06T21:24:05.7095738Z The reason for the two tables are that they handle different things: 2024-08-06T21:24:05.7096237Z 1.) the first table handles all tensor level information 2024-08-06T21:24:05.7096743Z 2.) the second table handles and displays all channel based information 2024-08-06T21:24:05.7097076Z 2024-08-06T21:24:05.7097408Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-06T21:24:05.7098245Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-06T21:24:05.7099138Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-06T21:24:05.7099608Z 2024-08-06T21:24:05.7099897Z Tensor table columns: 2024-08-06T21:24:05.7100249Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-06T21:24:05.7100737Z ---- --------- --------- --------- --------- --------- 2024-08-06T21:24:05.7101034Z 2024-08-06T21:24:05.7101214Z Per-Channel table columns: 2024-08-06T21:24:05.7101626Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-06T21:24:05.7102145Z ---- --------- ------- --------- --------- --------- --------- 2024-08-06T21:24:05.7102444Z 2024-08-06T21:24:05.7102531Z Args: 2024-08-06T21:24:05.7102923Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-06T21:24:05.7103414Z contain this filter substring 2024-08-06T21:24:05.7103803Z Default = "", results in all the features being printed 2024-08-06T21:24:05.7104423Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-06T21:24:05.7105049Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-06T21:24:05.7105429Z 2024-08-06T21:24:05.7105548Z Returns a dictionary with two keys: 2024-08-06T21:24:05.7105942Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-08-06T21:24:05.7106349Z "tensor_level_info", "channel_level_info" 2024-08-06T21:24:05.7106776Z Each key maps to a tuple with: 2024-08-06T21:24:05.7107121Z A list of the headers of each table 2024-08-06T21:24:05.7107527Z A list of lists containing the table information row by row 2024-08-06T21:24:05.7108004Z The 0th index row will contain the headers of the columns 2024-08-06T21:24:05.7108417Z The rest of the rows will contain data 2024-08-06T21:24:05.7108650Z 2024-08-06T21:24:05.7108742Z Example Use: 2024-08-06T21:24:05.7109007Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:05.7109398Z >>> mod_report_visualizer.generate_filtered_tables( 2024-08-06T21:24:05.7109779Z ... feature_filter = "per_channel_min", 2024-08-06T21:24:05.7110125Z ... module_fqn_filter = "block1" 2024-08-06T21:24:05.7110719Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-08-06T21:24:05.7111110Z 2024-08-06T21:24:05.7111366Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.7111747Z 2024-08-06T21:24:05.7112664Z 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. 2024-08-06T21:24:05.7113943Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.7114319Z 2024-08-06T21:24:05.7114678Z Takes in optional filter values and prints out formatted tables of the information. 2024-08-06T21:24:05.7115078Z 2024-08-06T21:24:05.7115419Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-08-06T21:24:05.7116053Z 1.) the first table handles all tensor level information 2024-08-06T21:24:05.7116557Z 2.) the second table handles and displays all channel based information 2024-08-06T21:24:05.7116885Z 2024-08-06T21:24:05.7117217Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-06T21:24:05.7117982Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-06T21:24:05.7118786Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-06T21:24:05.7119257Z 2024-08-06T21:24:05.7119373Z Tensor table columns: 2024-08-06T21:24:05.7119721Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-06T21:24:05.7120193Z ---- --------- --------- --------- --------- --------- 2024-08-06T21:24:05.7120457Z 2024-08-06T21:24:05.7120621Z Per-Channel table columns: 2024-08-06T21:24:05.7120798Z 2024-08-06T21:24:05.7121021Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-06T21:24:05.7121525Z ---- --------- ------- --------- --------- --------- --------- 2024-08-06T21:24:05.7121814Z 2024-08-06T21:24:05.7121901Z Args: 2024-08-06T21:24:05.7122280Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-06T21:24:05.7122785Z contain this filter substring 2024-08-06T21:24:05.7123174Z Default = "", results in all the features being printed 2024-08-06T21:24:05.7123719Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-06T21:24:05.7124341Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-06T21:24:05.7125182Z 2024-08-06T21:24:05.7125326Z Example Use: 2024-08-06T21:24:05.7125598Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:05.7126003Z >>> mod_report_visualizer.generate_table_visualization( 2024-08-06T21:24:05.7126414Z ... feature_filter = "per_channel_min", 2024-08-06T21:24:05.7126768Z ... module_fqn_filter = "block1" 2024-08-06T21:24:05.7127065Z ... ) 2024-08-06T21:24:05.7127387Z >>> # prints out neatly formatted table with per_channel_min info 2024-08-06T21:24:05.7127826Z >>> # for all modules in block 1 of the model 2024-08-06T21:24:05.7128065Z 2024-08-06T21:24:05.7128319Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.7128699Z 2024-08-06T21:24:05.7129644Z 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=565. 2024-08-06T21:24:05.7130928Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.7131321Z 2024-08-06T21:24:05.7131560Z Takes in a feature and optional module_filter and plots of the desired data. 2024-08-06T21:24:05.7131916Z 2024-08-06T21:24:05.7132278Z For per channel features, it averages the value across the channels and plots a point 2024-08-06T21:24:05.7132933Z per module. The reason for this is that for models with hundreds of channels, it can 2024-08-06T21:24:05.7133601Z be hard to differentiate one channel line from another, and so the point of generating 2024-08-06T21:24:05.7134276Z a single average point per module is to give a sense of general trends that encourage 2024-08-06T21:24:05.7134762Z further deep dives. 2024-08-06T21:24:05.7134925Z 2024-08-06T21:24:05.7135009Z Note: 2024-08-06T21:24:05.7135396Z Only features in the report that have tensor value data are plottable by this class 2024-08-06T21:24:05.7136032Z When the tensor information is plotted, it will plot: 2024-08-06T21:24:05.7136542Z idx as the x val, feature value as the y_val 2024-08-06T21:24:05.7136963Z When the channel information is plotted, it will plot: 2024-08-06T21:24:05.7137517Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-08-06T21:24:05.7138143Z The reason for this is that we want to be able to compare values across the 2024-08-06T21:24:05.7138725Z channels for same layer, and it will be hard if values are staggered by idx 2024-08-06T21:24:05.7139257Z This means each module is represented by only 1 x value 2024-08-06T21:24:05.7139627Z Args: 2024-08-06T21:24:05.7139965Z feature_filter (str): Filters the features presented to only those that 2024-08-06T21:24:05.7140424Z contain this filter substring 2024-08-06T21:24:05.7140904Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-06T21:24:05.7141537Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-06T21:24:05.7141898Z 2024-08-06T21:24:05.7141991Z Example Use: 2024-08-06T21:24:05.7142256Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:05.7142967Z >>> mod_report_visualizer.generate_plot_visualization( 2024-08-06T21:24:05.7143367Z ... feature_filter = "per_channel_min", 2024-08-06T21:24:05.7143715Z ... module_fqn_filter = "block1" 2024-08-06T21:24:05.7144023Z ... ) 2024-08-06T21:24:05.7144316Z >>> # outputs line plot of per_channel_min information for all 2024-08-06T21:24:05.7144800Z >>> # modules in block1 of model each channel gets it's own line, 2024-08-06T21:24:05.7145280Z >>> # and it's plotted across the in-order modules on the x-axis 2024-08-06T21:24:05.7145569Z 2024-08-06T21:24:05.7145821Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.7146200Z 2024-08-06T21:24:05.7147207Z 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=645. 2024-08-06T21:24:05.7148604Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.7148982Z 2024-08-06T21:24:05.7149283Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-08-06T21:24:05.7149684Z 2024-08-06T21:24:05.7149770Z Note: 2024-08-06T21:24:05.7150164Z Only features in the report that have tensor value data can be viewed as a histogram 2024-08-06T21:24:05.7150820Z If you want to plot a histogram from all the channel values of a specific feature for 2024-08-06T21:24:05.7151443Z a specific model, make sure to specify both the model and the feature properly 2024-08-06T21:24:05.7152057Z in the filters and you should be able to see a distribution of the channel data 2024-08-06T21:24:05.7152417Z 2024-08-06T21:24:05.7152513Z Args: 2024-08-06T21:24:05.7152889Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-06T21:24:05.7153385Z contain this filter substring 2024-08-06T21:24:05.7153765Z Default = "", results in all the features being printed 2024-08-06T21:24:05.7154381Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-06T21:24:05.7155015Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-06T21:24:05.7155612Z num_bins (int, optional): The number of bins to create the histogram with 2024-08-06T21:24:05.7156138Z Default = 10, the values will be split into 10 equal sized bins 2024-08-06T21:24:05.7156459Z 2024-08-06T21:24:05.7156554Z Example Use: 2024-08-06T21:24:05.7156801Z >>> # xdoctest: +SKIP 2024-08-06T21:24:05.7157283Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-08-06T21:24:05.7157821Z ... feature_filter = "per_channel_min", 2024-08-06T21:24:05.7158171Z ... module_fqn_filter = "block1" 2024-08-06T21:24:05.7158479Z ... ) 2024-08-06T21:24:05.7158869Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-08-06T21:24:05.7159532Z information is gathered across all channels for all modules in block 1 for the 2024-08-06T21:24:05.7160133Z per_channel_min and is displayed in a histogram of equally sized bins 2024-08-06T21:24:05.7160464Z 2024-08-06T21:24:05.7160715Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.7161094Z 2024-08-06T21:24:05.9713905Z 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. 2024-08-06T21:24:05.9714961Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.9715343Z 2024-08-06T21:24:05.9715605Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-08-06T21:24:05.9716199Z distribution where all component are from different parameterizations of 2024-08-06T21:24:05.9716784Z the same distribution type. It is parameterized by a `Categorical` 2024-08-06T21:24:05.9717537Z "selecting distribution" (over `k` component) and a component 2024-08-06T21:24:05.9718055Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-08-06T21:24:05.9718541Z (equal to `[k]`) which indexes each (batch of) component. 2024-08-06T21:24:05.9718822Z 2024-08-06T21:24:05.9718933Z Examples:: 2024-08-06T21:24:05.9719056Z 2024-08-06T21:24:05.9719186Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:05.9719611Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-08-06T21:24:05.9720049Z >>> # weighted normal distributions 2024-08-06T21:24:05.9720385Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-06T21:24:05.9720745Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-08-06T21:24:05.9721182Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-06T21:24:05.9721402Z 2024-08-06T21:24:05.9721617Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-08-06T21:24:05.9722061Z >>> # weighted bivariate normal distributions 2024-08-06T21:24:05.9722432Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-06T21:24:05.9722773Z >>> comp = D.Independent(D.Normal( 2024-08-06T21:24:05.9723108Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-08-06T21:24:05.9723474Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-06T21:24:05.9723696Z 2024-08-06T21:24:05.9723890Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-08-06T21:24:05.9724383Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-08-06T21:24:05.9724823Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-08-06T21:24:05.9725166Z >>> comp = D.Independent(D.Normal( 2024-08-06T21:24:05.9725506Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-08-06T21:24:05.9725878Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-06T21:24:05.9726098Z 2024-08-06T21:24:05.9726193Z Args: 2024-08-06T21:24:05.9726504Z mixture_distribution: `torch.distributions.Categorical`-like 2024-08-06T21:24:05.9727022Z instance. Manages the probability of selecting component. 2024-08-06T21:24:05.9727588Z The number of categories must match the rightmost batch 2024-08-06T21:24:05.9728052Z dimension of the `component_distribution`. Must have either 2024-08-06T21:24:05.9728500Z scalar `batch_shape` or `batch_shape` matching 2024-08-06T21:24:05.9728894Z `component_distribution.batch_shape[:-1]` 2024-08-06T21:24:05.9729357Z component_distribution: `torch.distributions.Distribution`-like 2024-08-06T21:24:05.9729880Z instance. Right-most batch dimension indexes component. 2024-08-06T21:24:05.9730171Z 2024-08-06T21:24:05.9730439Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.9730805Z 2024-08-06T21:24:05.9825369Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=110. 2024-08-06T21:24:05.9826376Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.9826860Z 2024-08-06T21:24:05.9827045Z Creates a RelaxedBernoulli distribution, parametrized by 2024-08-06T21:24:05.9827536Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-08-06T21:24:05.9828048Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-08-06T21:24:05.9828572Z so the values are in (0, 1), and has reparametrizable samples. 2024-08-06T21:24:05.9828875Z 2024-08-06T21:24:05.9828980Z Example:: 2024-08-06T21:24:05.9829101Z 2024-08-06T21:24:05.9829250Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-06T21:24:05.9829620Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-08-06T21:24:05.9830010Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-08-06T21:24:05.9830337Z >>> m.sample() 2024-08-06T21:24:05.9830600Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-08-06T21:24:05.9830937Z 2024-08-06T21:24:05.9831036Z Args: 2024-08-06T21:24:05.9831286Z temperature (Tensor): relaxation temperature 2024-08-06T21:24:05.9831720Z probs (Number, Tensor): the probability of sampling `1` 2024-08-06T21:24:05.9832177Z logits (Number, Tensor): the log-odds of sampling `1` 2024-08-06T21:24:05.9832449Z 2024-08-06T21:24:05.9832703Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.9833081Z 2024-08-06T21:24:05.9839921Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=98. 2024-08-06T21:24:05.9840987Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:05.9841369Z 2024-08-06T21:24:05.9841597Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-08-06T21:24:05.9842202Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-08-06T21:24:05.9842927Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-08-06T21:24:05.9843463Z its samples are on simplex, and are reparametrizable. 2024-08-06T21:24:05.9843736Z 2024-08-06T21:24:05.9843837Z Example:: 2024-08-06T21:24:05.9843972Z 2024-08-06T21:24:05.9844112Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-06T21:24:05.9844531Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-08-06T21:24:05.9844917Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-08-06T21:24:05.9845270Z >>> m.sample() 2024-08-06T21:24:05.9845533Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-08-06T21:24:05.9845758Z 2024-08-06T21:24:05.9845844Z Args: 2024-08-06T21:24:05.9846101Z temperature (Tensor): relaxation temperature 2024-08-06T21:24:05.9846472Z probs (Tensor): event probabilities 2024-08-06T21:24:05.9846876Z logits (Tensor): unnormalized log probability for each event 2024-08-06T21:24:05.9847189Z 2024-08-06T21:24:05.9847443Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:05.9847822Z 2024-08-06T21:24:06.5054360Z 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. 2024-08-06T21:24:06.5056244Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:06.5057216Z Asserts that ``actual`` and ``expected`` are close. 2024-08-06T21:24:06.5057689Z 2024-08-06T21:24:06.5058387Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-08-06T21:24:06.5059301Z 2024-08-06T21:24:06.5059493Z .. math:: 2024-08-06T21:24:06.5059720Z 2024-08-06T21:24:06.5060379Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-08-06T21:24:06.5061314Z 2024-08-06T21:24:06.5061955Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-08-06T21:24:06.5063213Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-08-06T21:24:06.5063836Z 2024-08-06T21:24:06.5064197Z In addition, they are only considered close if they have the same 2024-08-06T21:24:06.5064780Z 2024-08-06T21:24:06.5065129Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-08-06T21:24:06.5065921Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-08-06T21:24:06.5066585Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-08-06T21:24:06.5067343Z - stride (if ``check_stride`` is ``True``). 2024-08-06T21:24:06.5067791Z 2024-08-06T21:24:06.5068338Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-08-06T21:24:06.5069139Z 2024-08-06T21:24:06.5069843Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-08-06T21:24:06.5071623Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-08-06T21:24:06.5072982Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-08-06T21:24:06.5074274Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-08-06T21:24:06.5075255Z 2024-08-06T21:24:06.5075809Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-08-06T21:24:06.5077188Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-08-06T21:24:06.5078399Z definition above. 2024-08-06T21:24:06.5078686Z 2024-08-06T21:24:06.5079234Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-08-06T21:24:06.5080886Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-08-06T21:24:06.5082578Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-08-06T21:24:06.5084269Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-08-06T21:24:06.5085664Z their elements are considered close according to the above definition. 2024-08-06T21:24:06.5086344Z 2024-08-06T21:24:06.5086505Z .. note:: 2024-08-06T21:24:06.5086751Z 2024-08-06T21:24:06.5087403Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-08-06T21:24:06.5088848Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-08-06T21:24:06.5090101Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-08-06T21:24:06.5090882Z 2024-08-06T21:24:06.5091034Z Args: 2024-08-06T21:24:06.5091449Z actual (Any): Actual input. 2024-08-06T21:24:06.5092009Z expected (Any): Expected input. 2024-08-06T21:24:06.5093209Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-08-06T21:24:06.5094320Z are allowed. Otherwise type equality is required. 2024-08-06T21:24:06.5095494Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-08-06T21:24:06.5096884Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-06T21:24:06.5098233Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-08-06T21:24:06.5099549Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-06T21:24:06.5100693Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-08-06T21:24:06.5101864Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-08-06T21:24:06.5102995Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-08-06T21:24:06.5104062Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-08-06T21:24:06.5105327Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-08-06T21:24:06.5106862Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-08-06T21:24:06.5108012Z :func:`torch.promote_types`) before being compared. 2024-08-06T21:24:06.5109145Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-08-06T21:24:06.5110706Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-08-06T21:24:06.5111831Z compared. 2024-08-06T21:24:06.5112798Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-08-06T21:24:06.5114371Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-08-06T21:24:06.5115960Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-08-06T21:24:06.5117065Z should return the new message. 2024-08-06T21:24:06.5117472Z 2024-08-06T21:24:06.5117609Z Raises: 2024-08-06T21:24:06.5118254Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-08-06T21:24:06.5119190Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-08-06T21:24:06.5120420Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-08-06T21:24:06.5121954Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-08-06T21:24:06.5123043Z different types. 2024-08-06T21:24:06.5124059Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-08-06T21:24:06.5125654Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-08-06T21:24:06.5127058Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-08-06T21:24:06.5128425Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-08-06T21:24:06.5129414Z :attr:`~torch.Tensor.layout`. 2024-08-06T21:24:06.5130218Z AssertionError: If only one of corresponding tensors is quantized. 2024-08-06T21:24:06.5131561Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-08-06T21:24:06.5133078Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-08-06T21:24:06.5134161Z :attr:`~torch.Tensor.device`. 2024-08-06T21:24:06.5135190Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-08-06T21:24:06.5136702Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-08-06T21:24:06.5138314Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-08-06T21:24:06.5139245Z 2024-08-06T21:24:06.5139941Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-08-06T21:24:06.5141115Z ``dtype``'s, the maximum of both tolerances is used. 2024-08-06T21:24:06.5141612Z 2024-08-06T21:24:06.5141831Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5142737Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-08-06T21:24:06.5143312Z +===========================+============+==========+ 2024-08-06T21:24:06.5143871Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-08-06T21:24:06.5144540Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5145185Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-08-06T21:24:06.5145855Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5146530Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:06.5147263Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5147943Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-08-06T21:24:06.5148618Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5149282Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-08-06T21:24:06.5149939Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5150750Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:06.5151437Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5152104Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-08-06T21:24:06.5152799Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5153467Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:06.5154122Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5154780Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:06.5155455Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5156112Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:06.5156786Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5157584Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:06.5158235Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5158907Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:06.5159588Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5160219Z | other | ``0.0`` | ``0.0`` | 2024-08-06T21:24:06.5160865Z +---------------------------+------------+----------+ 2024-08-06T21:24:06.5161302Z 2024-08-06T21:24:06.5161478Z .. note:: 2024-08-06T21:24:06.5161702Z 2024-08-06T21:24:06.5162424Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-08-06T21:24:06.5164021Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-08-06T21:24:06.5165393Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-08-06T21:24:06.5166086Z 2024-08-06T21:24:06.5166270Z >>> import functools 2024-08-06T21:24:06.5167060Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-08-06T21:24:06.5167964Z >>> assert_equal(1e-9, 1e-10) 2024-08-06T21:24:06.5168678Z Traceback (most recent call last): 2024-08-06T21:24:06.5169229Z ... 2024-08-06T21:24:06.5169664Z AssertionError: Scalars are not equal! 2024-08-06T21:24:06.5170258Z 2024-08-06T21:24:06.5170692Z Expected 1e-10 but got 1e-09. 2024-08-06T21:24:06.5171288Z Absolute difference: 9.000000000000001e-10 2024-08-06T21:24:06.5171892Z Relative difference: 9.0 2024-08-06T21:24:06.5172252Z 2024-08-06T21:24:06.5172403Z Examples: 2024-08-06T21:24:06.5172822Z >>> # tensor to tensor comparison 2024-08-06T21:24:06.5173445Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-08-06T21:24:06.5174086Z >>> actual = torch.acos(torch.cos(expected)) 2024-08-06T21:24:06.5174839Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:06.5175326Z 2024-08-06T21:24:06.5175529Z >>> # scalar to scalar comparison 2024-08-06T21:24:06.5176058Z >>> import math 2024-08-06T21:24:06.5176515Z >>> expected = math.sqrt(2.0) 2024-08-06T21:24:06.5177036Z >>> actual = 2.0 / math.sqrt(2.0) 2024-08-06T21:24:06.5177609Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:06.5178028Z 2024-08-06T21:24:06.5178238Z >>> # numpy array to numpy array comparison 2024-08-06T21:24:06.5178827Z >>> import numpy as np 2024-08-06T21:24:06.5179348Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-08-06T21:24:06.5179964Z >>> actual = np.arccos(np.cos(expected)) 2024-08-06T21:24:06.5180620Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:06.5181115Z 2024-08-06T21:24:06.5181350Z >>> # sequence to sequence comparison 2024-08-06T21:24:06.5181966Z >>> import numpy as np 2024-08-06T21:24:06.5182754Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-08-06T21:24:06.5183793Z >>> # length and their elements have to match. 2024-08-06T21:24:06.5184538Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-08-06T21:24:06.5185216Z >>> actual = tuple(expected) 2024-08-06T21:24:06.5185833Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:06.5186318Z 2024-08-06T21:24:06.5186525Z >>> # mapping to mapping comparison 2024-08-06T21:24:06.5187243Z >>> from collections import OrderedDict 2024-08-06T21:24:06.5187743Z >>> import numpy as np 2024-08-06T21:24:06.5188215Z >>> foo = torch.tensor(1.0) 2024-08-06T21:24:06.5188716Z >>> bar = 2.0 2024-08-06T21:24:06.5189115Z >>> baz = np.array(3.0) 2024-08-06T21:24:06.5189892Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-08-06T21:24:06.5191014Z >>> # have to have the same set of keys and their elements have to match. 2024-08-06T21:24:06.5191966Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-08-06T21:24:06.5192801Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-08-06T21:24:06.5193516Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:06.5193996Z 2024-08-06T21:24:06.5194210Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-06T21:24:06.5194818Z >>> actual = expected.clone() 2024-08-06T21:24:06.5195503Z >>> # By default, directly related instances can be compared 2024-08-06T21:24:06.5196427Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-08-06T21:24:06.5197408Z >>> # This check can be made more strict with allow_subclasses=False 2024-08-06T21:24:06.5198187Z >>> torch.testing.assert_close( 2024-08-06T21:24:06.5198964Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-08-06T21:24:06.5199721Z ... ) 2024-08-06T21:24:06.5200146Z Traceback (most recent call last): 2024-08-06T21:24:06.5200716Z ... 2024-08-06T21:24:06.5201457Z TypeError: No comparison pair was able to handle inputs of type 2024-08-06T21:24:06.5202455Z and . 2024-08-06T21:24:06.5203498Z >>> # If the inputs are not directly related, they are never considered close 2024-08-06T21:24:06.5204475Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-08-06T21:24:06.5205228Z Traceback (most recent call last): 2024-08-06T21:24:06.5205789Z ... 2024-08-06T21:24:06.5206509Z TypeError: No comparison pair was able to handle inputs of type 2024-08-06T21:24:06.5207477Z and . 2024-08-06T21:24:06.5208337Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-08-06T21:24:06.5209266Z >>> # their type if check_dtype=False. 2024-08-06T21:24:06.5210002Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-08-06T21:24:06.5210529Z 2024-08-06T21:24:06.5210716Z >>> # NaN != NaN by default. 2024-08-06T21:24:06.5211311Z >>> expected = torch.tensor(float("Nan")) 2024-08-06T21:24:06.5211934Z >>> actual = expected.clone() 2024-08-06T21:24:06.5212578Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:06.5213265Z Traceback (most recent call last): 2024-08-06T21:24:06.5213840Z ... 2024-08-06T21:24:06.5214269Z AssertionError: Scalars are not close! 2024-08-06T21:24:06.5214865Z 2024-08-06T21:24:06.5215323Z Expected nan but got nan. 2024-08-06T21:24:06.5215913Z Absolute difference: nan (up to 1e-05 allowed) 2024-08-06T21:24:06.5216636Z Relative difference: nan (up to 1.3e-06 allowed) 2024-08-06T21:24:06.5217493Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-08-06T21:24:06.5218081Z 2024-08-06T21:24:06.5218291Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-06T21:24:06.5219041Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-08-06T21:24:06.5219729Z >>> # The default error message can be overwritten. 2024-08-06T21:24:06.5220740Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-08-06T21:24:06.5221613Z Traceback (most recent call last): 2024-08-06T21:24:06.5222159Z ... 2024-08-06T21:24:06.5222636Z AssertionError: Argh, the tensors are not close! 2024-08-06T21:24:06.5223518Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-08-06T21:24:06.5224336Z >>> # extra information 2024-08-06T21:24:06.5224854Z >>> torch.testing.assert_close( 2024-08-06T21:24:06.5225627Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-08-06T21:24:06.5226460Z ... ) 2024-08-06T21:24:06.5226979Z Traceback (most recent call last): 2024-08-06T21:24:06.5227554Z ... 2024-08-06T21:24:06.5227953Z AssertionError: Header 2024-08-06T21:24:06.5228434Z 2024-08-06T21:24:06.5228883Z Tensor-likes are not close! 2024-08-06T21:24:06.5229419Z 2024-08-06T21:24:06.5229857Z Mismatched elements: 2 / 3 (66.7%) 2024-08-06T21:24:06.5230690Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-08-06T21:24:06.5231771Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-08-06T21:24:06.5232588Z 2024-08-06T21:24:06.5233001Z Footer 2024-08-06T21:24:06.5233378Z 2024-08-06T21:24:06.5234029Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:06.5234749Z 2024-08-06T21:24:07.7246525Z gathering tests 2024-08-06T21:24:07.7260091Z running 694 test(s) 2024-08-06T21:24:07.7298979Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 972 <- wrt source file 2024-08-06T21:24:07.7307735Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2024-08-06T21:24:07.7308919Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1008 <- wrt source file 2024-08-06T21:24:07.7312273Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2024-08-06T21:24:07.7313649Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1077 <- wrt source file 2024-08-06T21:24:07.7315179Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2024-08-06T21:24:07.7316477Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1126 <- wrt source file 2024-08-06T21:24:07.7318131Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-08-06T21:24:07.7320205Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1163 <- wrt source file 2024-08-06T21:24:07.7321415Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2024-08-06T21:24:07.7322913Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1318 <- wrt source file 2024-08-06T21:24:07.7324805Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-08-06T21:24:07.7326222Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2404 <- wrt source file 2024-08-06T21:24:07.7327424Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2024-08-06T21:24:07.7328786Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2653 <- wrt source file 2024-08-06T21:24:07.7330139Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2024-08-06T21:24:07.7331466Z * 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 2024-08-06T21:24:07.7332805Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2024-08-06T21:24:07.7334123Z * 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 2024-08-06T21:24:07.7335545Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2024-08-06T21:24:07.7336769Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2024-08-06T21:24:07.7337910Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2024-08-06T21:24:07.7339000Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file 2024-08-06T21:24:07.7340080Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2024-08-06T21:24:07.7341200Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file 2024-08-06T21:24:07.7623383Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-08-06T21:24:07.7624868Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2024-08-06T21:24:07.7626173Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-08-06T21:24:07.7627479Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 546 <- wrt source file 2024-08-06T21:24:07.7642821Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-08-06T21:24:07.7644141Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 603 <- wrt source file 2024-08-06T21:24:07.7662307Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-08-06T21:24:07.7663634Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1232 <- wrt source file 2024-08-06T21:24:07.7781844Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-08-06T21:24:07.7785535Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1277 <- wrt source file 2024-08-06T21:24:07.7790694Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-08-06T21:24:07.7792815Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1350 <- wrt source file 2024-08-06T21:24:07.7797727Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2024-08-06T21:24:07.7800187Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1380 <- wrt source file 2024-08-06T21:24:07.7803328Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-08-06T21:24:07.7805597Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0, line 1403 <- wrt source file 2024-08-06T21:24:07.7807887Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0 2024-08-06T21:24:07.7831173Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-08-06T21:24:07.7833508Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-08-06T21:24:07.7839033Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-08-06T21:24:07.7841269Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2024-08-06T21:24:07.7843716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-08-06T21:24:07.7845831Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2024-08-06T21:24:07.7847817Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-08-06T21:24:07.7857110Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2024-08-06T21:24:07.7859043Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 287 <- wrt source file 2024-08-06T21:24:07.7910782Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2024-08-06T21:24:07.7912921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1014 <- wrt source file 2024-08-06T21:24:07.7923253Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-08-06T21:24:07.7925405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1289 <- wrt source file 2024-08-06T21:24:07.7934662Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2024-08-06T21:24:07.7935878Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1373 <- wrt source file 2024-08-06T21:24:07.7941826Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2024-08-06T21:24:07.7943188Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1407 <- wrt source file 2024-08-06T21:24:07.7950628Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2024-08-06T21:24:07.7951767Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1458 <- wrt source file 2024-08-06T21:24:07.7963706Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2024-08-06T21:24:07.7964843Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1499 <- wrt source file 2024-08-06T21:24:07.7979062Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2024-08-06T21:24:07.7980228Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1535 <- wrt source file 2024-08-06T21:24:07.7995283Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2024-08-06T21:24:07.7996438Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1573 <- wrt source file 2024-08-06T21:24:07.8015022Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2024-08-06T21:24:07.8016149Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1746 <- wrt source file 2024-08-06T21:24:07.8046402Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2024-08-06T21:24:07.8047572Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1913 <- wrt source file 2024-08-06T21:24:07.8073032Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2024-08-06T21:24:07.8074848Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2013 <- wrt source file 2024-08-06T21:24:07.8076423Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2024-08-06T21:24:07.8077625Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2113 <- wrt source file 2024-08-06T21:24:07.8078757Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2024-08-06T21:24:07.8079811Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-08-06T21:24:07.8081100Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2024-08-06T21:24:07.8082357Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-08-06T21:24:07.8083406Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2024-08-06T21:24:07.8084445Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file 2024-08-06T21:24:07.8085531Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0 2024-08-06T21:24:07.8086637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 129 <- wrt source file 2024-08-06T21:24:07.8087805Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0 2024-08-06T21:24:07.8089037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 217 <- wrt source file 2024-08-06T21:24:07.8144974Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2024-08-06T21:24:07.8146202Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 268 <- wrt source file 2024-08-06T21:24:07.8149411Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2024-08-06T21:24:07.8150523Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 452 <- wrt source file 2024-08-06T21:24:07.8162695Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2024-08-06T21:24:07.8165331Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 519 <- wrt source file 2024-08-06T21:24:07.8173496Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2024-08-06T21:24:07.8176001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 627 <- wrt source file 2024-08-06T21:24:07.8178392Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2024-08-06T21:24:07.8180599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 943 <- wrt source file 2024-08-06T21:24:07.8264859Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0 2024-08-06T21:24:07.8267279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1032 <- wrt source file 2024-08-06T21:24:07.8384945Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0 2024-08-06T21:24:07.8387236Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-08-06T21:24:07.8393088Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2024-08-06T21:24:07.8395145Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file 2024-08-06T21:24:07.8430375Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2024-08-06T21:24:07.8432619Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1564 <- wrt source file 2024-08-06T21:24:07.8435307Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2024-08-06T21:24:07.8437602Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1699 <- wrt source file 2024-08-06T21:24:07.8439956Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2024-08-06T21:24:07.8442361Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1947 <- wrt source file 2024-08-06T21:24:07.8469206Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-08-06T21:24:07.8471343Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1966 <- wrt source file 2024-08-06T21:24:07.8477436Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2024-08-06T21:24:07.8479655Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-08-06T21:24:07.8481929Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2024-08-06T21:24:07.8484091Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 216 <- wrt source file 2024-08-06T21:24:07.8486353Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2024-08-06T21:24:07.8488591Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 241 <- wrt source file 2024-08-06T21:24:07.8491029Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0 2024-08-06T21:24:07.8493293Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 304 <- wrt source file 2024-08-06T21:24:07.8495587Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2024-08-06T21:24:07.8497781Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 765 <- wrt source file 2024-08-06T21:24:07.8499817Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2024-08-06T21:24:07.8502073Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 18 <- wrt source file 2024-08-06T21:24:07.8504313Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2024-08-06T21:24:07.8506682Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 134 <- wrt source file 2024-08-06T21:24:07.8509021Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-08-06T21:24:07.8511377Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 164 <- wrt source file 2024-08-06T21:24:07.8513677Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0 2024-08-06T21:24:07.8515855Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 146 <- wrt source file 2024-08-06T21:24:07.8518235Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0 2024-08-06T21:24:07.8520659Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1747 <- wrt source file 2024-08-06T21:24:07.8523266Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-08-06T21:24:07.8525662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 94 <- wrt source file 2024-08-06T21:24:07.8527984Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-08-06T21:24:07.8530302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 194 <- wrt source file 2024-08-06T21:24:07.8532631Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-08-06T21:24:07.8534849Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 222 <- wrt source file 2024-08-06T21:24:07.8537223Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-08-06T21:24:07.8539600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 242 <- wrt source file 2024-08-06T21:24:07.8542130Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-08-06T21:24:07.8544381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 214 <- wrt source file 2024-08-06T21:24:07.8545691Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0 2024-08-06T21:24:07.8546897Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 298 <- wrt source file 2024-08-06T21:24:07.8548024Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0 2024-08-06T21:24:07.8549224Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 396 <- wrt source file 2024-08-06T21:24:07.8550502Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2024-08-06T21:24:07.8552208Z * 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 2024-08-06T21:24:07.8553566Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-08-06T21:24:07.8554882Z * 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 2024-08-06T21:24:07.8556193Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2024-08-06T21:24:07.8592339Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 215 <- wrt source file 2024-08-06T21:24:07.8595899Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0 2024-08-06T21:24:07.8598060Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 277 <- wrt source file 2024-08-06T21:24:07.8659011Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0 2024-08-06T21:24:07.8661312Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 361 <- wrt source file 2024-08-06T21:24:07.8682443Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2024-08-06T21:24:07.8684958Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 454 <- wrt source file 2024-08-06T21:24:07.8690801Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-08-06T21:24:07.8693214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 540 <- wrt source file 2024-08-06T21:24:07.8748540Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-08-06T21:24:07.8751103Z * 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 1158 <- wrt source file 2024-08-06T21:24:07.8753984Z * 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 2024-08-06T21:24:07.8757059Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 212 <- wrt source file 2024-08-06T21:24:07.8759491Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2024-08-06T21:24:07.8762097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.__getitem__:0, line 433 <- wrt source file 2024-08-06T21:24:07.8764715Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.__getitem__:0 2024-08-06T21:24:07.8767302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 607 <- wrt source file 2024-08-06T21:24:07.8770064Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2024-08-06T21:24:07.8772601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 669 <- wrt source file 2024-08-06T21:24:07.8775083Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2024-08-06T21:24:07.8777630Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2226 <- wrt source file 2024-08-06T21:24:07.8780483Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2024-08-06T21:24:07.8783093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0, line 2318 <- wrt source file 2024-08-06T21:24:07.8785725Z * SKIPPED: 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* SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2024-08-06T21:24:07.8819610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0, line 3270 <- wrt source file 2024-08-06T21:24:07.8822103Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0 2024-08-06T21:24:07.8824665Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3352 <- wrt source file 2024-08-06T21:24:07.8827452Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 2024-08-06T21:24:07.8830104Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 3477 <- wrt source file 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SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0 2024-08-06T21:24:07.8882031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 42 <- wrt source file 2024-08-06T21:24:07.8885229Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0 2024-08-06T21:24:07.8888364Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0, line 113 <- wrt source file 2024-08-06T21:24:07.8891538Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0 2024-08-06T21:24:07.8894398Z * DOCTEST : 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 284 <- wrt source file 2024-08-06T21:24:07.9387288Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0 2024-08-06T21:24:07.9390846Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 411 <- wrt source file 2024-08-06T21:24:07.9394124Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0 2024-08-06T21:24:07.9397244Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 16 <- wrt source file 2024-08-06T21:24:07.9400348Z * SUCCESS: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 86 <- wrt source file 2024-08-06T21:24:07.9418426Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2024-08-06T21:24:07.9421381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 116 <- wrt source file 2024-08-06T21:24:07.9424436Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0 2024-08-06T21:24:07.9427517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 46 <- wrt source file 2024-08-06T21:24:07.9430646Z * SKIPPED: 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file 2024-08-06T21:24:07.9447154Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2024-08-06T21:24:07.9449443Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0, line 76 <- wrt source file 2024-08-06T21:24:07.9451814Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0 2024-08-06T21:24:07.9454785Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 53 <- wrt source file 2024-08-06T21:24:07.9458057Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0 2024-08-06T21:24:07.9460905Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2024-08-06T21:24:07.9463457Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2024-08-06T21:24:07.9465935Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file 2024-08-06T21:24:07.9468530Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0 2024-08-06T21:24:07.9471095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file 2024-08-06T21:24:07.9473753Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0 2024-08-06T21:24:07.9476253Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file 2024-08-06T21:24:07.9478711Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0 2024-08-06T21:24:07.9481313Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 467 <- wrt source file 2024-08-06T21:24:07.9483931Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2024-08-06T21:24:07.9486505Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 586 <- wrt source file 2024-08-06T21:24:08.0347786Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2024-08-06T21:24:08.0370388Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2181 <- wrt source file 2024-08-06T21:24:08.0380867Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0 2024-08-06T21:24:08.0383530Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file 2024-08-06T21:24:08.0389450Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0 2024-08-06T21:24:08.0391614Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file 2024-08-06T21:24:08.0400279Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0 2024-08-06T21:24:08.0402479Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0, line 3049 <- wrt source file 2024-08-06T21:24:08.0417726Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0 2024-08-06T21:24:08.0419859Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3126 <- wrt source file 2024-08-06T21:24:08.0424672Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0 2024-08-06T21:24:08.0426938Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3451 <- wrt source file 2024-08-06T21:24:08.0434284Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0 2024-08-06T21:24:08.0436956Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3523 <- wrt source file 2024-08-06T21:24:08.0440894Z * SUCCESS: 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2024-08-06T21:24:08.0488212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-08-06T21:24:08.0491025Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-08-06T21:24:08.0493078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-08-06T21:24:08.0498852Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2024-08-06T21:24:08.0500907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-08-06T21:24:08.0504047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-08-06T21:24:08.0506217Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-08-06T21:24:08.0538743Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2024-08-06T21:24:08.0541394Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-08-06T21:24:08.0558980Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-08-06T21:24:08.0561379Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-08-06T21:24:08.0563681Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0 2024-08-06T21:24:08.0565690Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-08-06T21:24:08.0567688Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2024-08-06T21:24:08.0569870Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-08-06T21:24:08.0571833Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2024-08-06T21:24:08.0573820Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-08-06T21:24:08.0576916Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2024-08-06T21:24:08.0580056Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-08-06T21:24:08.0582094Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2024-08-06T21:24:08.0584048Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-08-06T21:24:08.0586009Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2024-08-06T21:24:08.0587987Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-08-06T21:24:08.0589920Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2024-08-06T21:24:08.0591824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-08-06T21:24:08.0593749Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2024-08-06T21:24:08.0595671Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-08-06T21:24:08.0597795Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2024-08-06T21:24:08.0599808Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-08-06T21:24:08.0601927Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-08-06T21:24:08.0603971Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file 2024-08-06T21:24:08.0606050Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2024-08-06T21:24:08.0608126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file 2024-08-06T21:24:08.0610334Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-08-06T21:24:08.0612420Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file 2024-08-06T21:24:08.0614544Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-08-06T21:24:08.0616586Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file 2024-08-06T21:24:08.0618643Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2024-08-06T21:24:08.0620618Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file 2024-08-06T21:24:08.0622606Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2024-08-06T21:24:08.0624786Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 81 <- wrt source file 2024-08-06T21:24:08.0627161Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-08-06T21:24:08.0629422Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file 2024-08-06T21:24:08.0631639Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-08-06T21:24:08.0633886Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-08-06T21:24:08.0636211Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-08-06T21:24:08.0638427Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-08-06T21:24:08.0640626Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-08-06T21:24:08.0643001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-08-06T21:24:08.0645280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-08-06T21:24:08.0647523Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-08-06T21:24:08.0649817Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-08-06T21:24:08.0652065Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-08-06T21:24:08.0654437Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-08-06T21:24:08.0656688Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-08-06T21:24:08.0658969Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-08-06T21:24:08.0661260Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-08-06T21:24:08.0663640Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-08-06T21:24:08.0665995Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-08-06T21:24:08.0668305Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-08-06T21:24:08.0670808Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-08-06T21:24:08.0673085Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-08-06T21:24:08.0675264Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-08-06T21:24:08.0677479Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2024-08-06T21:24:08.0679730Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-08-06T21:24:08.0682616Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-08-06T21:24:08.0684828Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-08-06T21:24:08.0687047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2024-08-06T21:24:08.0689229Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-08-06T21:24:08.0691467Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2024-08-06T21:24:08.0693638Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-08-06T21:24:08.0695845Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2024-08-06T21:24:08.0698025Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-08-06T21:24:08.0700227Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2024-08-06T21:24:08.0702391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-08-06T21:24:08.0704630Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2024-08-06T21:24:08.0706961Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-08-06T21:24:08.0709423Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-08-06T21:24:08.0711728Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-08-06T21:24:08.0714063Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-08-06T21:24:08.0716370Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-08-06T21:24:08.0718727Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-08-06T21:24:08.0720997Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-08-06T21:24:08.0723347Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-08-06T21:24:08.0725645Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-08-06T21:24:08.0727991Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-08-06T21:24:08.0730391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-08-06T21:24:08.0732935Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-08-06T21:24:08.0735286Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-08-06T21:24:08.0737536Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-08-06T21:24:08.0739867Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-08-06T21:24:08.0742191Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-08-06T21:24:08.0744611Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-08-06T21:24:08.0747287Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-08-06T21:24:08.0749943Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-08-06T21:24:08.0752581Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-08-06T21:24:08.0754972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-08-06T21:24:08.0757247Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-08-06T21:24:08.0759474Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-08-06T21:24:08.0761798Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-08-06T21:24:08.0764107Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-08-06T21:24:08.0766446Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-08-06T21:24:08.0768861Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-08-06T21:24:08.0771201Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-08-06T21:24:08.0773502Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-08-06T21:24:08.0999684Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-08-06T21:24:08.1002276Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-08-06T21:24:08.3574842Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-08-06T21:24:08.3746353Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-08-06T21:24:08.3765592Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-08-06T21:24:08.3767989Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 86 <- wrt source file 2024-08-06T21:24:08.3770311Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2024-08-06T21:24:08.3772612Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 292 <- wrt source file 2024-08-06T21:24:08.3774932Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-08-06T21:24:08.3777520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 466 <- wrt source file 2024-08-06T21:24:08.3779843Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-08-06T21:24:08.3782153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 598 <- wrt source file 2024-08-06T21:24:08.3784530Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-08-06T21:24:08.3786956Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 750 <- wrt source file 2024-08-06T21:24:08.3789368Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-08-06T21:24:08.3791764Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-08-06T21:24:08.3794197Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-08-06T21:24:08.3796581Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-08-06T21:24:08.3798998Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-08-06T21:24:08.3801262Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-08-06T21:24:08.3803457Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-08-06T21:24:08.3805744Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-08-06T21:24:08.3808009Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-08-06T21:24:08.3810220Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-08-06T21:24:08.3826974Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-08-06T21:24:08.3829204Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-08-06T21:24:08.3904369Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-08-06T21:24:08.3907205Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-08-06T21:24:08.3909526Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-08-06T21:24:08.3911899Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-08-06T21:24:08.3986778Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-08-06T21:24:08.3989467Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-08-06T21:24:08.3993002Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-08-06T21:24:08.3995370Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-08-06T21:24:08.3999491Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2024-08-06T21:24:08.4001557Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-08-06T21:24:08.4014453Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-08-06T21:24:08.4016744Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-08-06T21:24:08.4028367Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-08-06T21:24:08.4030815Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-08-06T21:24:08.4220145Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-08-06T21:24:08.4222885Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-08-06T21:24:08.6779139Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-08-06T21:24:08.6947504Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-08-06T21:24:08.6951065Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-08-06T21:24:08.6953282Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-08-06T21:24:08.6958328Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2024-08-06T21:24:08.6960498Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-08-06T21:24:08.6967394Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2024-08-06T21:24:08.6969547Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-08-06T21:24:08.6987689Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-08-06T21:24:08.6990230Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-08-06T21:24:08.6996051Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-08-06T21:24:08.6998155Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-08-06T21:24:08.7022430Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-08-06T21:24:08.7025170Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-08-06T21:24:08.7030183Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-08-06T21:24:08.7032516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-08-06T21:24:08.7045619Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-08-06T21:24:08.7047860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 517 <- wrt source file 2024-08-06T21:24:08.7054531Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-08-06T21:24:08.7057007Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 595 <- wrt source file 2024-08-06T21:24:08.7060901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-08-06T21:24:08.7063018Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 677 <- wrt source file 2024-08-06T21:24:08.7069112Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-08-06T21:24:08.7071555Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 748 <- wrt source file 2024-08-06T21:24:08.7080535Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-08-06T21:24:08.7082927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 941 <- wrt source file 2024-08-06T21:24:08.7088656Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-08-06T21:24:08.7091069Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1261 <- wrt source file 2024-08-06T21:24:08.7097169Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-08-06T21:24:08.7099549Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1401 <- wrt source file 2024-08-06T21:24:08.7107096Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-08-06T21:24:08.7109526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1466 <- wrt source file 2024-08-06T21:24:08.7114721Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-08-06T21:24:08.7117427Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1545 <- wrt source file 2024-08-06T21:24:08.7122805Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-08-06T21:24:08.7125150Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1645 <- wrt source file 2024-08-06T21:24:08.7134632Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-08-06T21:24:08.7136871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1886 <- wrt source file 2024-08-06T21:24:08.7167001Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-08-06T21:24:08.7169350Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 545 <- wrt source file 2024-08-06T21:24:08.7171901Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-08-06T21:24:08.7174526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1005 <- wrt source file 2024-08-06T21:24:08.7181554Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-08-06T21:24:08.7183805Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1259 <- wrt source file 2024-08-06T21:24:08.7192287Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2024-08-06T21:24:08.7194587Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2167 <- wrt source file 2024-08-06T21:24:08.7196999Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-08-06T21:24:08.7199412Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2609 <- wrt source file 2024-08-06T21:24:08.7201849Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-08-06T21:24:08.7204296Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2637 <- wrt source file 2024-08-06T21:24:08.7206829Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-08-06T21:24:08.7209225Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2664 <- wrt source file 2024-08-06T21:24:08.7211724Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-08-06T21:24:08.7214105Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2691 <- wrt source file 2024-08-06T21:24:08.7216569Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-08-06T21:24:08.7219021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2722 <- wrt source file 2024-08-06T21:24:08.7221515Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-08-06T21:24:08.7223864Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2746 <- wrt source file 2024-08-06T21:24:08.7226124Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-08-06T21:24:08.7229086Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2784 <- wrt source file 2024-08-06T21:24:08.7232168Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-08-06T21:24:08.7235326Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-08-06T21:24:08.7245846Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-08-06T21:24:08.7249295Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-08-06T21:24:08.7255365Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-08-06T21:24:08.7258124Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-08-06T21:24:08.7263356Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-08-06T21:24:08.7266082Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-08-06T21:24:08.7268904Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-08-06T21:24:08.7271208Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-08-06T21:24:08.7276348Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-08-06T21:24:08.7278670Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-08-06T21:24:08.7296685Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-08-06T21:24:08.7298999Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-08-06T21:24:09.3791153Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-08-06T21:24:09.4099105Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-08-06T21:24:09.4107468Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-08-06T21:24:09.4109814Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-08-06T21:24:09.4114233Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-08-06T21:24:09.4116569Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-08-06T21:24:09.4140749Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-08-06T21:24:09.4143610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-08-06T21:24:09.4147347Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-08-06T21:24:09.4149737Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-08-06T21:24:09.4153130Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-08-06T21:24:09.4155493Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-08-06T21:24:09.4157896Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-08-06T21:24:09.4160271Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-08-06T21:24:09.4164527Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-08-06T21:24:09.4167130Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-08-06T21:24:09.4170959Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-08-06T21:24:09.4173330Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-08-06T21:24:09.9525582Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-08-06T21:24:09.9835009Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-08-06T21:24:09.9845204Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-08-06T21:24:09.9847356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-08-06T21:24:09.9853463Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-08-06T21:24:09.9855641Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-08-06T21:24:09.9879111Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-08-06T21:24:09.9881426Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-08-06T21:24:09.9885443Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-08-06T21:24:09.9887903Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-08-06T21:24:09.9890968Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-08-06T21:24:09.9893309Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-08-06T21:24:09.9897594Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-08-06T21:24:09.9899832Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-08-06T21:24:09.9951426Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-08-06T21:24:09.9953699Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-08-06T21:24:10.2242022Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-08-06T21:24:10.2301969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-08-06T21:24:10.2313489Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-08-06T21:24:10.2315773Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-08-06T21:24:10.3060336Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-08-06T21:24:10.3062981Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-08-06T21:24:10.3070000Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-08-06T21:24:10.3072238Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-08-06T21:24:10.3111440Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-08-06T21:24:10.3161928Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-08-06T21:24:10.4872646Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-08-06T21:24:10.4931712Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-08-06T21:24:10.4984443Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-08-06T21:24:10.4987014Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-08-06T21:24:10.5859204Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-08-06T21:24:10.5861773Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-08-06T21:24:10.5868810Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-08-06T21:24:10.5871697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-08-06T21:24:10.5925643Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-08-06T21:24:10.5928536Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-08-06T21:24:10.8192282Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-08-06T21:24:10.8321780Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-08-06T21:24:10.8329143Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-08-06T21:24:10.8331973Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-08-06T21:24:10.8341044Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-08-06T21:24:10.8344202Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-08-06T21:24:10.8375020Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-08-06T21:24:10.8377486Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-08-06T21:24:10.8380839Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-08-06T21:24:10.8384072Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-08-06T21:24:10.8392091Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-08-06T21:24:10.8395011Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-08-06T21:24:10.8418154Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-08-06T21:24:10.8420325Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 589 <- wrt source file 2024-08-06T21:24:10.8433525Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-08-06T21:24:10.8436218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 946 <- wrt source file 2024-08-06T21:24:10.8750198Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-08-06T21:24:10.8752270Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1284 <- wrt source file 2024-08-06T21:24:10.8767422Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-08-06T21:24:10.8769505Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1535 <- wrt source file 2024-08-06T21:24:10.8780446Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-08-06T21:24:10.8782979Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1657 <- wrt source file 2024-08-06T21:24:10.8793366Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-08-06T21:24:10.8795511Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1771 <- wrt source file 2024-08-06T21:24:10.8806124Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-08-06T21:24:10.8808279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-08-06T21:24:10.8819517Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-08-06T21:24:10.8822083Z * 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 2024-08-06T21:24:10.8825132Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-08-06T21:24:10.8827740Z * 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 2024-08-06T21:24:10.8832330Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-08-06T21:24:10.8834836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2024-08-06T21:24:11.7321046Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-08-06T21:24:11.7336815Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 258 <- wrt source file 2024-08-06T21:24:11.7339842Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-08-06T21:24:11.7342535Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 323 <- wrt source file 2024-08-06T21:24:11.8483625Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-08-06T21:24:11.8523657Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 536 <- wrt source file 2024-08-06T21:24:12.0966145Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-08-06T21:24:12.0973538Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 657 <- wrt source file 2024-08-06T21:24:12.1274368Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-08-06T21:24:12.1277214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 961 <- wrt source file 2024-08-06T21:24:12.1837678Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-08-06T21:24:12.1840252Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-08-06T21:24:12.1863270Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-08-06T21:24:12.1865972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-08-06T21:24:12.1876383Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-08-06T21:24:12.1878966Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-08-06T21:24:12.1884070Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-08-06T21:24:12.1886644Z * 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 2024-08-06T21:24:12.1889330Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-08-06T21:24:12.1891915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-08-06T21:24:12.1894626Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-08-06T21:24:12.1897359Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1415 <- wrt source file 2024-08-06T21:24:12.1900231Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-08-06T21:24:12.1903184Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1978 <- wrt source file 2024-08-06T21:24:12.1906419Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-08-06T21:24:12.1909541Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1988 <- wrt source file 2024-08-06T21:24:12.1912642Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-08-06T21:24:12.1915752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2023 <- wrt source file 2024-08-06T21:24:12.1918982Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-08-06T21:24:12.1921808Z * 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 2024-08-06T21:24:12.1924414Z * 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 2024-08-06T21:24:12.1926714Z * 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 2024-08-06T21:24:12.1928832Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2024-08-06T21:24:12.1931102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-08-06T21:24:12.1933570Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-08-06T21:24:12.1936038Z * 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 2024-08-06T21:24:12.1938510Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-08-06T21:24:12.1940959Z * 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 2024-08-06T21:24:12.1943651Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-08-06T21:24:12.1946191Z * 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 2024-08-06T21:24:12.1948931Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-08-06T21:24:12.1951244Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 846 <- wrt source file 2024-08-06T21:24:12.1953384Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2024-08-06T21:24:12.1955618Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 882 <- wrt source file 2024-08-06T21:24:12.1957995Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-08-06T21:24:12.1960268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 925 <- wrt source file 2024-08-06T21:24:12.1962562Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-08-06T21:24:12.1964934Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1192 <- wrt source file 2024-08-06T21:24:12.1967073Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2024-08-06T21:24:12.1969166Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1220 <- wrt source file 2024-08-06T21:24:12.1971323Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-08-06T21:24:12.1973516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 357 <- wrt source file 2024-08-06T21:24:12.1975833Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-08-06T21:24:12.1978044Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 434 <- wrt source file 2024-08-06T21:24:12.1980210Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-08-06T21:24:12.1982347Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 488 <- wrt source file 2024-08-06T21:24:12.1991402Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-08-06T21:24:12.1993661Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 544 <- wrt source file 2024-08-06T21:24:12.1998513Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-08-06T21:24:12.2000700Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 572 <- wrt source file 2024-08-06T21:24:12.2014196Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-08-06T21:24:12.2016494Z * 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 2024-08-06T21:24:12.2021732Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-08-06T21:24:12.2024190Z * 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 2024-08-06T21:24:12.2029393Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-08-06T21:24:12.2031847Z * 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 2024-08-06T21:24:12.2034250Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-08-06T21:24:12.2036542Z * 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 2024-08-06T21:24:12.2041460Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-08-06T21:24:12.2043954Z * 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 2024-08-06T21:24:12.2047981Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-08-06T21:24:12.2050688Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 317 <- wrt source file 2024-08-06T21:24:12.2053302Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-08-06T21:24:12.2056150Z * 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 2024-08-06T21:24:12.2130457Z * 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 2024-08-06T21:24:12.2134307Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 308 <- wrt source file 2024-08-06T21:24:12.2137114Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-08-06T21:24:12.2139398Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 410 <- wrt source file 2024-08-06T21:24:12.2141477Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-08-06T21:24:12.2142938Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 510 <- wrt source file 2024-08-06T21:24:12.2144153Z * SKIPPED: 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2024-08-06T21:24:12.2152915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 842 <- wrt source file 2024-08-06T21:24:12.2154264Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2024-08-06T21:24:12.2155513Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 979 <- wrt source file 2024-08-06T21:24:12.2156777Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2024-08-06T21:24:12.2158051Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1135 <- wrt source file 2024-08-06T21:24:12.2159355Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2024-08-06T21:24:12.2160656Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1278 <- wrt source file 2024-08-06T21:24:12.2161980Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2024-08-06T21:24:12.2163389Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1510 <- wrt source file 2024-08-06T21:24:12.2164810Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2024-08-06T21:24:12.2167151Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1780 <- wrt source file 2024-08-06T21:24:12.2169920Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2024-08-06T21:24:12.2172719Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1796 <- wrt source file 2024-08-06T21:24:12.2175427Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2024-08-06T21:24:12.2177981Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1941 <- wrt source file 2024-08-06T21:24:12.2180124Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2024-08-06T21:24:12.2181515Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0, line 319 <- wrt source file 2024-08-06T21:24:12.2182719Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0 2024-08-06T21:24:12.2183915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 257 <- wrt source file 2024-08-06T21:24:12.2185175Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2024-08-06T21:24:12.2186799Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 299 <- wrt source file 2024-08-06T21:24:12.2188070Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-08-06T21:24:12.2189275Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 329 <- wrt source file 2024-08-06T21:24:12.2194879Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-08-06T21:24:12.2197070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 364 <- wrt source file 2024-08-06T21:24:12.2200186Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-08-06T21:24:12.2202391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 399 <- wrt source file 2024-08-06T21:24:12.2206242Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-08-06T21:24:12.2208405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 436 <- wrt source file 2024-08-06T21:24:12.2213386Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-08-06T21:24:12.2215617Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 812 <- wrt source file 2024-08-06T21:24:12.2222844Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-08-06T21:24:12.2225194Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 933 <- wrt source file 2024-08-06T21:24:12.2228084Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2024-08-06T21:24:12.2230492Z * 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 2024-08-06T21:24:12.2233252Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-08-06T21:24:12.2236086Z * 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 2024-08-06T21:24:12.2239089Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-08-06T21:24:12.2241835Z * 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 2024-08-06T21:24:12.2244588Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2024-08-06T21:24:12.2247090Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 546 <- wrt source file 2024-08-06T21:24:12.2249555Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2024-08-06T21:24:12.2251994Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 748 <- wrt source file 2024-08-06T21:24:12.2254629Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2024-08-06T21:24:12.2256914Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file 2024-08-06T21:24:12.2259043Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2024-08-06T21:24:12.2261302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file 2024-08-06T21:24:12.2263699Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2024-08-06T21:24:12.2266013Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file 2024-08-06T21:24:12.2268456Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2024-08-06T21:24:12.2284885Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 303 <- wrt source file 2024-08-06T21:24:12.2287231Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-08-06T21:24:12.2289584Z * 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 2024-08-06T21:24:12.2292081Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-08-06T21:24:12.2294448Z * 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 2024-08-06T21:24:12.2296784Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-08-06T21:24:12.2299149Z * 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 2024-08-06T21:24:12.2301646Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2024-08-06T21:24:12.2304154Z * 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 2024-08-06T21:24:12.2306776Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2024-08-06T21:24:12.2309316Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 264 <- wrt source file 2024-08-06T21:24:12.2311970Z * SKIPPED: 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2024-08-06T21:24:12.2329166Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2024-08-06T21:24:12.2332082Z * 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 2024-08-06T21:24:12.2335017Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-08-06T21:24:12.2337856Z * 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 2024-08-06T21:24:12.2340730Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2024-08-06T21:24:12.2343769Z * 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 2024-08-06T21:24:12.2346574Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2024-08-06T21:24:12.2349521Z * 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 2024-08-06T21:24:12.2352549Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2024-08-06T21:24:12.2355514Z * 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 2024-08-06T21:24:12.2358529Z * SKIPPED: 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line 34 <- wrt source file 2024-08-06T21:24:12.2376222Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2024-08-06T21:24:12.2379099Z * 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 2024-08-06T21:24:12.2381989Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-08-06T21:24:12.2384874Z * 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 2024-08-06T21:24:12.2387915Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2024-08-06T21:24:12.2390786Z * 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 2024-08-06T21:24:12.2393684Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2024-08-06T21:24:12.2396514Z * 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 2024-08-06T21:24:12.2399420Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2024-08-06T21:24:12.2402410Z * 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 2024-08-06T21:24:12.2405664Z * SKIPPED: 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line 33 <- wrt source file 2024-08-06T21:24:12.2423245Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2024-08-06T21:24:12.2426165Z * 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 2024-08-06T21:24:12.2429144Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-08-06T21:24:12.2431975Z * 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 2024-08-06T21:24:12.2434844Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-08-06T21:24:12.2437739Z * 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 2024-08-06T21:24:12.2440607Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-08-06T21:24:12.2443603Z * 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 2024-08-06T21:24:12.2446551Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-08-06T21:24:12.2449511Z * 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 2024-08-06T21:24:12.2452318Z * SKIPPED: 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* SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-08-06T21:24:12.6155703Z * 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 2024-08-06T21:24:12.6158032Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-08-06T21:24:12.6160096Z * 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 2024-08-06T21:24:12.6162353Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-08-06T21:24:12.6164498Z * 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 2024-08-06T21:24:12.6166016Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-08-06T21:24:12.6167463Z * 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 2024-08-06T21:24:12.6168948Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-08-06T21:24:12.6170404Z * 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 2024-08-06T21:24:12.6171888Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-08-06T21:24:12.6173471Z * 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 2024-08-06T21:24:12.6174986Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-08-06T21:24:12.6176489Z * 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 2024-08-06T21:24:12.6178031Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-08-06T21:24:12.6179565Z * 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 2024-08-06T21:24:12.6181042Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-08-06T21:24:12.6182490Z * 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 2024-08-06T21:24:12.6183966Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-08-06T21:24:12.6185385Z * 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 2024-08-06T21:24:12.6186936Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-08-06T21:24:12.6188411Z * 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 2024-08-06T21:24:12.6190034Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-08-06T21:24:12.6191501Z * 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 2024-08-06T21:24:12.6192992Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-08-06T21:24:12.6194601Z * 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 2024-08-06T21:24:12.6196322Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-08-06T21:24:12.6197990Z * 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 2024-08-06T21:24:12.6199682Z * SKIPPED: 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wrt source file 2024-08-06T21:24:12.6208739Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2024-08-06T21:24:12.6210075Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 796 <- wrt source file 2024-08-06T21:24:12.6895541Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2024-08-06T21:24:12.6897037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0, line 324 <- wrt source file 2024-08-06T21:24:12.6898320Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0 2024-08-06T21:24:12.6899734Z * 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 2024-08-06T21:24:12.6901298Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2024-08-06T21:24:12.6902729Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 271 <- wrt source file 2024-08-06T21:24:12.6933646Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2024-08-06T21:24:12.6934908Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 510 <- wrt source file 2024-08-06T21:24:12.7001721Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2024-08-06T21:24:12.7002996Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1064 <- wrt source file 2024-08-06T21:24:12.7874005Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-08-06T21:24:12.7875367Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1219 <- wrt source file 2024-08-06T21:24:12.7933494Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-08-06T21:24:12.7934789Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1384 <- wrt source file 2024-08-06T21:24:12.7953875Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-08-06T21:24:12.7956768Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1548 <- wrt source file 2024-08-06T21:24:12.7959356Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2024-08-06T21:24:12.7961937Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1748 <- wrt source file 2024-08-06T21:24:12.8273581Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2024-08-06T21:24:12.8276073Z * 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 2024-08-06T21:24:12.8279003Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0 2024-08-06T21:24:12.8281404Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2024-08-06T21:24:12.8283870Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2024-08-06T21:24:12.8286592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 110 <- wrt source file 2024-08-06T21:24:12.8289939Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0 2024-08-06T21:24:12.8292746Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 67 <- wrt source file 2024-08-06T21:24:12.8295189Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2024-08-06T21:24:12.8297389Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 81 <- wrt source file 2024-08-06T21:24:12.8299303Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-08-06T21:24:12.8301103Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 93 <- wrt source file 2024-08-06T21:24:12.8303049Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-08-06T21:24:12.8305126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0, line 83 <- wrt source file 2024-08-06T21:24:12.8572026Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-08-06T21:24:12.8574509Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 212 <- wrt source file 2024-08-06T21:24:12.8636306Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2024-08-06T21:24:12.8638922Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 281 <- wrt source file 2024-08-06T21:24:12.8641634Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2024-08-06T21:24:12.8644419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 400 <- wrt source file 2024-08-06T21:24:12.8698235Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2024-08-06T21:24:12.8699684Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 523 <- wrt source file 2024-08-06T21:24:12.8817506Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2024-08-06T21:24:12.8818924Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 697 <- wrt source file 2024-08-06T21:24:12.8937104Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2024-08-06T21:24:12.8938561Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 184 <- wrt source file 2024-08-06T21:24:12.8940069Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-08-06T21:24:12.8941457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 159 <- wrt source file 2024-08-06T21:24:12.8987231Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-08-06T21:24:12.8988762Z * 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 2024-08-06T21:24:12.8993173Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0 2024-08-06T21:24:12.8994810Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 409 <- wrt source file 2024-08-06T21:24:12.8996021Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2024-08-06T21:24:12.8997241Z * 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 2024-08-06T21:24:12.9052772Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-08-06T21:24:12.9054164Z * 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 2024-08-06T21:24:12.9055785Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-08-06T21:24:12.9057191Z * 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 2024-08-06T21:24:12.9100734Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-08-06T21:24:12.9102104Z * 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 2024-08-06T21:24:12.9103452Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-08-06T21:24:12.9104760Z * 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 2024-08-06T21:24:12.9118975Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-08-06T21:24:12.9120333Z * 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 2024-08-06T21:24:12.9122952Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-08-06T21:24:12.9124321Z * 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 2024-08-06T21:24:12.9127078Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-08-06T21:24:12.9128686Z * 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 2024-08-06T21:24:12.9130045Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-08-06T21:24:12.9131293Z * 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 2024-08-06T21:24:12.9133107Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-08-06T21:24:12.9134704Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 85 <- wrt source file 2024-08-06T21:24:12.9136259Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-08-06T21:24:12.9137958Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-08-06T21:24:12.9139635Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-08-06T21:24:12.9141010Z * 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 2024-08-06T21:24:12.9142707Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-08-06T21:24:12.9144304Z * 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 2024-08-06T21:24:12.9145999Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-08-06T21:24:12.9147802Z * 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 2024-08-06T21:24:12.9149378Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-08-06T21:24:12.9150958Z * 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 2024-08-06T21:24:12.9152598Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-08-06T21:24:12.9154175Z * 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 2024-08-06T21:24:12.9155737Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-08-06T21:24:12.9157182Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 25 <- wrt source file 2024-08-06T21:24:12.9159387Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-08-06T21:24:12.9161249Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 318 <- wrt source file 2024-08-06T21:24:12.9188446Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-08-06T21:24:12.9190482Z * 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 2024-08-06T21:24:12.9192339Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-08-06T21:24:12.9193684Z * 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 2024-08-06T21:24:12.9194998Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-08-06T21:24:12.9196275Z * 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 2024-08-06T21:24:12.9197791Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-08-06T21:24:12.9199266Z * 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 2024-08-06T21:24:12.9200651Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-08-06T21:24:12.9202408Z * 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 2024-08-06T21:24:12.9204498Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-08-06T21:24:12.9206480Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 42 <- wrt source file 2024-08-06T21:24:12.9208496Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-08-06T21:24:12.9210397Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 123 <- wrt source file 2024-08-06T21:24:12.9212897Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-08-06T21:24:12.9214310Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 207 <- wrt source file 2024-08-06T21:24:12.9215909Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-08-06T21:24:12.9218366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 293 <- wrt source file 2024-08-06T21:24:12.9220850Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-08-06T21:24:12.9222581Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 375 <- wrt source file 2024-08-06T21:24:12.9224134Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-08-06T21:24:12.9225923Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 457 <- wrt source file 2024-08-06T21:24:12.9228822Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-08-06T21:24:12.9231522Z * 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 2024-08-06T21:24:12.9234205Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-08-06T21:24:12.9236835Z * 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 2024-08-06T21:24:12.9239372Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-08-06T21:24:12.9241848Z * 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 2024-08-06T21:24:12.9244532Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-08-06T21:24:12.9247072Z * 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 2024-08-06T21:24:12.9249779Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-08-06T21:24:12.9252381Z * 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 2024-08-06T21:24:12.9255039Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-08-06T21:24:12.9257612Z * 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 2024-08-06T21:24:12.9260187Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-08-06T21:24:12.9262696Z * 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 2024-08-06T21:24:12.9265260Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-08-06T21:24:12.9267872Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 506 <- wrt source file 2024-08-06T21:24:12.9270288Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-08-06T21:24:12.9272657Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file 2024-08-06T21:24:12.9275065Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-08-06T21:24:12.9277545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 892 <- wrt source file 2024-08-06T21:24:12.9280161Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-08-06T21:24:12.9282772Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1014 <- wrt source file 2024-08-06T21:24:12.9285403Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-08-06T21:24:12.9287988Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1140 <- wrt source file 2024-08-06T21:24:12.9290603Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-08-06T21:24:12.9293206Z * 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 2024-08-06T21:24:12.9295943Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-08-06T21:24:12.9298619Z * 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 2024-08-06T21:24:12.9301364Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-08-06T21:24:12.9304162Z * 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 2024-08-06T21:24:12.9307182Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-08-06T21:24:12.9310081Z * 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 2024-08-06T21:24:12.9312924Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-08-06T21:24:12.9315533Z * 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 2024-08-06T21:24:12.9318033Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-08-06T21:24:12.9321029Z * 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 2024-08-06T21:24:12.9324560Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-08-06T21:24:12.9328128Z * 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 2024-08-06T21:24:12.9420614Z * 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 2024-08-06T21:24:12.9424089Z * 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 2024-08-06T21:24:12.9427555Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-08-06T21:24:12.9430254Z * 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 2024-08-06T21:24:12.9432913Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-08-06T21:24:12.9448810Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 49 <- wrt source file 2024-08-06T21:24:12.9451820Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-08-06T21:24:12.9454359Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 178 <- wrt source file 2024-08-06T21:24:12.9456865Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-08-06T21:24:12.9459696Z * 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 2024-08-06T21:24:12.9462601Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-08-06T21:24:12.9465416Z * 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 2024-08-06T21:24:12.9468367Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-08-06T21:24:12.9471156Z * 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 2024-08-06T21:24:12.9474241Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-08-06T21:24:12.9477040Z * 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 2024-08-06T21:24:12.9479990Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-08-06T21:24:12.9482532Z * 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 2024-08-06T21:24:12.9484967Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2024-08-06T21:24:12.9487427Z * 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 2024-08-06T21:24:12.9490410Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2024-08-06T21:24:12.9492921Z * 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 2024-08-06T21:24:12.9495355Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2024-08-06T21:24:12.9497824Z * 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 2024-08-06T21:24:12.9500313Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2024-08-06T21:24:12.9502889Z * 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 2024-08-06T21:24:12.9505463Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2024-08-06T21:24:12.9507953Z * 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 2024-08-06T21:24:12.9510423Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2024-08-06T21:24:12.9513075Z * 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 2024-08-06T21:24:12.9515912Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2024-08-06T21:24:12.9518963Z * 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 2024-08-06T21:24:12.9522051Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2024-08-06T21:24:12.9524875Z * 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 2024-08-06T21:24:12.9527534Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2024-08-06T21:24:12.9530199Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 123 <- wrt source file 2024-08-06T21:24:12.9533003Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-08-06T21:24:12.9535701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 215 <- wrt source file 2024-08-06T21:24:12.9538280Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-08-06T21:24:12.9540518Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 147 <- wrt source file 2024-08-06T21:24:12.9543194Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-08-06T21:24:12.9545657Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 514 <- wrt source file 2024-08-06T21:24:12.9548037Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-08-06T21:24:12.9550624Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 536 <- wrt source file 2024-08-06T21:24:12.9552837Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-08-06T21:24:12.9555114Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 550 <- wrt source file 2024-08-06T21:24:12.9557455Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-08-06T21:24:12.9559712Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 572 <- wrt source file 2024-08-06T21:24:12.9562110Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-08-06T21:24:12.9564550Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 698 <- wrt source file 2024-08-06T21:24:12.9567049Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-08-06T21:24:12.9569458Z * DOCTEST : 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* DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1001 <- wrt source file 2024-08-06T21:24:12.9659663Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0 2024-08-06T21:24:12.9662048Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1100 <- wrt source file 2024-08-06T21:24:12.9664021Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0 2024-08-06T21:24:12.9665947Z * 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 2024-08-06T21:24:12.9668220Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0 2024-08-06T21:24:12.9670318Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2024-08-06T21:24:12.9716985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 722 <- wrt source file 2024-08-06T21:24:12.9719602Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2024-08-06T21:24:12.9721862Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 173 <- wrt source file 2024-08-06T21:24:12.9723979Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0 2024-08-06T21:24:12.9726234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0, line 653 <- wrt source file 2024-08-06T21:24:12.9728483Z * SKIPPED: 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2024-08-06T21:24:12.9745824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file 2024-08-06T21:24:12.9748267Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2024-08-06T21:24:12.9750497Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 138 <- wrt source file 2024-08-06T21:24:12.9752585Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2024-08-06T21:24:12.9754757Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 171 <- wrt source file 2024-08-06T21:24:12.9757495Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2024-08-06T21:24:12.9759723Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2024-08-06T21:24:12.9761560Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0 2024-08-06T21:24:12.9763660Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 29 <- wrt source file 2024-08-06T21:24:12.9765969Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2024-08-06T21:24:12.9768213Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 19 <- wrt source file 2024-08-06T21:24:12.9770434Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0 2024-08-06T21:24:12.9772439Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 28 <- wrt source file 2024-08-06T21:24:12.9774893Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2024-08-06T21:24:12.9777296Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file 2024-08-06T21:24:12.9779627Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0 2024-08-06T21:24:12.9782353Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 22 <- wrt source file 2024-08-06T21:24:12.9784854Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0 2024-08-06T21:24:12.9787450Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0, line 15 <- wrt source file 2024-08-06T21:24:12.9789527Z * SUCCESS: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2024-08-06T21:24:12.9884601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0, line 60 <- wrt source file 2024-08-06T21:24:12.9887522Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0 2024-08-06T21:24:12.9890340Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 36 <- wrt source file 2024-08-06T21:24:12.9893092Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0 2024-08-06T21:24:12.9896136Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 100 <- wrt source file 2024-08-06T21:24:12.9899412Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2024-08-06T21:24:12.9902149Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0, line 20 <- wrt source file 2024-08-06T21:24:12.9904640Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0 2024-08-06T21:24:12.9907496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 30 <- wrt source file 2024-08-06T21:24:12.9910251Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2024-08-06T21:24:12.9912512Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 17 <- wrt source file 2024-08-06T21:24:12.9914690Z * SUCCESS: 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2024-08-06T21:24:12.9964724Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 25 <- wrt source file 2024-08-06T21:24:12.9966104Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0 2024-08-06T21:24:12.9967363Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0, line 21 <- wrt source file 2024-08-06T21:24:12.9968599Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0 2024-08-06T21:24:12.9969917Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0, line 514 <- wrt source file 2024-08-06T21:24:12.9971133Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0 2024-08-06T21:24:12.9972425Z * DOCTEST : 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 746 <- wrt source file 2024-08-06T21:24:12.9981856Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2024-08-06T21:24:12.9983701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4346 <- wrt source file 2024-08-06T21:24:12.9986546Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2024-08-06T21:24:12.9989320Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4360 <- wrt source file 2024-08-06T21:24:12.9992175Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2024-08-06T21:24:12.9994735Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4390 <- wrt source file 2024-08-06T21:24:12.9997185Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2024-08-06T21:24:12.9999456Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 192 <- wrt source file 2024-08-06T21:24:13.0001621Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2024-08-06T21:24:13.0003989Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 288 <- wrt source file 2024-08-06T21:24:13.0006662Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0 2024-08-06T21:24:13.0009298Z * DOCTEST : /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 2024-08-06T21:24:13.0050388Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2024-08-06T21:24:13.0052110Z ============ 2024-08-06T21:24:13.0052401Z Finished doctests 2024-08-06T21:24:13.0052648Z 334 / 694 passed 2024-08-06T21:24:13.0052894Z  2024-08-06T21:24:13.0053210Z === Found 100 parse-time warnings === 2024-08-06T21:24:13.0053645Z --- Parse Warning: 1 / 100 --- 2024-08-06T21:24:13.0066334Z /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=426. 2024-08-06T21:24:13.0068673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0070605Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-08-06T21:24:13.0073819Z 2024-08-06T21:24:13.0075486Z This is helpful when you want to visualize data over some 2024-08-06T21:24:13.0078987Z range of inputs. See below for a plotting example. 2024-08-06T21:24:13.0080705Z 2024-08-06T21:24:13.0081395Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-08-06T21:24:13.0082247Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-08-06T21:24:13.0083132Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-08-06T21:24:13.0083968Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-08-06T21:24:13.0084794Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-08-06T21:24:13.0085503Z to the result shape. 2024-08-06T21:24:13.0085995Z 2024-08-06T21:24:13.0086351Z .. note:: 2024-08-06T21:24:13.0086880Z 0D inputs are treated equivalently to 1D inputs of a 2024-08-06T21:24:13.0087546Z single element. 2024-08-06T21:24:13.0088007Z 2024-08-06T21:24:13.0088349Z .. warning:: 2024-08-06T21:24:13.0088943Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-08-06T21:24:13.0089932Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-08-06T21:24:13.0090556Z 2024-08-06T21:24:13.0091008Z In the future `torch.meshgrid` will transition to 2024-08-06T21:24:13.0091696Z `indexing='xy'` as the default. 2024-08-06T21:24:13.0092221Z 2024-08-06T21:24:13.0092743Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-08-06T21:24:13.0096102Z this issue with the goal of migrating to NumPy's behavior. 2024-08-06T21:24:13.0105803Z 2024-08-06T21:24:13.0106144Z .. seealso:: 2024-08-06T21:24:13.0106563Z 2024-08-06T21:24:13.0107268Z :func:`torch.cartesian_prod` has the same effect but it 2024-08-06T21:24:13.0108024Z collects the data in a tensor of vectors. 2024-08-06T21:24:13.0109179Z 2024-08-06T21:24:13.0109733Z Args: 2024-08-06T21:24:13.0110935Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-08-06T21:24:13.0111918Z treated as tensors of size :math:`(1,)` automatically 2024-08-06T21:24:13.0112633Z 2024-08-06T21:24:13.0113812Z indexing: (str, optional): the indexing mode, either "xy" 2024-08-06T21:24:13.0114654Z or "ij", defaults to "ij". See warning for future changes. 2024-08-06T21:24:13.0115590Z 2024-08-06T21:24:13.0116049Z If "xy" is selected, the first dimension corresponds 2024-08-06T21:24:13.0116825Z to the cardinality of the second input and the second 2024-08-06T21:24:13.0117618Z dimension corresponds to the cardinality of the first 2024-08-06T21:24:13.0118299Z input. 2024-08-06T21:24:13.0118724Z 2024-08-06T21:24:13.0119154Z If "ij" is selected, the dimensions are in the same 2024-08-06T21:24:13.0119942Z order as the cardinality of the inputs. 2024-08-06T21:24:13.0120537Z 2024-08-06T21:24:13.0120863Z Returns: 2024-08-06T21:24:13.0121401Z seq (sequence of Tensors): If the input has :math:`N` 2024-08-06T21:24:13.0122209Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-08-06T21:24:13.0123004Z output will also have :math:`N` tensors, where each tensor 2024-08-06T21:24:13.0123740Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-08-06T21:24:13.0124293Z 2024-08-06T21:24:13.0124631Z Example:: 2024-08-06T21:24:13.0125039Z 2024-08-06T21:24:13.0125420Z >>> x = torch.tensor([1, 2, 3]) 2024-08-06T21:24:13.0125984Z >>> y = torch.tensor([4, 5, 6]) 2024-08-06T21:24:13.0126613Z 2024-08-06T21:24:13.0127133Z Observe the element-wise pairings across the grid, (1, 4), 2024-08-06T21:24:13.0127904Z (1, 5), ..., (3, 6). This is the same thing as the 2024-08-06T21:24:13.0128524Z cartesian product. 2024-08-06T21:24:13.0129153Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-08-06T21:24:13.0129778Z >>> grid_x 2024-08-06T21:24:13.0131384Z tensor([[1, 1, 1], 2024-08-06T21:24:13.0131876Z [2, 2, 2], 2024-08-06T21:24:13.0132343Z [3, 3, 3]]) 2024-08-06T21:24:13.0132832Z >>> grid_y 2024-08-06T21:24:13.0133283Z tensor([[4, 5, 6], 2024-08-06T21:24:13.0133751Z [4, 5, 6], 2024-08-06T21:24:13.0134220Z [4, 5, 6]]) 2024-08-06T21:24:13.0134691Z 2024-08-06T21:24:13.0135123Z This correspondence can be seen when these grids are 2024-08-06T21:24:13.0135797Z stacked properly. 2024-08-06T21:24:13.0136503Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-08-06T21:24:13.0137286Z ... torch.cartesian_prod(x, y)) 2024-08-06T21:24:13.0137971Z True 2024-08-06T21:24:13.0138378Z 2024-08-06T21:24:13.0138875Z `torch.meshgrid` is commonly used to produce a grid for 2024-08-06T21:24:13.0139564Z plotting. 2024-08-06T21:24:13.0140086Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-08-06T21:24:13.0140736Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-08-06T21:24:13.0141393Z >>> import matplotlib.pyplot as plt 2024-08-06T21:24:13.0142036Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-08-06T21:24:13.0142830Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-08-06T21:24:13.0143495Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-08-06T21:24:13.0144158Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-08-06T21:24:13.0144785Z >>> ax = plt.axes(projection='3d') 2024-08-06T21:24:13.0145463Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-08-06T21:24:13.0146104Z >>> plt.show() 2024-08-06T21:24:13.0146541Z 2024-08-06T21:24:13.0147012Z .. image:: ../_static/img/meshgrid.png 2024-08-06T21:24:13.0147577Z :width: 512 2024-08-06T21:24:13.0147998Z 2024-08-06T21:24:13.0148327Z 2024-08-06T21:24:13.0148996Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0149807Z 2024-08-06T21:24:13.0150162Z warnings.warn(msg) 2024-08-06T21:24:13.0150608Z 2024-08-06T21:24:13.0151166Z --- Parse Warning: 2 / 100 --- 2024-08-06T21:24:13.0153172Z /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=815. 2024-08-06T21:24:13.0155534Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0156933Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-08-06T21:24:13.0157988Z 2024-08-06T21:24:13.0158431Z Returns the unique elements of the input tensor. 2024-08-06T21:24:13.0159031Z 2024-08-06T21:24:13.0159750Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-08-06T21:24:13.0160866Z this function also eliminates non-consecutive duplicate values. 2024-08-06T21:24:13.0161594Z 2024-08-06T21:24:13.0162180Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-08-06T21:24:13.0163327Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-08-06T21:24:13.0164645Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-08-06T21:24:13.0174058Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-08-06T21:24:13.0175645Z 2024-08-06T21:24:13.0175990Z Args: 2024-08-06T21:24:13.0177677Z input (Tensor): the input tensor 2024-08-06T21:24:13.0178454Z sorted (bool): Whether to sort the unique elements in ascending order 2024-08-06T21:24:13.0179249Z before returning as output. 2024-08-06T21:24:13.0179999Z return_inverse (bool): Whether to also return the indices for where 2024-08-06T21:24:13.0180982Z elements in the original input ended up in the returned unique list. 2024-08-06T21:24:13.0181982Z return_counts (bool): Whether to also return the counts for each unique 2024-08-06T21:24:13.0182748Z element. 2024-08-06T21:24:13.0183389Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-08-06T21:24:13.0184330Z unique of the flattened input is returned. Otherwise, each of the 2024-08-06T21:24:13.0185267Z tensors indexed by the given dimension is treated as one of the 2024-08-06T21:24:13.0186332Z elements to apply the unique operation upon. See examples for more 2024-08-06T21:24:13.0187188Z details. Default: ``None`` 2024-08-06T21:24:13.0187695Z 2024-08-06T21:24:13.0188034Z Returns: 2024-08-06T21:24:13.0188780Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-08-06T21:24:13.0189637Z 2024-08-06T21:24:13.0190167Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-08-06T21:24:13.0190968Z - **inverse_indices** (*Tensor*): (optional) if 2024-08-06T21:24:13.0191726Z :attr:`return_inverse` is True, there will be an additional 2024-08-06T21:24:13.0192631Z returned tensor (same shape as input) representing the indices 2024-08-06T21:24:13.0193572Z for where elements in the original input map to in the output; 2024-08-06T21:24:13.0194478Z otherwise, this function will only return a single tensor. 2024-08-06T21:24:13.0195226Z - **counts** (*Tensor*): (optional) if 2024-08-06T21:24:13.0195959Z :attr:`return_counts` is True, there will be an additional 2024-08-06T21:24:13.0196820Z returned tensor (same shape as output or output.size(dim), 2024-08-06T21:24:13.0197697Z if dim was specified) representing the number of occurrences 2024-08-06T21:24:13.0198450Z for each unique value or tensor. 2024-08-06T21:24:13.0199003Z 2024-08-06T21:24:13.0199338Z Example:: 2024-08-06T21:24:13.0199719Z 2024-08-06T21:24:13.0200297Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-08-06T21:24:13.0201047Z >>> output 2024-08-06T21:24:13.0201472Z tensor([1, 2, 3]) 2024-08-06T21:24:13.0201962Z 2024-08-06T21:24:13.0202374Z >>> output, inverse_indices = torch.unique( 2024-08-06T21:24:13.0203237Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-06T21:24:13.0204032Z >>> output 2024-08-06T21:24:13.0204448Z tensor([1, 2, 3]) 2024-08-06T21:24:13.0204919Z >>> inverse_indices 2024-08-06T21:24:13.0205383Z tensor([0, 2, 1, 2]) 2024-08-06T21:24:13.0205846Z 2024-08-06T21:24:13.0206253Z >>> output, inverse_indices = torch.unique( 2024-08-06T21:24:13.0207083Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-06T21:24:13.0207881Z >>> output 2024-08-06T21:24:13.0208303Z tensor([1, 2, 3]) 2024-08-06T21:24:13.0208805Z >>> inverse_indices 2024-08-06T21:24:13.0209281Z tensor([[0, 2], 2024-08-06T21:24:13.0209729Z [1, 2]]) 2024-08-06T21:24:13.0210160Z 2024-08-06T21:24:13.0210519Z >>> a = torch.tensor([ 2024-08-06T21:24:13.0211010Z ... [ 2024-08-06T21:24:13.0211421Z ... [1, 1, 0, 0], 2024-08-06T21:24:13.0211915Z ... [1, 1, 0, 0], 2024-08-06T21:24:13.0212399Z ... [0, 0, 1, 1], 2024-08-06T21:24:13.0212874Z ... ], 2024-08-06T21:24:13.0213274Z ... [ 2024-08-06T21:24:13.0213679Z ... [0, 0, 1, 1], 2024-08-06T21:24:13.0214164Z ... [0, 0, 1, 1], 2024-08-06T21:24:13.0214656Z ... [1, 1, 1, 1], 2024-08-06T21:24:13.0215140Z ... ], 2024-08-06T21:24:13.0215536Z ... [ 2024-08-06T21:24:13.0215943Z ... [1, 1, 0, 0], 2024-08-06T21:24:13.0216438Z ... [1, 1, 0, 0], 2024-08-06T21:24:13.0216926Z ... [0, 0, 1, 1], 2024-08-06T21:24:13.0217412Z ... ], 2024-08-06T21:24:13.0217815Z ... ]) 2024-08-06T21:24:13.0218180Z 2024-08-06T21:24:13.0218875Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-08-06T21:24:13.0219840Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-08-06T21:24:13.0220646Z >>> # each other, so one of them will be removed. 2024-08-06T21:24:13.0221260Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-08-06T21:24:13.0221802Z tensor(True) 2024-08-06T21:24:13.0222278Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-08-06T21:24:13.0222859Z >>> a_unique_dim0 2024-08-06T21:24:13.0223334Z tensor([[[0, 0, 1, 1], 2024-08-06T21:24:13.0223812Z [0, 0, 1, 1], 2024-08-06T21:24:13.0224290Z [1, 1, 1, 1]], 2024-08-06T21:24:13.0224766Z [[1, 1, 0, 0], 2024-08-06T21:24:13.0225237Z [1, 1, 0, 0], 2024-08-06T21:24:13.0225715Z [0, 0, 1, 1]]]) 2024-08-06T21:24:13.0226180Z 2024-08-06T21:24:13.0226815Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-08-06T21:24:13.0227599Z >>> # `a_unique_dim0`: 2024-08-06T21:24:13.0228127Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-08-06T21:24:13.0228713Z tensor(True) 2024-08-06T21:24:13.0229198Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-08-06T21:24:13.0229765Z tensor(True) 2024-08-06T21:24:13.0230187Z 2024-08-06T21:24:13.0230730Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-08-06T21:24:13.0231629Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-08-06T21:24:13.0232353Z >>> # them will be removed. 2024-08-06T21:24:13.0232896Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-08-06T21:24:13.0233411Z tensor(True) 2024-08-06T21:24:13.0233871Z >>> torch.unique(a, dim=1) 2024-08-06T21:24:13.0234446Z tensor([[[0, 0, 1, 1], 2024-08-06T21:24:13.0234926Z [1, 1, 0, 0]], 2024-08-06T21:24:13.0235420Z [[1, 1, 1, 1], 2024-08-06T21:24:13.0235900Z [0, 0, 1, 1]], 2024-08-06T21:24:13.0236370Z [[0, 0, 1, 1], 2024-08-06T21:24:13.0236848Z [1, 1, 0, 0]]]) 2024-08-06T21:24:13.0237331Z 2024-08-06T21:24:13.0237872Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-08-06T21:24:13.0238733Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-08-06T21:24:13.0239534Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-08-06T21:24:13.0240239Z >>> # sub-tensors will be removed. 2024-08-06T21:24:13.0240849Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-08-06T21:24:13.0241387Z tensor(True) 2024-08-06T21:24:13.0241825Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-08-06T21:24:13.0242355Z tensor(True) 2024-08-06T21:24:13.0242973Z >>> torch.unique(a, dim=2) 2024-08-06T21:24:13.0243495Z tensor([[[0, 1], 2024-08-06T21:24:13.0243952Z [0, 1], 2024-08-06T21:24:13.0244396Z [1, 0]], 2024-08-06T21:24:13.0244834Z [[1, 0], 2024-08-06T21:24:13.0245281Z [1, 0], 2024-08-06T21:24:13.0245727Z [1, 1]], 2024-08-06T21:24:13.0246165Z [[0, 1], 2024-08-06T21:24:13.0246603Z [0, 1], 2024-08-06T21:24:13.0247053Z [1, 0]]]) 2024-08-06T21:24:13.0247492Z 2024-08-06T21:24:13.0248147Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0249002Z 2024-08-06T21:24:13.0249344Z warnings.warn(msg) 2024-08-06T21:24:13.0249776Z 2024-08-06T21:24:13.0250343Z --- Parse Warning: 3 / 100 --- 2024-08-06T21:24:13.0252391Z /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. 2024-08-06T21:24:13.0254560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0255416Z 2024-08-06T21:24:13.0255875Z Load a model from a github repo or a local directory. 2024-08-06T21:24:13.0256511Z 2024-08-06T21:24:13.0257115Z Note: Loading a model is the typical use case, but this can also be used to 2024-08-06T21:24:13.0258120Z for loading other objects such as tokenizers, loss functions, etc. 2024-08-06T21:24:13.0258870Z 2024-08-06T21:24:13.0259356Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-08-06T21:24:13.0260156Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-08-06T21:24:13.0260841Z ref (a tag or a branch). 2024-08-06T21:24:13.0261307Z 2024-08-06T21:24:13.0261777Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-08-06T21:24:13.0262486Z path to a local directory. 2024-08-06T21:24:13.0262970Z 2024-08-06T21:24:13.0263290Z Args: 2024-08-06T21:24:13.0263719Z repo_or_dir (str): If ``source`` is 'github', 2024-08-06T21:24:13.0264672Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-08-06T21:24:13.0265959Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-08-06T21:24:13.0267274Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-08-06T21:24:13.0268323Z If ``source`` is 'local' then it should be a path to a local directory. 2024-08-06T21:24:13.0269230Z model (str): the name of a callable (entrypoint) defined in the 2024-08-06T21:24:13.0269964Z repo/dir's ``hubconf.py``. 2024-08-06T21:24:13.0270775Z *args (optional): the corresponding args for callable ``model``. 2024-08-06T21:24:13.0271634Z source (str, optional): 'github' or 'local'. Specifies how 2024-08-06T21:24:13.0272450Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-08-06T21:24:13.0273337Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-08-06T21:24:13.0274330Z This parameter was introduced in v1.12 and helps ensuring that users 2024-08-06T21:24:13.0275173Z only run code from repos that they trust. 2024-08-06T21:24:13.0275747Z 2024-08-06T21:24:13.0276255Z - If ``False``, a prompt will ask the user whether the repo should 2024-08-06T21:24:13.0276969Z be trusted. 2024-08-06T21:24:13.0277582Z - If ``True``, the repo will be added to the trusted list and loaded 2024-08-06T21:24:13.0278401Z without requiring explicit confirmation. 2024-08-06T21:24:13.0279135Z - If ``"check"``, the repo will be checked against the list of 2024-08-06T21:24:13.0280025Z trusted repos in the cache. If it is not present in that list, the 2024-08-06T21:24:13.0280950Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-08-06T21:24:13.0281846Z - If ``None``: this will raise a warning, inviting the user to set 2024-08-06T21:24:13.0282719Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-08-06T21:24:13.0283639Z is only present for backward compatibility and will be removed in 2024-08-06T21:24:13.0284367Z v2.0. 2024-08-06T21:24:13.0284751Z 2024-08-06T21:24:13.0285282Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-08-06T21:24:13.0286250Z force_reload (bool, optional): whether to force a fresh download of 2024-08-06T21:24:13.0287176Z the github repo unconditionally. Does not have any effect if 2024-08-06T21:24:13.0287949Z ``source = 'local'``. Default is ``False``. 2024-08-06T21:24:13.0288788Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-08-06T21:24:13.0289743Z local caches. Note that the message about first download cannot be 2024-08-06T21:24:13.0290633Z muted. Does not have any effect if ``source = 'local'``. 2024-08-06T21:24:13.0291300Z Default is ``True``. 2024-08-06T21:24:13.0292168Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-08-06T21:24:13.0293440Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-08-06T21:24:13.0294643Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-08-06T21:24:13.0295674Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-08-06T21:24:13.0296599Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-08-06T21:24:13.0297338Z 2024-08-06T21:24:13.0297682Z Returns: 2024-08-06T21:24:13.0298246Z The output of the ``model`` callable when called with the given 2024-08-06T21:24:13.0298943Z ``*args`` and ``**kwargs``. 2024-08-06T21:24:13.0299430Z 2024-08-06T21:24:13.0299762Z Example: 2024-08-06T21:24:13.0300197Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-06T21:24:13.0300813Z >>> # from a github repo 2024-08-06T21:24:13.0301322Z >>> repo = "pytorch/vision" 2024-08-06T21:24:13.0301830Z >>> model = torch.hub.load( 2024-08-06T21:24:13.0302504Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-08-06T21:24:13.0303194Z ... ) 2024-08-06T21:24:13.0303566Z >>> # from a local directory 2024-08-06T21:24:13.0304141Z >>> path = "/some/local/path/pytorch/vision" 2024-08-06T21:24:13.0304735Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.0305461Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-08-06T21:24:13.0306304Z 2024-08-06T21:24:13.0307022Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0307823Z 2024-08-06T21:24:13.0308179Z warnings.warn(msg) 2024-08-06T21:24:13.0308609Z 2024-08-06T21:24:13.0309141Z --- Parse Warning: 4 / 100 --- 2024-08-06T21:24:13.0311146Z /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. 2024-08-06T21:24:13.0313421Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0314360Z Download object at the given URL to a local path. 2024-08-06T21:24:13.0315016Z 2024-08-06T21:24:13.0315352Z Args: 2024-08-06T21:24:13.0315773Z url (str): URL of the object to download 2024-08-06T21:24:13.0316621Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-08-06T21:24:13.0317865Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-08-06T21:24:13.0318832Z Default: None 2024-08-06T21:24:13.0319599Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-08-06T21:24:13.0320444Z Default: True 2024-08-06T21:24:13.0320885Z 2024-08-06T21:24:13.0321225Z Example: 2024-08-06T21:24:13.0321695Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-06T21:24:13.0322327Z >>> # xdoctest: +REQUIRES(POSIX) 2024-08-06T21:24:13.0322919Z >>> torch.hub.download_url_to_file( 2024-08-06T21:24:13.0323721Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-08-06T21:24:13.0324505Z ... "/tmp/temporary_file", 2024-08-06T21:24:13.0325037Z ... ) 2024-08-06T21:24:13.0325413Z 2024-08-06T21:24:13.0325735Z 2024-08-06T21:24:13.0326474Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0327248Z 2024-08-06T21:24:13.0327593Z warnings.warn(msg) 2024-08-06T21:24:13.0328033Z 2024-08-06T21:24:13.0328547Z --- Parse Warning: 5 / 100 --- 2024-08-06T21:24:13.0330560Z /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. 2024-08-06T21:24:13.0332855Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0333823Z Loads the Torch serialized object at the given URL. 2024-08-06T21:24:13.0334430Z 2024-08-06T21:24:13.0334942Z If downloaded file is a zip file, it will be automatically 2024-08-06T21:24:13.0335633Z decompressed. 2024-08-06T21:24:13.0336032Z 2024-08-06T21:24:13.0336605Z If the object is already present in `model_dir`, it's deserialized and 2024-08-06T21:24:13.0337370Z returned. 2024-08-06T21:24:13.0337964Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-08-06T21:24:13.0338906Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-08-06T21:24:13.0339621Z 2024-08-06T21:24:13.0339951Z Args: 2024-08-06T21:24:13.0340384Z url (str): URL of the object to download 2024-08-06T21:24:13.0341153Z model_dir (str, optional): directory in which to save the object 2024-08-06T21:24:13.0342348Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-08-06T21:24:13.0343785Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-08-06T21:24:13.0344632Z Default: True 2024-08-06T21:24:13.0345638Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-08-06T21:24:13.0346892Z ``filename-.ext`` where ```` is the first eight or more 2024-08-06T21:24:13.0347904Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-08-06T21:24:13.0348867Z ensure unique names and to verify the contents of the file. 2024-08-06T21:24:13.0349568Z Default: False 2024-08-06T21:24:13.0350487Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-08-06T21:24:13.0351904Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-08-06T21:24:13.0353164Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-08-06T21:24:13.0354056Z 2024-08-06T21:24:13.0354398Z Example: 2024-08-06T21:24:13.0354856Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-06T21:24:13.0355579Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-08-06T21:24:13.0356433Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-08-06T21:24:13.0357165Z ... ) 2024-08-06T21:24:13.0357543Z 2024-08-06T21:24:13.0357874Z 2024-08-06T21:24:13.0358515Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0359344Z 2024-08-06T21:24:13.0359700Z warnings.warn(msg) 2024-08-06T21:24:13.0360125Z 2024-08-06T21:24:13.0360659Z --- Parse Warning: 6 / 100 --- 2024-08-06T21:24:13.0362692Z /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=334. 2024-08-06T21:24:13.0364947Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-06T21:24:13.0366060Z Registers the function implementation as the fallback for the given key. 2024-08-06T21:24:13.0366965Z 2024-08-06T21:24:13.0367532Z This function only works for a library with global namespace ("_"). 2024-08-06T21:24:13.0368286Z 2024-08-06T21:24:13.0368618Z Args: 2024-08-06T21:24:13.0369349Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-08-06T21:24:13.0370287Z to register a fallthrough. 2024-08-06T21:24:13.0371293Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-08-06T21:24:13.0372390Z the dispatch key that the library was created with. 2024-08-06T21:24:13.0373591Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-08-06T21:24:13.0375089Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-08-06T21:24:13.0376046Z 2024-08-06T21:24:13.0376396Z Example:: 2024-08-06T21:24:13.0376841Z >>> my_lib = Library("_", "IMPL") 2024-08-06T21:24:13.0377475Z >>> def fallback_kernel(op, *args, **kwargs): 2024-08-06T21:24:13.0378127Z >>> # Handle all autocast ops generically 2024-08-06T21:24:13.0378713Z >>> # ... 2024-08-06T21:24:13.0379260Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-08-06T21:24:13.0379861Z 2024-08-06T21:24:13.0381252Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-08-06T21:24:13.0382773Z 2024-08-06T21:24:13.0383179Z my_lib.fallback(fallback_kernel, "Autocast") 2024-08-06T21:24:13.0383799Z ^ 2024-08-06T21:24:13.0384147Z warnings.warn(msg) 2024-08-06T21:24:13.0384560Z 2024-08-06T21:24:13.0385077Z --- Parse Warning: 7 / 100 --- 2024-08-06T21:24:13.0387136Z /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=677. 2024-08-06T21:24:13.0389337Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-06T21:24:13.0390415Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-08-06T21:24:13.0391187Z 2024-08-06T21:24:13.0391679Z Also sometimes known as a "meta kernel", "abstract impl". 2024-08-06T21:24:13.0392346Z 2024-08-06T21:24:13.0392984Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-08-06T21:24:13.0394085Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-08-06T21:24:13.0395133Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-08-06T21:24:13.0396028Z what the properties of the output Tensors are. 2024-08-06T21:24:13.0396619Z 2024-08-06T21:24:13.0397231Z The FakeTensor implementation has the same signature as the operator. 2024-08-06T21:24:13.0398245Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-08-06T21:24:13.0399217Z implementation, assume that all Tensor inputs to the operator are 2024-08-06T21:24:13.0400195Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-08-06T21:24:13.0401152Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-08-06T21:24:13.0402146Z The FakeTensor implementation must consist of only PyTorch operations 2024-08-06T21:24:13.0403143Z (and may not directly access the storage or data of any input or 2024-08-06T21:24:13.0403877Z intermediate Tensors). 2024-08-06T21:24:13.0404339Z 2024-08-06T21:24:13.0404785Z This API may be used as a decorator (see examples). 2024-08-06T21:24:13.0405402Z 2024-08-06T21:24:13.0405908Z For a detailed guide on custom ops, please see 2024-08-06T21:24:13.0406780Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-08-06T21:24:13.0407556Z 2024-08-06T21:24:13.0407882Z Examples: 2024-08-06T21:24:13.0408288Z >>> import torch 2024-08-06T21:24:13.0408774Z >>> import numpy as np 2024-08-06T21:24:13.0409296Z >>> from torch import Tensor 2024-08-06T21:24:13.0409829Z >>> 2024-08-06T21:24:13.0410391Z >>> # Example 1: an operator without data-dependent output shape 2024-08-06T21:24:13.0411322Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-08-06T21:24:13.0412430Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-08-06T21:24:13.0413364Z >>> raise NotImplementedError("Implementation goes here") 2024-08-06T21:24:13.0414033Z >>> 2024-08-06T21:24:13.0414558Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-08-06T21:24:13.0415242Z >>> def _(x, weight, bias): 2024-08-06T21:24:13.0415766Z >>> assert x.dim() == 2 2024-08-06T21:24:13.0416339Z >>> assert weight.dim() == 2 2024-08-06T21:24:13.0416915Z >>> assert bias.dim() == 1 2024-08-06T21:24:13.0417507Z >>> assert x.shape[1] == weight.shape[1] 2024-08-06T21:24:13.0418149Z >>> assert weight.shape[0] == bias.shape[0] 2024-08-06T21:24:13.0418784Z >>> assert x.device == weight.device 2024-08-06T21:24:13.0419335Z >>> 2024-08-06T21:24:13.0419748Z >>> return (x @ weight.t()) + bias 2024-08-06T21:24:13.0420298Z >>> 2024-08-06T21:24:13.0420804Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-08-06T21:24:13.0421548Z >>> x = torch.randn(2, 3) 2024-08-06T21:24:13.0422094Z >>> w = torch.randn(3, 3) 2024-08-06T21:24:13.0422626Z >>> b = torch.randn(3) 2024-08-06T21:24:13.0423222Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-08-06T21:24:13.0423813Z >>> 2024-08-06T21:24:13.0424202Z >>> assert y.shape == (2, 3) 2024-08-06T21:24:13.0424718Z >>> 2024-08-06T21:24:13.0425250Z >>> # Example 2: an operator with data-dependent output shape 2024-08-06T21:24:13.0426153Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-08-06T21:24:13.0427075Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-08-06T21:24:13.0427693Z >>> x_np = x.numpy(force=True) 2024-08-06T21:24:13.0428339Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-08-06T21:24:13.0429006Z >>> return torch.tensor(res, device=x.device) 2024-08-06T21:24:13.0429602Z >>> 2024-08-06T21:24:13.0430108Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-08-06T21:24:13.0430778Z >>> def _(x): 2024-08-06T21:24:13.0431311Z >>> # Number of nonzero-elements is data-dependent. 2024-08-06T21:24:13.0432033Z >>> # Since we cannot peek at the data in an fake impl, 2024-08-06T21:24:13.0432790Z >>> # we use the ctx object to construct a new symint that 2024-08-06T21:24:13.0433493Z >>> # represents the data-dependent size. 2024-08-06T21:24:13.0434127Z >>> ctx = torch.library.get_ctx() 2024-08-06T21:24:13.0434717Z >>> nnz = ctx.new_dynamic_size() 2024-08-06T21:24:13.0435303Z >>> shape = [nnz, x.dim()] 2024-08-06T21:24:13.0435939Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-08-06T21:24:13.0436564Z >>> return result 2024-08-06T21:24:13.0437045Z >>> 2024-08-06T21:24:13.0437583Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-06T21:24:13.0438249Z >>> 2024-08-06T21:24:13.0438734Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-08-06T21:24:13.0439575Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-08-06T21:24:13.0440405Z >>> trace.print_readable() 2024-08-06T21:24:13.0440926Z >>> 2024-08-06T21:24:13.0441546Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-08-06T21:24:13.0442298Z 2024-08-06T21:24:13.0442771Z 2024-08-06T21:24:13.0443940Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-08-06T21:24:13.0445269Z 2024-08-06T21:24:13.0445601Z _._ = None 2024-08-06T21:24:13.0445955Z ^ 2024-08-06T21:24:13.0446307Z warnings.warn(msg) 2024-08-06T21:24:13.0446745Z 2024-08-06T21:24:13.0447276Z --- Parse Warning: 8 / 100 --- 2024-08-06T21:24:13.0449300Z /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=798. 2024-08-06T21:24:13.0451587Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0452525Z Register a backward formula for this custom op. 2024-08-06T21:24:13.0453106Z 2024-08-06T21:24:13.0453655Z In order for an operator to work with autograd, you need to register 2024-08-06T21:24:13.0454410Z a backward formula: 2024-08-06T21:24:13.0455054Z 1. You must tell us how to compute gradients during the backward pass 2024-08-06T21:24:13.0455841Z by providing us a "backward" function. 2024-08-06T21:24:13.0456631Z 2. If you need any values from the forward to compute gradients, you can 2024-08-06T21:24:13.0457454Z use `setup_context` to save values for backward. 2024-08-06T21:24:13.0458138Z 2024-08-06T21:24:13.0458734Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-08-06T21:24:13.0459688Z - ``grads`` is one or more gradients. The number of gradients matches 2024-08-06T21:24:13.0460462Z the number of outputs of the operator. 2024-08-06T21:24:13.0461257Z The ``ctx`` object is `the same ctx object `_ used by 2024-08-06T21:24:13.0462295Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-08-06T21:24:13.0463210Z same as :meth:`torch.autograd.Function.backward`. 2024-08-06T21:24:13.0463834Z 2024-08-06T21:24:13.0464390Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-08-06T21:24:13.0465405Z Please save quantities needed for backward onto the ``ctx`` object via 2024-08-06T21:24:13.0466499Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-08-06T21:24:13.0467552Z or assigning them as attributes of ``ctx``. If your custom op has 2024-08-06T21:24:13.0468512Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-08-06T21:24:13.0469455Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-08-06T21:24:13.0470143Z 2024-08-06T21:24:13.0504461Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-08-06T21:24:13.0505484Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-08-06T21:24:13.0506520Z not depend on or mutate global state. If you need a non-traceable backward, 2024-08-06T21:24:13.0507620Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-08-06T21:24:13.0508365Z 2024-08-06T21:24:13.0508699Z Examples: 2024-08-06T21:24:13.0509075Z >>> import torch 2024-08-06T21:24:13.0509544Z >>> import numpy as np 2024-08-06T21:24:13.0510065Z >>> from torch import Tensor 2024-08-06T21:24:13.0510602Z >>> 2024-08-06T21:24:13.0511418Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-08-06T21:24:13.0512193Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-08-06T21:24:13.0512793Z >>> x_np = x.cpu().numpy() 2024-08-06T21:24:13.0513344Z >>> y_np = np.sin(x_np) 2024-08-06T21:24:13.0513975Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-06T21:24:13.0514615Z >>> 2024-08-06T21:24:13.0515092Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-08-06T21:24:13.0515717Z >>> x, = inputs 2024-08-06T21:24:13.0516217Z >>> ctx.save_for_backward(x) 2024-08-06T21:24:13.0516752Z >>> 2024-08-06T21:24:13.0517146Z >>> def backward(ctx, grad): 2024-08-06T21:24:13.0517701Z >>> x, = ctx.saved_tensors 2024-08-06T21:24:13.0518252Z >>> return grad * x.cos() 2024-08-06T21:24:13.0518759Z >>> 2024-08-06T21:24:13.0519190Z >>> torch.library.register_autograd( 2024-08-06T21:24:13.0519926Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-08-06T21:24:13.0520595Z ... ) 2024-08-06T21:24:13.0520973Z >>> 2024-08-06T21:24:13.0521395Z >>> x = torch.randn(3, requires_grad=True) 2024-08-06T21:24:13.0521969Z >>> y = numpy_sin(x) 2024-08-06T21:24:13.0522590Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-06T21:24:13.0523315Z >>> assert torch.allclose(grad_x, x.cos()) 2024-08-06T21:24:13.0523877Z >>> 2024-08-06T21:24:13.0524300Z >>> # Example with a keyword-only arg 2024-08-06T21:24:13.0525068Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-06T21:24:13.0525891Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-08-06T21:24:13.0526594Z >>> x_np = x.cpu().numpy() 2024-08-06T21:24:13.0527148Z >>> y_np = x_np * val 2024-08-06T21:24:13.0527766Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-06T21:24:13.0528403Z >>> 2024-08-06T21:24:13.0529027Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-08-06T21:24:13.0529854Z >>> ctx.val = keyword_only_inputs["val"] 2024-08-06T21:24:13.0530429Z >>> 2024-08-06T21:24:13.0530836Z >>> def backward(ctx, grad): 2024-08-06T21:24:13.0531380Z >>> return grad * ctx.val 2024-08-06T21:24:13.0531903Z >>> 2024-08-06T21:24:13.0532336Z >>> torch.library.register_autograd( 2024-08-06T21:24:13.0533045Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-08-06T21:24:13.0533771Z ... ) 2024-08-06T21:24:13.0534153Z >>> 2024-08-06T21:24:13.0534577Z >>> x = torch.randn(3, requires_grad=True) 2024-08-06T21:24:13.0535189Z >>> y = numpy_mul(x, val=3.14) 2024-08-06T21:24:13.0535878Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-06T21:24:13.0536688Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-08-06T21:24:13.0537363Z 2024-08-06T21:24:13.0537703Z 2024-08-06T21:24:13.0538342Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0539157Z 2024-08-06T21:24:13.0539515Z warnings.warn(msg) 2024-08-06T21:24:13.0539929Z 2024-08-06T21:24:13.0540526Z --- Parse Warning: 9 / 100 --- 2024-08-06T21:24:13.0542629Z /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=1206. 2024-08-06T21:24:13.0544854Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0545937Z Given an operator and some sample arguments, tests if the operator is 2024-08-06T21:24:13.0546939Z registered correctly. 2024-08-06T21:24:13.0547393Z 2024-08-06T21:24:13.0547965Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-08-06T21:24:13.0549000Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-08-06T21:24:13.0550055Z and these APIs require that the functions you pass them satisfy certain 2024-08-06T21:24:13.0551090Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-08-06T21:24:13.0551973Z ``opcheck`` tests these metadata and properties. 2024-08-06T21:24:13.0552563Z 2024-08-06T21:24:13.0552954Z Concretely, we test the following: 2024-08-06T21:24:13.0553617Z - test_schema: if the operator's schema is correct. 2024-08-06T21:24:13.0554460Z - test_autograd_registration: if autograd was registered correctly. 2024-08-06T21:24:13.0555382Z - test_faketensor: If the operator has a FakeTensor kernel 2024-08-06T21:24:13.0556217Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-08-06T21:24:13.0557107Z but not sufficient) for the operator to work with PyTorch compilation 2024-08-06T21:24:13.0557835Z APIs (torch.compile/export/FX). 2024-08-06T21:24:13.0558573Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-08-06T21:24:13.0559444Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-08-06T21:24:13.0560341Z This checks that the outputs (and gradients, if applicable) are the 2024-08-06T21:24:13.0561193Z same under eager-mode PyTorch and torch.compile. 2024-08-06T21:24:13.0561884Z This test is a superset of ``test_faketensor``. 2024-08-06T21:24:13.0562479Z 2024-08-06T21:24:13.0563017Z For best results, please call ``opcheck`` multiple times with a 2024-08-06T21:24:13.0563954Z representative set of inputs. If your operator supports 2024-08-06T21:24:13.0564911Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-08-06T21:24:13.0565961Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-08-06T21:24:13.0566847Z use ``opcheck`` with inputs on all supported devices. 2024-08-06T21:24:13.0567475Z 2024-08-06T21:24:13.0567804Z Args: 2024-08-06T21:24:13.0568288Z op: The operator. Must either be a function decorated with 2024-08-06T21:24:13.0569200Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-08-06T21:24:13.0570171Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-08-06T21:24:13.0570966Z args: The args to the operator 2024-08-06T21:24:13.0571609Z kwargs: The kwargs to the operator 2024-08-06T21:24:13.0572333Z test_utils: Tests that we should run. Default: all of them. 2024-08-06T21:24:13.0573110Z Example: ("test_schema", "test_faketensor") 2024-08-06T21:24:13.0573917Z raise_exception: If we should raise an exception on the first 2024-08-06T21:24:13.0574751Z error. If False, we will return a dict with information 2024-08-06T21:24:13.0575462Z on if each test passed or not. 2024-08-06T21:24:13.0576007Z 2024-08-06T21:24:13.0576341Z .. warning:: 2024-08-06T21:24:13.0576728Z 2024-08-06T21:24:13.0577308Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-08-06T21:24:13.0577677Z opcheck tests if your usage of torch.library APIs is correct while 2024-08-06T21:24:13.0578055Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-08-06T21:24:13.0578457Z mathematically correct. Use both to test custom ops that support 2024-08-06T21:24:13.0578634Z gradient computation. 2024-08-06T21:24:13.0578771Z 2024-08-06T21:24:13.0578928Z Example: 2024-08-06T21:24:13.0579061Z 2024-08-06T21:24:13.0579363Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:13.0579732Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-06T21:24:13.0579969Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-08-06T21:24:13.0580150Z >>> x_np = x.numpy(force=True) 2024-08-06T21:24:13.0580323Z >>> z_np = x_np + y 2024-08-06T21:24:13.0580548Z >>> return torch.from_numpy(z_np).to(x.device) 2024-08-06T21:24:13.0580686Z >>> 2024-08-06T21:24:13.0580883Z >>> @numpy_sin.register_fake 2024-08-06T21:24:13.0581036Z >>> def _(x, y): 2024-08-06T21:24:13.0581234Z >>> return torch.empty_like(x) 2024-08-06T21:24:13.0581376Z >>> 2024-08-06T21:24:13.0581593Z >>> def setup_context(ctx, inputs, output): 2024-08-06T21:24:13.0581763Z >>> y, = inputs 2024-08-06T21:24:13.0581912Z >>> ctx.y = y 2024-08-06T21:24:13.0582055Z >>> 2024-08-06T21:24:13.0582247Z >>> def backward(ctx, grad): 2024-08-06T21:24:13.0582433Z >>> return grad * ctx.y, None 2024-08-06T21:24:13.0582573Z >>> 2024-08-06T21:24:13.0582978Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-08-06T21:24:13.0583114Z >>> 2024-08-06T21:24:13.0583285Z >>> sample_inputs = [ 2024-08-06T21:24:13.0583475Z >>> (torch.randn(3), 3.14), 2024-08-06T21:24:13.0583690Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-08-06T21:24:13.0583935Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-08-06T21:24:13.0584279Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-08-06T21:24:13.0584425Z >>> ] 2024-08-06T21:24:13.0584566Z >>> 2024-08-06T21:24:13.0584806Z >>> for args in sample_inputs: 2024-08-06T21:24:13.0585017Z >>> torch.library.opcheck(foo, args) 2024-08-06T21:24:13.0585160Z 2024-08-06T21:24:13.0585313Z 2024-08-06T21:24:13.0585759Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0585893Z 2024-08-06T21:24:13.0586070Z warnings.warn(msg) 2024-08-06T21:24:13.0586205Z 2024-08-06T21:24:13.0586660Z --- Parse Warning: 10 / 100 --- 2024-08-06T21:24:13.0588248Z /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=1042. 2024-08-06T21:24:13.0588721Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0589378Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) 2024-08-06T21:24:13.0589518Z 2024-08-06T21:24:13.0589823Z Loads an object saved with :func:`torch.save` from a file. 2024-08-06T21:24:13.0589973Z 2024-08-06T21:24:13.0590391Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-08-06T21:24:13.0590799Z which underlie tensors, specially. They are first deserialized on the 2024-08-06T21:24:13.0591194Z CPU and are then moved to the device they were saved from. If this fails 2024-08-06T21:24:13.0591606Z (e.g. because the run time system doesn't have certain devices), an exception 2024-08-06T21:24:13.0592024Z is raised. However, storages can be dynamically remapped to an alternative 2024-08-06T21:24:13.0592318Z set of devices using the :attr:`map_location` argument. 2024-08-06T21:24:13.0592460Z 2024-08-06T21:24:13.0592908Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-08-06T21:24:13.0593305Z storage with two arguments: storage and location. The storage argument 2024-08-06T21:24:13.0593715Z will be the initial deserialization of the storage, residing on the CPU. 2024-08-06T21:24:13.0594178Z Each serialized storage has a location tag associated with it which 2024-08-06T21:24:13.0594550Z identifies the device it was saved from, and this tag is the second 2024-08-06T21:24:13.0595000Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-08-06T21:24:13.0595403Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-08-06T21:24:13.0595752Z :attr:`map_location` should return either ``None`` or a storage. If 2024-08-06T21:24:13.0596205Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-08-06T21:24:13.0596654Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-08-06T21:24:13.0597090Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-08-06T21:24:13.0597251Z 2024-08-06T21:24:13.0597670Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-08-06T21:24:13.0598081Z a device tag, it indicates the location where all tensors should be loaded. 2024-08-06T21:24:13.0598233Z 2024-08-06T21:24:13.0598692Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-08-06T21:24:13.0599057Z appearing in the file (keys), to ones that specify where to put the 2024-08-06T21:24:13.0599235Z storages (values). 2024-08-06T21:24:13.0599372Z 2024-08-06T21:24:13.0599764Z User extensions can register their own location tags and tagging and 2024-08-06T21:24:13.0600251Z deserialization methods using :func:`torch.serialization.register_package`. 2024-08-06T21:24:13.0600394Z 2024-08-06T21:24:13.0600537Z Args: 2024-08-06T21:24:13.0601123Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-08-06T21:24:13.0601459Z or a string or os.PathLike object containing a file name 2024-08-06T21:24:13.0602043Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-08-06T21:24:13.0602210Z locations 2024-08-06T21:24:13.0602612Z pickle_module: module used for unpickling metadata and objects (has to 2024-08-06T21:24:13.0602913Z match the :attr:`pickle_module` used to serialize file) 2024-08-06T21:24:13.0603289Z weights_only: Indicates whether unpickler should be restricted to 2024-08-06T21:24:13.0603562Z loading only tensors, primitive types, dictionaries 2024-08-06T21:24:13.0603959Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-08-06T21:24:13.0604598Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-08-06T21:24:13.0605215Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-08-06T21:24:13.0605857Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-08-06T21:24:13.0606453Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-08-06T21:24:13.0606893Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-08-06T21:24:13.0607313Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-08-06T21:24:13.0607700Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-08-06T21:24:13.0607875Z :attr:`errors=...`. 2024-08-06T21:24:13.0608009Z 2024-08-06T21:24:13.0608165Z .. warning:: 2024-08-06T21:24:13.0608545Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-08-06T21:24:13.0608887Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-08-06T21:24:13.0609440Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-08-06T21:24:13.0609874Z during unpickling. Never load data that could have come from an untrusted 2024-08-06T21:24:13.0610404Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-08-06T21:24:13.0610542Z 2024-08-06T21:24:13.0610702Z .. note:: 2024-08-06T21:24:13.0611155Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-08-06T21:24:13.0611619Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-08-06T21:24:13.0612099Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-08-06T21:24:13.0612239Z 2024-08-06T21:24:13.0612400Z .. note:: 2024-08-06T21:24:13.0612843Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-08-06T21:24:13.0613215Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-08-06T21:24:13.0613591Z when loading files saved by Python 2 in Python 3. If this default 2024-08-06T21:24:13.0614047Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-08-06T21:24:13.0614467Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-08-06T21:24:13.0614900Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-08-06T21:24:13.0615304Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-08-06T21:24:13.0615453Z 2024-08-06T21:24:13.0615596Z Example: 2024-08-06T21:24:13.0615812Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-08-06T21:24:13.0616095Z >>> torch.load("tensors.pt", weights_only=True) 2024-08-06T21:24:13.0616285Z # Load all tensors onto the CPU 2024-08-06T21:24:13.0616724Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-08-06T21:24:13.0616985Z # Load all tensors onto the CPU, using a function 2024-08-06T21:24:13.0617142Z >>> torch.load( 2024-08-06T21:24:13.0617568Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-08-06T21:24:13.0617724Z ... ) 2024-08-06T21:24:13.0617906Z # Load all tensors onto GPU 1 2024-08-06T21:24:13.0618057Z >>> torch.load( 2024-08-06T21:24:13.0618230Z ... "tensors.pt", 2024-08-06T21:24:13.0618512Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-08-06T21:24:13.0618718Z ... weights_only=True, 2024-08-06T21:24:13.0618921Z ... ) # type: ignore[attr-defined] 2024-08-06T21:24:13.0619116Z # Map tensors from GPU 1 to GPU 0 2024-08-06T21:24:13.0619548Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-08-06T21:24:13.0619762Z # Load tensor from io.BytesIO object 2024-08-06T21:24:13.0620202Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-08-06T21:24:13.0620415Z >>> with open("tensor.pt", "rb") as f: 2024-08-06T21:24:13.0620603Z ... buffer = io.BytesIO(f.read()) 2024-08-06T21:24:13.0620813Z >>> torch.load(buffer, weights_only=False) 2024-08-06T21:24:13.0621085Z # Load a module with 'ascii' encoding for unpickling 2024-08-06T21:24:13.0621529Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-08-06T21:24:13.0621873Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-08-06T21:24:13.0622023Z 2024-08-06T21:24:13.0622483Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0622620Z 2024-08-06T21:24:13.0622879Z warnings.warn(msg) 2024-08-06T21:24:13.0623017Z 2024-08-06T21:24:13.0623373Z --- Parse Warning: 11 / 100 --- 2024-08-06T21:24:13.0624961Z /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. 2024-08-06T21:24:13.0625404Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-06T21:24:13.0625614Z Retrieves the CUDA runtime API module. 2024-08-06T21:24:13.0625757Z 2024-08-06T21:24:13.0625899Z 2024-08-06T21:24:13.0626367Z This function initializes the CUDA runtime environment if it is not already 2024-08-06T21:24:13.0626840Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-08-06T21:24:13.0627246Z runtime API module provides access to various CUDA runtime functions. 2024-08-06T21:24:13.0627398Z 2024-08-06T21:24:13.0627546Z Args: 2024-08-06T21:24:13.0627693Z ``None`` 2024-08-06T21:24:13.0627852Z 2024-08-06T21:24:13.0627998Z Returns: 2024-08-06T21:24:13.0628243Z module: The CUDA runtime API module (_cudart). 2024-08-06T21:24:13.0628396Z 2024-08-06T21:24:13.0628540Z Raises: 2024-08-06T21:24:13.0628948Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-08-06T21:24:13.0629618Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-08-06T21:24:13.0629775Z 2024-08-06T21:24:13.0629998Z Example of CUDA operations with profiling: 2024-08-06T21:24:13.0630157Z >>> import torch 2024-08-06T21:24:13.0630393Z >>> from torch.cuda import cudart, check_error 2024-08-06T21:24:13.0630591Z >>> import os 2024-08-06T21:24:13.0630731Z >>> 2024-08-06T21:24:13.0630937Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-08-06T21:24:13.0631083Z >>> 2024-08-06T21:24:13.0631314Z >>> def perform_cuda_operations_with_streams(): 2024-08-06T21:24:13.0631519Z >>> stream = torch.cuda.Stream() 2024-08-06T21:24:13.0631718Z >>> with torch.cuda.stream(stream): 2024-08-06T21:24:13.0631929Z >>> x = torch.randn(100, 100, device='cuda') 2024-08-06T21:24:13.0632147Z >>> y = torch.randn(100, 100, device='cuda') 2024-08-06T21:24:13.0632318Z >>> z = torch.mul(x, y) 2024-08-06T21:24:13.0632477Z >>> return z 2024-08-06T21:24:13.0632620Z >>> 2024-08-06T21:24:13.0632808Z >>> torch.cuda.synchronize() 2024-08-06T21:24:13.0633077Z >>> print("====== Start nsys profiling ======") 2024-08-06T21:24:13.0633303Z >>> check_error(cudart().cudaProfilerStart()) 2024-08-06T21:24:13.0633538Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-08-06T21:24:13.0633808Z >>> result = perform_cuda_operations_with_streams() 2024-08-06T21:24:13.0634020Z >>> print("CUDA operations completed.") 2024-08-06T21:24:13.0634301Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-08-06T21:24:13.0634520Z >>> print("====== End nsys profiling ======") 2024-08-06T21:24:13.0634653Z 2024-08-06T21:24:13.0634994Z To run this example and save the profiling information, execute: 2024-08-06T21:24:13.0635645Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-06T21:24:13.0635783Z 2024-08-06T21:24:13.0636214Z This command profiles the CUDA operations in the provided script and saves 2024-08-06T21:24:13.0636570Z the profiling information to a file named `trace_name.prof`. 2024-08-06T21:24:13.0636981Z The `--profile-from-start off` option ensures that profiling starts only 2024-08-06T21:24:13.0637313Z after the `cudaProfilerStart` call in the script. 2024-08-06T21:24:13.0637705Z The `--csv` and `--print-summary` options format the profiling output as a 2024-08-06T21:24:13.0637927Z CSV file and print a summary, respectively. 2024-08-06T21:24:13.0638361Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-08-06T21:24:13.0638624Z overwrite of the output file if it already exists. 2024-08-06T21:24:13.0638767Z 2024-08-06T21:24:13.0639969Z 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)) 2024-08-06T21:24:13.0640110Z 2024-08-06T21:24:13.0640734Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-06T21:24:13.0640889Z ^ 2024-08-06T21:24:13.0641049Z warnings.warn(msg) 2024-08-06T21:24:13.0641185Z 2024-08-06T21:24:13.0641543Z --- Parse Warning: 12 / 100 --- 2024-08-06T21:24:13.0643354Z /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. 2024-08-06T21:24:13.0643841Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0643979Z 2024-08-06T21:24:13.0644382Z Append the given callback function to this ``Future``, which will be run 2024-08-06T21:24:13.0644756Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-08-06T21:24:13.0645118Z the same ``Future``, but the order in which they will be executed cannot 2024-08-06T21:24:13.0645440Z be guaranteed (to enforce a certain order consider chaining: 2024-08-06T21:24:13.0645890Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-08-06T21:24:13.0646256Z is the reference to this ``Future``. The callback function can use the 2024-08-06T21:24:13.0646610Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-08-06T21:24:13.0647018Z already completed, the given callback will be run immediately inline. 2024-08-06T21:24:13.0647155Z 2024-08-06T21:24:13.0647497Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-08-06T21:24:13.0647886Z callback might be invoked while the async kernels that are populating 2024-08-06T21:24:13.0648294Z those tensors haven't yet finished executing on the device. However, the 2024-08-06T21:24:13.0648667Z callback will be invoked with some dedicated streams set as current 2024-08-06T21:24:13.0649074Z (fetched from a global pool) which will be synchronized with those 2024-08-06T21:24:13.0649475Z kernels. Hence any operation performed by the callback on these tensors 2024-08-06T21:24:13.0649854Z will be scheduled on the device after the kernels complete. In other 2024-08-06T21:24:13.0650202Z words, as long as the callback doesn't switch streams, it can safely 2024-08-06T21:24:13.0650608Z manipulate the result without any additional synchronization. This is 2024-08-06T21:24:13.0650891Z similar to the non-blocking behavior of :meth:`wait`. 2024-08-06T21:24:13.0651027Z 2024-08-06T21:24:13.0651403Z Similarly, if the callback returns a value that contains tensors that 2024-08-06T21:24:13.0651745Z reside on a GPU, it can do so even if the kernels that are producing 2024-08-06T21:24:13.0652132Z these tensors are still running on the device, as long as the callback 2024-08-06T21:24:13.0652501Z didn't change streams during its execution. If one wants to change 2024-08-06T21:24:13.0652872Z streams, one must be careful to re-synchronize them with the original 2024-08-06T21:24:13.0653255Z streams, that is, those that were current when the callback was invoked. 2024-08-06T21:24:13.0653406Z 2024-08-06T21:24:13.0653548Z Args: 2024-08-06T21:24:13.0653988Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-08-06T21:24:13.0654184Z the only argument. 2024-08-06T21:24:13.0654318Z 2024-08-06T21:24:13.0654453Z Returns: 2024-08-06T21:24:13.0654747Z A new ``Future`` object that holds the return value of the 2024-08-06T21:24:13.0655048Z ``callback`` and will be marked as completed when the given 2024-08-06T21:24:13.0655216Z ``callback`` finishes. 2024-08-06T21:24:13.0655363Z 2024-08-06T21:24:13.0655660Z .. note:: Note that if the callback function throws, either 2024-08-06T21:24:13.0656029Z through the original future being completed with an exception and 2024-08-06T21:24:13.0656384Z calling ``fut.wait()``, or through other code in the callback, the 2024-08-06T21:24:13.0656738Z future returned by ``then`` will be marked appropriately with the 2024-08-06T21:24:13.0657098Z encountered error. However, if this callback later completes 2024-08-06T21:24:13.0657481Z additional futures, those futures are not marked as completed with 2024-08-06T21:24:13.0657835Z an error and the user is responsible for handling completion/waiting 2024-08-06T21:24:13.0658035Z on those futures independently. 2024-08-06T21:24:13.0658170Z 2024-08-06T21:24:13.0658321Z Example:: 2024-08-06T21:24:13.0658582Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-06T21:24:13.0658741Z >>> def callback(fut): 2024-08-06T21:24:13.0658971Z ... print(f"RPC return value is {fut.wait()}.") 2024-08-06T21:24:13.0659168Z >>> fut = torch.futures.Future() 2024-08-06T21:24:13.0659453Z >>> # The inserted callback will print the return value when 2024-08-06T21:24:13.0659659Z >>> # receiving the response from "worker1" 2024-08-06T21:24:13.0659848Z >>> cb_fut = fut.then(callback) 2024-08-06T21:24:13.0660044Z >>> chain_cb_fut = cb_fut.then( 2024-08-06T21:24:13.0660301Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-08-06T21:24:13.0660456Z ... ) 2024-08-06T21:24:13.0660616Z >>> fut.set_result(5) 2024-08-06T21:24:13.0660784Z RPC return value is 5. 2024-08-06T21:24:13.0660960Z Chained cb done. None 2024-08-06T21:24:13.0661093Z 2024-08-06T21:24:13.0661545Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0661697Z 2024-08-06T21:24:13.0661858Z warnings.warn(msg) 2024-08-06T21:24:13.0661994Z 2024-08-06T21:24:13.0662345Z --- Parse Warning: 13 / 100 --- 2024-08-06T21:24:13.0664019Z /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. 2024-08-06T21:24:13.0664546Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0664687Z 2024-08-06T21:24:13.0665052Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-08-06T21:24:13.0665451Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-08-06T21:24:13.0665643Z cannot be marked completed twice. 2024-08-06T21:24:13.0665785Z 2024-08-06T21:24:13.0666180Z If the result contains tensors that reside on GPUs, this method can be 2024-08-06T21:24:13.0666536Z called even if the asynchronous kernels that are populating those 2024-08-06T21:24:13.0667002Z tensors haven't yet completed running on the device, provided that the 2024-08-06T21:24:13.0667414Z streams on which those kernels were enqueued are set as the current ones 2024-08-06T21:24:13.0667789Z when this method is called. Put simply, it's safe to call this method 2024-08-06T21:24:13.0668180Z immediately after launching those kernels, without any additional 2024-08-06T21:24:13.0668577Z synchronization, as long as one doesn't change streams in between. This 2024-08-06T21:24:13.0669070Z method will record events on all the relevant current streams and will 2024-08-06T21:24:13.0669429Z use them to ensure proper scheduling for all the consumers of this 2024-08-06T21:24:13.0669577Z ``Future``. 2024-08-06T21:24:13.0669712Z 2024-08-06T21:24:13.0669864Z Args: 2024-08-06T21:24:13.0670143Z result (object): the result object of this ``Future``. 2024-08-06T21:24:13.0670272Z 2024-08-06T21:24:13.0670431Z Example:: 2024-08-06T21:24:13.0670676Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-06T21:24:13.0670833Z >>> import threading 2024-08-06T21:24:13.0670991Z >>> import time 2024-08-06T21:24:13.0671179Z >>> def slow_set_future(fut, value): 2024-08-06T21:24:13.0671340Z ... time.sleep(0.5) 2024-08-06T21:24:13.0671526Z ... fut.set_result(value) 2024-08-06T21:24:13.0671710Z >>> fut = torch.futures.Future() 2024-08-06T21:24:13.0671873Z >>> t = threading.Thread( 2024-08-06T21:24:13.0672059Z ... target=slow_set_future, 2024-08-06T21:24:13.0672240Z ... args=(fut, torch.ones(2) * 3) 2024-08-06T21:24:13.0672377Z ... ) 2024-08-06T21:24:13.0672531Z >>> t.start() 2024-08-06T21:24:13.0672688Z >>> print(fut.wait()) 2024-08-06T21:24:13.0672836Z tensor([3., 3.]) 2024-08-06T21:24:13.0672984Z >>> t.join() 2024-08-06T21:24:13.0673118Z 2024-08-06T21:24:13.0673569Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0673716Z 2024-08-06T21:24:13.0673873Z warnings.warn(msg) 2024-08-06T21:24:13.0674001Z 2024-08-06T21:24:13.0674336Z --- Parse Warning: 14 / 100 --- 2024-08-06T21:24:13.0675877Z /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=201. 2024-08-06T21:24:13.0676401Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0676674Z Return the sum of each row of the given sparse tensor. 2024-08-06T21:24:13.0676810Z 2024-08-06T21:24:13.0677210Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-08-06T21:24:13.0677548Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-08-06T21:24:13.0677916Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-08-06T21:24:13.0678187Z returns a dense tensor instead of a sparse tensor. 2024-08-06T21:24:13.0678319Z 2024-08-06T21:24:13.0678781Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-08-06T21:24:13.0679174Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-08-06T21:24:13.0679313Z 2024-08-06T21:24:13.0679698Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-08-06T21:24:13.0680129Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-08-06T21:24:13.0680263Z 2024-08-06T21:24:13.0680418Z Args: 2024-08-06T21:24:13.0680627Z input (Tensor): the input sparse tensor 2024-08-06T21:24:13.0681109Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-08-06T21:24:13.0681279Z over all dims. 2024-08-06T21:24:13.0681732Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-08-06T21:24:13.0681924Z Default: dtype of :attr:`input`. 2024-08-06T21:24:13.0682076Z 2024-08-06T21:24:13.0682226Z Example:: 2024-08-06T21:24:13.0682366Z 2024-08-06T21:24:13.0682524Z >>> nnz = 3 2024-08-06T21:24:13.0682678Z >>> dims = [5, 5, 2, 3] 2024-08-06T21:24:13.0682944Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-08-06T21:24:13.0683341Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-08-06T21:24:13.0683544Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-08-06T21:24:13.0683716Z >>> size = torch.Size(dims) 2024-08-06T21:24:13.0683965Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-06T21:24:13.0684172Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-08-06T21:24:13.0684315Z >>> S 2024-08-06T21:24:13.0684519Z tensor(indices=tensor([[2, 0, 3], 2024-08-06T21:24:13.0684682Z [2, 4, 1]]), 2024-08-06T21:24:13.0684894Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-08-06T21:24:13.0685090Z [ 0.3411, 0.0918, -0.2312]], 2024-08-06T21:24:13.0685230Z 2024-08-06T21:24:13.0685424Z [[ 0.5348, 0.0634, -2.0494], 2024-08-06T21:24:13.0685611Z [-0.7125, -1.0646, 2.1844]], 2024-08-06T21:24:13.0685754Z 2024-08-06T21:24:13.0685946Z [[ 0.1276, 0.1874, -0.6334], 2024-08-06T21:24:13.0686132Z [-1.9682, -0.5340, 0.7483]]]), 2024-08-06T21:24:13.0686369Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-08-06T21:24:13.0686523Z 2024-08-06T21:24:13.0686856Z # when sum over only part of sparse_dims, return a sparse tensor 2024-08-06T21:24:13.0687042Z >>> torch.sparse.sum(S, [1, 3]) 2024-08-06T21:24:13.0687245Z tensor(indices=tensor([[0, 2, 3]]), 2024-08-06T21:24:13.0687436Z values=tensor([[-1.4512, 0.4073], 2024-08-06T21:24:13.0687610Z [-0.8901, 0.2017], 2024-08-06T21:24:13.0687800Z [-0.3183, -1.7539]]), 2024-08-06T21:24:13.0688057Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-08-06T21:24:13.0688196Z 2024-08-06T21:24:13.0688475Z # when sum over all sparse dim, return a dense tensor 2024-08-06T21:24:13.0688647Z # with summed dims squeezed 2024-08-06T21:24:13.0688835Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-08-06T21:24:13.0689017Z tensor([-2.6596, -1.1450]) 2024-08-06T21:24:13.0689162Z 2024-08-06T21:24:13.0689634Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0689773Z 2024-08-06T21:24:13.0689944Z warnings.warn(msg) 2024-08-06T21:24:13.0690098Z 2024-08-06T21:24:13.0690424Z --- Parse Warning: 15 / 100 --- 2024-08-06T21:24:13.0692202Z /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=2720. 2024-08-06T21:24:13.0692729Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0692868Z 2024-08-06T21:24:13.0693270Z Gathers picklable objects from the whole group in a single process. 2024-08-06T21:24:13.0693422Z 2024-08-06T21:24:13.0693839Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-08-06T21:24:13.0694093Z object must be picklable in order to be gathered. 2024-08-06T21:24:13.0694251Z 2024-08-06T21:24:13.0694393Z Args: 2024-08-06T21:24:13.0694610Z obj (Any): Input object. Must be picklable. 2024-08-06T21:24:13.0694994Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-08-06T21:24:13.0695316Z should be correctly sized as the size of the group for this 2024-08-06T21:24:13.0695715Z collective and will contain the output. Must be ``None`` on non-dst 2024-08-06T21:24:13.0695896Z ranks. (default is ``None``) 2024-08-06T21:24:13.0696595Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-08-06T21:24:13.0697085Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-08-06T21:24:13.0697418Z the default process group will be used. Default is ``None``. 2024-08-06T21:24:13.0697556Z 2024-08-06T21:24:13.0697720Z Returns: 2024-08-06T21:24:13.0698030Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-08-06T21:24:13.0698203Z output of the collective. 2024-08-06T21:24:13.0698355Z 2024-08-06T21:24:13.0698726Z .. note:: Note that this API differs slightly from the gather collective 2024-08-06T21:24:13.0699106Z since it does not provide an async_op handle and thus will be a blocking 2024-08-06T21:24:13.0699262Z call. 2024-08-06T21:24:13.0699398Z 2024-08-06T21:24:13.0699802Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-08-06T21:24:13.0700192Z of objects must be moved to the GPU device before communication takes 2024-08-06T21:24:13.0700442Z place. In this case, the device used is given by 2024-08-06T21:24:13.0700839Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-08-06T21:24:13.0701199Z ensure that this is set so that each rank has an individual GPU, via 2024-08-06T21:24:13.0701376Z ``torch.cuda.set_device()``. 2024-08-06T21:24:13.0701524Z 2024-08-06T21:24:13.0701668Z .. warning:: 2024-08-06T21:24:13.0702010Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-08-06T21:24:13.0702408Z known to be insecure. It is possible to construct malicious pickle data 2024-08-06T21:24:13.0702790Z which will execute arbitrary code during unpickling. Only call this 2024-08-06T21:24:13.0702973Z function with data you trust. 2024-08-06T21:24:13.0703122Z 2024-08-06T21:24:13.0703268Z .. warning:: 2024-08-06T21:24:13.0703679Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-08-06T21:24:13.0704097Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-08-06T21:24:13.0704390Z pickled. Please consider using :func:`gather` instead. 2024-08-06T21:24:13.0704526Z 2024-08-06T21:24:13.0704685Z Example:: 2024-08-06T21:24:13.0704913Z >>> # xdoctest: +SKIP("need process group init") 2024-08-06T21:24:13.0705219Z >>> # Note: Process group initialization omitted on each rank. 2024-08-06T21:24:13.0705433Z >>> import torch.distributed as dist 2024-08-06T21:24:13.0705608Z >>> # Assumes world_size of 3. 2024-08-06T21:24:13.0705921Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-08-06T21:24:13.0706123Z >>> output = [None for _ in gather_objects] 2024-08-06T21:24:13.0706320Z >>> dist.gather_object( 2024-08-06T21:24:13.0706532Z ... gather_objects[dist.get_rank()], 2024-08-06T21:24:13.0706805Z ... output if dist.get_rank() == 0 else None, 2024-08-06T21:24:13.0706951Z ... dst=0 2024-08-06T21:24:13.0707110Z ... ) 2024-08-06T21:24:13.0707258Z >>> # On rank 0 2024-08-06T21:24:13.0707402Z >>> output 2024-08-06T21:24:13.0707565Z ['foo', 12, {1: 2}] 2024-08-06T21:24:13.0707707Z 2024-08-06T21:24:13.0708165Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0708316Z 2024-08-06T21:24:13.0708477Z warnings.warn(msg) 2024-08-06T21:24:13.0708612Z 2024-08-06T21:24:13.0708947Z --- Parse Warning: 16 / 100 --- 2024-08-06T21:24:13.0710526Z /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. 2024-08-06T21:24:13.0710999Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0711158Z 2024-08-06T21:24:13.0711360Z Module ``torch.distributed.launch``. 2024-08-06T21:24:13.0711570Z 2024-08-06T21:24:13.0712027Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-08-06T21:24:13.0712292Z training processes on each of the training nodes. 2024-08-06T21:24:13.0712445Z 2024-08-06T21:24:13.0712596Z .. warning:: 2024-08-06T21:24:13.0712734Z 2024-08-06T21:24:13.0713199Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-08-06T21:24:13.0713335Z 2024-08-06T21:24:13.0713763Z The utility can be used for single-node distributed training, in which one or 2024-08-06T21:24:13.0714194Z more processes per node will be spawned. The utility can be used for either 2024-08-06T21:24:13.0714581Z CPU training or GPU training. If the utility is used for GPU training, 2024-08-06T21:24:13.0715021Z each distributed process will be operating on a single GPU. This can achieve 2024-08-06T21:24:13.0715453Z well-improved single-node training performance. It can also be used in 2024-08-06T21:24:13.0715937Z multi-node distributed training, by spawning up multiple processes on each node 2024-08-06T21:24:13.0716355Z for well-improved multi-node distributed training performance as well. 2024-08-06T21:24:13.0716781Z This will especially be beneficial for systems with multiple Infiniband 2024-08-06T21:24:13.0717236Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-08-06T21:24:13.0717431Z aggregated communication bandwidth. 2024-08-06T21:24:13.0717585Z 2024-08-06T21:24:13.0718001Z In both cases of single-node distributed training or multi-node distributed 2024-08-06T21:24:13.0718432Z training, this utility will launch the given number of processes per node 2024-08-06T21:24:13.0718828Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-08-06T21:24:13.0719255Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-08-06T21:24:13.0719626Z and each process will be operating on a single GPU from *GPU 0 to 2024-08-06T21:24:13.0719802Z GPU (nproc_per_node - 1)*. 2024-08-06T21:24:13.0719941Z 2024-08-06T21:24:13.0720129Z **How to use this module:** 2024-08-06T21:24:13.0720263Z 2024-08-06T21:24:13.0720520Z 1. Single-Node multi-process distributed training 2024-08-06T21:24:13.0720673Z 2024-08-06T21:24:13.0720821Z :: 2024-08-06T21:24:13.0720951Z 2024-08-06T21:24:13.0721378Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-06T21:24:13.0721710Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-08-06T21:24:13.0721918Z arguments of your training script) 2024-08-06T21:24:13.0722106Z 2024-08-06T21:24:13.0722468Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-08-06T21:24:13.0722608Z 2024-08-06T21:24:13.0722753Z 2024-08-06T21:24:13.0722996Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-08-06T21:24:13.0723135Z 2024-08-06T21:24:13.0723288Z :: 2024-08-06T21:24:13.0723425Z 2024-08-06T21:24:13.0723838Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-06T21:24:13.0724120Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-08-06T21:24:13.0724479Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-06T21:24:13.0724742Z and all other arguments of your training script) 2024-08-06T21:24:13.0724883Z 2024-08-06T21:24:13.0725022Z Node 2: 2024-08-06T21:24:13.0725172Z 2024-08-06T21:24:13.0725309Z :: 2024-08-06T21:24:13.0725446Z 2024-08-06T21:24:13.0725867Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-06T21:24:13.0726130Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-08-06T21:24:13.0726490Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-06T21:24:13.0726815Z and all other arguments of your training script) 2024-08-06T21:24:13.0726955Z 2024-08-06T21:24:13.0727233Z 3. To look up what optional arguments this module offers: 2024-08-06T21:24:13.0727381Z 2024-08-06T21:24:13.0727515Z :: 2024-08-06T21:24:13.0727654Z 2024-08-06T21:24:13.0727895Z python -m torch.distributed.launch --help 2024-08-06T21:24:13.0728032Z 2024-08-06T21:24:13.0728170Z 2024-08-06T21:24:13.0728351Z **Important Notices:** 2024-08-06T21:24:13.0728485Z 2024-08-06T21:24:13.0728811Z 1. This utility and multi-process distributed (single-node or 2024-08-06T21:24:13.0729267Z multi-node) GPU training currently only achieves the best performance using 2024-08-06T21:24:13.0729721Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-08-06T21:24:13.0729888Z use for GPU training. 2024-08-06T21:24:13.0730044Z 2024-08-06T21:24:13.0730425Z 2. In your training program, you must parse the command-line argument: 2024-08-06T21:24:13.0730836Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-08-06T21:24:13.0731262Z If your training program uses GPUs, you should ensure that your code only 2024-08-06T21:24:13.0731597Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-08-06T21:24:13.0731745Z 2024-08-06T21:24:13.0731930Z Parsing the local_rank argument 2024-08-06T21:24:13.0732070Z 2024-08-06T21:24:13.0732225Z :: 2024-08-06T21:24:13.0732364Z 2024-08-06T21:24:13.0732528Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.0732698Z >>> import argparse 2024-08-06T21:24:13.0732907Z >>> parser = argparse.ArgumentParser() 2024-08-06T21:24:13.0733246Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-08-06T21:24:13.0733499Z >>> args = parser.parse_args() 2024-08-06T21:24:13.0733639Z 2024-08-06T21:24:13.0733850Z Set your device to local rank using either 2024-08-06T21:24:13.0734003Z 2024-08-06T21:24:13.0734144Z :: 2024-08-06T21:24:13.0734280Z 2024-08-06T21:24:13.0734642Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-08-06T21:24:13.0734792Z 2024-08-06T21:24:13.0734927Z or 2024-08-06T21:24:13.0735075Z 2024-08-06T21:24:13.0735216Z :: 2024-08-06T21:24:13.0735350Z 2024-08-06T21:24:13.0735588Z >>> with torch.cuda.device(args.local_rank): 2024-08-06T21:24:13.0735750Z >>> # your code to run 2024-08-06T21:24:13.0735896Z >>> ... 2024-08-06T21:24:13.0736052Z 2024-08-06T21:24:13.0736226Z .. versionchanged:: 2.0.0 2024-08-06T21:24:13.0736363Z 2024-08-06T21:24:13.0736846Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-08-06T21:24:13.0737266Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-08-06T21:24:13.0737504Z previously used underscored ``--local_rank``. 2024-08-06T21:24:13.0737667Z 2024-08-06T21:24:13.0738091Z For backward compatibility, it may be necessary for users to handle both 2024-08-06T21:24:13.0738574Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-08-06T21:24:13.0738960Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-08-06T21:24:13.0739409Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-08-06T21:24:13.0739827Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-08-06T21:24:13.0740084Z including ``"--local-rank"`` should be sufficient. 2024-08-06T21:24:13.0740223Z 2024-08-06T21:24:13.0740653Z 3. In your training program, you are supposed to call the following function 2024-08-06T21:24:13.0741081Z at the beginning to start the distributed backend. It is strongly recommended 2024-08-06T21:24:13.0741528Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-08-06T21:24:13.0741870Z but ``env://`` is the one that is officially supported by this module. 2024-08-06T21:24:13.0742012Z 2024-08-06T21:24:13.0742151Z :: 2024-08-06T21:24:13.0742305Z 2024-08-06T21:24:13.0742796Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-08-06T21:24:13.0743016Z >>> init_method='env://') 2024-08-06T21:24:13.0743166Z 2024-08-06T21:24:13.0743590Z 4. In your training program, you can either use regular distributed functions 2024-08-06T21:24:13.0744003Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-08-06T21:24:13.0744395Z training program uses GPUs for training and you would like to use 2024-08-06T21:24:13.0744718Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-08-06T21:24:13.0744914Z here is how to configure it. 2024-08-06T21:24:13.0745050Z 2024-08-06T21:24:13.0745193Z :: 2024-08-06T21:24:13.0745343Z 2024-08-06T21:24:13.0745680Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-08-06T21:24:13.0745910Z >>> device_ids=[args.local_rank], 2024-08-06T21:24:13.0746158Z >>> output_device=args.local_rank) 2024-08-06T21:24:13.0746303Z 2024-08-06T21:24:13.0746797Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-08-06T21:24:13.0747228Z that your code will be operating on. This is generally the local rank of the 2024-08-06T21:24:13.0747652Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-08-06T21:24:13.0748037Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-08-06T21:24:13.0748187Z utility 2024-08-06T21:24:13.0748398Z 2024-08-06T21:24:13.0748830Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-08-06T21:24:13.0749219Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-08-06T21:24:13.0749601Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-08-06T21:24:13.0749955Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-08-06T21:24:13.0750248Z will not pass ``--local-rank`` when you specify this flag. 2024-08-06T21:24:13.0750384Z 2024-08-06T21:24:13.0750544Z .. warning:: 2024-08-06T21:24:13.0750681Z 2024-08-06T21:24:13.0751031Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-08-06T21:24:13.0751368Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-08-06T21:24:13.0751619Z write to a networked filesystem. See 2024-08-06T21:24:13.0751995Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-08-06T21:24:13.0752286Z how things can go wrong if you don't do this correctly. 2024-08-06T21:24:13.0752425Z 2024-08-06T21:24:13.0752570Z 2024-08-06T21:24:13.0752716Z 2024-08-06T21:24:13.0752848Z 2024-08-06T21:24:13.0753297Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0753446Z 2024-08-06T21:24:13.0753610Z warnings.warn(msg) 2024-08-06T21:24:13.0753747Z 2024-08-06T21:24:13.0754117Z --- Parse Warning: 17 / 100 --- 2024-08-06T21:24:13.0755978Z /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. 2024-08-06T21:24:13.0756464Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0756602Z 2024-08-06T21:24:13.0757036Z DistributedOptimizer takes remote references to parameters scattered 2024-08-06T21:24:13.0757561Z across workers and applies the given optimizer locally for each parameter. 2024-08-06T21:24:13.0757697Z 2024-08-06T21:24:13.0758110Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-08-06T21:24:13.0758382Z to retrieve the gradients for specific parameters. 2024-08-06T21:24:13.0758520Z 2024-08-06T21:24:13.0758684Z Concurrent calls to 2024-08-06T21:24:13.0759049Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-08-06T21:24:13.0759290Z either from the same or different clients, will 2024-08-06T21:24:13.0759682Z be serialized on each worker -- as each worker's optimizer can only work 2024-08-06T21:24:13.0760058Z on one set of gradients at a time. However, there is no guarantee that 2024-08-06T21:24:13.0760494Z the full forward-backward-optimizer sequence will execute for one client 2024-08-06T21:24:13.0760878Z at a time. This means that the gradients being applied may not correspond 2024-08-06T21:24:13.0761297Z to the latest forward pass executed on a given worker. Also, there is no 2024-08-06T21:24:13.0761487Z guaranteed ordering across workers. 2024-08-06T21:24:13.0761638Z 2024-08-06T21:24:13.0762111Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-08-06T21:24:13.0762513Z by default, so that optimizer updates are not blocked by the Python Global 2024-08-06T21:24:13.0762959Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-08-06T21:24:13.0763384Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-08-06T21:24:13.0763829Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-08-06T21:24:13.0764028Z for your own custom optimizers. 2024-08-06T21:24:13.0764167Z 2024-08-06T21:24:13.0764306Z Args: 2024-08-06T21:24:13.0764665Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-08-06T21:24:13.0764883Z instantiate on each worker. 2024-08-06T21:24:13.0765253Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-08-06T21:24:13.0765419Z to optimize. 2024-08-06T21:24:13.0765799Z args: arguments to pass to the optimizer constructor on each worker. 2024-08-06T21:24:13.0766190Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-08-06T21:24:13.0766342Z 2024-08-06T21:24:13.0766499Z Example:: 2024-08-06T21:24:13.0766691Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:13.0766987Z >>> import torch.distributed.autograd as dist_autograd 2024-08-06T21:24:13.0767198Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:13.0767387Z >>> from torch import optim 2024-08-06T21:24:13.0767745Z >>> from torch.distributed.optim import DistributedOptimizer 2024-08-06T21:24:13.0767892Z >>> 2024-08-06T21:24:13.0768132Z >>> with dist_autograd.context() as context_id: 2024-08-06T21:24:13.0768293Z >>> # Forward pass. 2024-08-06T21:24:13.0768647Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-08-06T21:24:13.0769007Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-08-06T21:24:13.0769214Z >>> loss = rref1.to_here() + rref2.to_here() 2024-08-06T21:24:13.0769358Z >>> 2024-08-06T21:24:13.0769531Z >>> # Backward pass. 2024-08-06T21:24:13.0769788Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-08-06T21:24:13.0769930Z >>> 2024-08-06T21:24:13.0770089Z >>> # Optimizer. 2024-08-06T21:24:13.0770300Z >>> dist_optim = DistributedOptimizer( 2024-08-06T21:24:13.0770455Z >>> optim.SGD, 2024-08-06T21:24:13.0770634Z >>> [rref1, rref2], 2024-08-06T21:24:13.0770785Z >>> lr=0.05, 2024-08-06T21:24:13.0770928Z >>> ) 2024-08-06T21:24:13.0771126Z >>> dist_optim.step(context_id) 2024-08-06T21:24:13.0771266Z 2024-08-06T21:24:13.0771589Z __ https://github.com/pytorch/tutorials/pull/1465 2024-08-06T21:24:13.0771741Z 2024-08-06T21:24:13.0772194Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0772334Z 2024-08-06T21:24:13.0772506Z warnings.warn(msg) 2024-08-06T21:24:13.0772642Z 2024-08-06T21:24:13.0772961Z --- Parse Warning: 18 / 100 --- 2024-08-06T21:24:13.0774974Z /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. 2024-08-06T21:24:13.0775454Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0775615Z 2024-08-06T21:24:13.0776313Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-08-06T21:24:13.0776576Z This optimizer runs local optimizer at every step. 2024-08-06T21:24:13.0777192Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-08-06T21:24:13.0777327Z 2024-08-06T21:24:13.0777462Z Args: 2024-08-06T21:24:13.0777651Z optim: The local optimizer. 2024-08-06T21:24:13.0778032Z averager: A model averager instance to run post-localSGD algorithm. 2024-08-06T21:24:13.0778171Z 2024-08-06T21:24:13.0778330Z Example:: 2024-08-06T21:24:13.0778469Z 2024-08-06T21:24:13.0778679Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.0778845Z >>> import torch 2024-08-06T21:24:13.0779044Z >>> import torch.distributed as dist 2024-08-06T21:24:13.0779527Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-06T21:24:13.0779706Z >>> import torch.nn as nn 2024-08-06T21:24:13.0780082Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-08-06T21:24:13.0780590Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-06T21:24:13.0780764Z >>> PostLocalSGDState, 2024-08-06T21:24:13.0780930Z >>> post_localSGD_hook, 2024-08-06T21:24:13.0781078Z >>> ) 2024-08-06T21:24:13.0781220Z >>> 2024-08-06T21:24:13.0781475Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-06T21:24:13.0781727Z >>> module, device_ids=[rank], output_device=rank 2024-08-06T21:24:13.0781862Z >>> ) 2024-08-06T21:24:13.0782005Z >>> 2024-08-06T21:24:13.0782260Z >>> # Register a post-localSGD communication hook. 2024-08-06T21:24:13.0782798Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-06T21:24:13.0783112Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-06T21:24:13.0783270Z >>> 2024-08-06T21:24:13.0783622Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-08-06T21:24:13.0784064Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-08-06T21:24:13.0784358Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-06T21:24:13.0784732Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-08-06T21:24:13.0784939Z >>> opt = PostLocalSGDOptimizer( 2024-08-06T21:24:13.0785107Z >>> optim=local_optim, 2024-08-06T21:24:13.0785545Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-06T21:24:13.0785698Z >>> ) 2024-08-06T21:24:13.0785836Z >>> 2024-08-06T21:24:13.0786230Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-08-06T21:24:13.0786868Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-08-06T21:24:13.0787645Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-08-06T21:24:13.0787823Z >>> for step in range(0, 200): 2024-08-06T21:24:13.0788000Z >>> opt.zero_grad() 2024-08-06T21:24:13.0788185Z >>> loss = loss_fn(output, labels) 2024-08-06T21:24:13.0788350Z >>> loss.backward() 2024-08-06T21:24:13.0788520Z >>> opt.step() 2024-08-06T21:24:13.0788658Z 2024-08-06T21:24:13.0789116Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0789269Z 2024-08-06T21:24:13.0789431Z warnings.warn(msg) 2024-08-06T21:24:13.0789570Z 2024-08-06T21:24:13.0789911Z --- Parse Warning: 19 / 100 --- 2024-08-06T21:24:13.0791962Z /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. 2024-08-06T21:24:13.0792455Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0792595Z 2024-08-06T21:24:13.0793329Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-08-06T21:24:13.0793487Z 2024-08-06T21:24:13.0793702Z The sharing is done as described by ZeRO_. 2024-08-06T21:24:13.0793836Z 2024-08-06T21:24:13.0794103Z The local optimizer instance in each rank is only 2024-08-06T21:24:13.0794541Z responsible for updating approximately ``1 / world_size`` parameters and 2024-08-06T21:24:13.0794898Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-08-06T21:24:13.0795347Z parameters are updated locally, each rank will broadcast its parameters to 2024-08-06T21:24:13.0795664Z all other peers to keep all model replicas in the same state. 2024-08-06T21:24:13.0796051Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-08-06T21:24:13.0796526Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-08-06T21:24:13.0796692Z memory consumption. 2024-08-06T21:24:13.0796839Z 2024-08-06T21:24:13.0797310Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-08-06T21:24:13.0797728Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-08-06T21:24:13.0798170Z not divided among ranks. The partition is arbitrary and might not match the 2024-08-06T21:24:13.0798391Z the parameter registration or usage order. 2024-08-06T21:24:13.0798530Z 2024-08-06T21:24:13.0798692Z Arguments: 2024-08-06T21:24:13.0799021Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-08-06T21:24:13.0799380Z or :class:`dict` s giving all parameters, which will be sharded 2024-08-06T21:24:13.0799548Z across ranks. 2024-08-06T21:24:13.0799684Z 2024-08-06T21:24:13.0799835Z Keyword Args: 2024-08-06T21:24:13.0800240Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-08-06T21:24:13.0800388Z optimizer. 2024-08-06T21:24:13.0800752Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-08-06T21:24:13.0801109Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-08-06T21:24:13.0801360Z :meth:`torch.distributed.init_process_group`). 2024-08-06T21:24:13.0801763Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-08-06T21:24:13.0802157Z packed into buckets to speed up communication, and ``param.data`` 2024-08-06T21:24:13.0802510Z fields point to bucket views at different offsets; if ``False``, 2024-08-06T21:24:13.0802887Z each individual parameter is communicated separately, and each 2024-08-06T21:24:13.0803143Z ``params.data`` stays intact (default: ``False``). 2024-08-06T21:24:13.0803485Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-08-06T21:24:13.0803912Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-08-06T21:24:13.0804297Z synchronization; this requires (1) either a functional optimizer 2024-08-06T21:24:13.0804617Z for the ``optimizer_class`` argument or one with a functional 2024-08-06T21:24:13.0804934Z equivalent and (2) registering a DDP communication hook 2024-08-06T21:24:13.0805286Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-08-06T21:24:13.0805575Z parameters are packed into buckets matching those in 2024-08-06T21:24:13.0805853Z :class:`DistributedDataParallel`, meaning that the 2024-08-06T21:24:13.0806109Z ``parameters_as_bucket_view`` argument is ignored. 2024-08-06T21:24:13.0806444Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-08-06T21:24:13.0806602Z (per normal). 2024-08-06T21:24:13.0806769Z (default: ``False``) 2024-08-06T21:24:13.0807156Z **defaults: any trailing arguments, which are forwarded to the local 2024-08-06T21:24:13.0807305Z optimizer. 2024-08-06T21:24:13.0807442Z 2024-08-06T21:24:13.0807604Z Example:: 2024-08-06T21:24:13.0807741Z 2024-08-06T21:24:13.0807899Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.0808078Z >>> import torch.nn as nn 2024-08-06T21:24:13.0808428Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-08-06T21:24:13.0808774Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-08-06T21:24:13.0809180Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-08-06T21:24:13.0809382Z >>> ddp = DDP(model, device_ids=[rank]) 2024-08-06T21:24:13.0809580Z >>> opt = ZeroRedundancyOptimizer( 2024-08-06T21:24:13.0809801Z >>> ddp.parameters(), 2024-08-06T21:24:13.0810005Z >>> optimizer_class=torch.optim.Adam, 2024-08-06T21:24:13.0810156Z >>> lr=0.01 2024-08-06T21:24:13.0810307Z >>> ) 2024-08-06T21:24:13.0810488Z >>> ddp(inputs).sum().backward() 2024-08-06T21:24:13.0810637Z >>> opt.step() 2024-08-06T21:24:13.0810789Z 2024-08-06T21:24:13.0810937Z .. warning:: 2024-08-06T21:24:13.0811302Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-08-06T21:24:13.0811560Z passed-in parameters are the same dense type. 2024-08-06T21:24:13.0811699Z 2024-08-06T21:24:13.0811851Z .. warning:: 2024-08-06T21:24:13.0812239Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-08-06T21:24:13.0812587Z the way that overlapping :class:`DistributedDataParallel` with 2024-08-06T21:24:13.0813051Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-08-06T21:24:13.0813434Z two or three training iterations do not perform parameter updates in 2024-08-06T21:24:13.0813776Z the optimizer step, depending on if ``static_graph=False`` or 2024-08-06T21:24:13.0814110Z ``static_graph=True``, respectively. This is because it needs 2024-08-06T21:24:13.0814434Z information about the gradient bucketing strategy used by 2024-08-06T21:24:13.0814825Z :class:`DistributedDataParallel`, which is not finalized until the 2024-08-06T21:24:13.0815183Z second forward pass if ``static_graph=False`` or until the third 2024-08-06T21:24:13.0815551Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-08-06T21:24:13.0815732Z is to prepend dummy inputs. 2024-08-06T21:24:13.0815886Z 2024-08-06T21:24:13.0816339Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-08-06T21:24:13.0816477Z 2024-08-06T21:24:13.0816706Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-08-06T21:24:13.0816847Z 2024-08-06T21:24:13.0816982Z 2024-08-06T21:24:13.0817448Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0817652Z 2024-08-06T21:24:13.0817829Z warnings.warn(msg) 2024-08-06T21:24:13.0817965Z 2024-08-06T21:24:13.0818295Z --- Parse Warning: 20 / 100 --- 2024-08-06T21:24:13.0820262Z /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. 2024-08-06T21:24:13.0820735Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0820874Z 2024-08-06T21:24:13.0821313Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-08-06T21:24:13.0821565Z Needs to be called on all ranks in an SPMD fashion. 2024-08-06T21:24:13.0821699Z 2024-08-06T21:24:13.0821861Z Args: 2024-08-06T21:24:13.0822350Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-08-06T21:24:13.0822650Z of shards that represent the local shards on this rank. 2024-08-06T21:24:13.0823061Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-08-06T21:24:13.0823258Z shape of the overall sharded tensor. 2024-08-06T21:24:13.0823413Z 2024-08-06T21:24:13.0823570Z Keyword args: 2024-08-06T21:24:13.0824047Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-08-06T21:24:13.0824274Z the default process group will be used. 2024-08-06T21:24:13.0824572Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-06T21:24:13.0824933Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-06T21:24:13.0825286Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-06T21:24:13.0825486Z Default: ``False``. 2024-08-06T21:24:13.0825627Z 2024-08-06T21:24:13.0825788Z Returns: 2024-08-06T21:24:13.0826052Z A :class:`ShardedTensor` object handle on this rank 2024-08-06T21:24:13.0826190Z 2024-08-06T21:24:13.0826336Z 2024-08-06T21:24:13.0826483Z Examples: 2024-08-06T21:24:13.0826994Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-08-06T21:24:13.0827327Z each shard have a (5, 5) local tensor, we can do it like below: 2024-08-06T21:24:13.0827464Z 2024-08-06T21:24:13.0827607Z on rank 0: 2024-08-06T21:24:13.0827821Z >>> # xdoctest: +SKIP("not distributed") 2024-08-06T21:24:13.0828027Z >>> local_shard_metadata = ShardMetadata( 2024-08-06T21:24:13.0828198Z >>> shard_offsets=[0, 0], 2024-08-06T21:24:13.0828421Z >>> shard_lengths=[5, 5], 2024-08-06T21:24:13.0828601Z >>> placement="rank:0/cuda:0" 2024-08-06T21:24:13.0828747Z >>> ) 2024-08-06T21:24:13.0829102Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-06T21:24:13.0829448Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-06T21:24:13.0829599Z 2024-08-06T21:24:13.0829744Z on rank 1: 2024-08-06T21:24:13.0829943Z >>> # xdoctest: +SKIP("not distributed") 2024-08-06T21:24:13.0830156Z >>> local_shard_metadata = ShardMetadata( 2024-08-06T21:24:13.0830322Z >>> shard_offsets=[5, 0], 2024-08-06T21:24:13.0830491Z >>> shard_lengths=[5, 5], 2024-08-06T21:24:13.0830685Z >>> placement="rank:1/cuda:1" 2024-08-06T21:24:13.0830822Z >>> ) 2024-08-06T21:24:13.0831154Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-06T21:24:13.0831502Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-06T21:24:13.0831643Z 2024-08-06T21:24:13.0832091Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0832249Z 2024-08-06T21:24:13.0832410Z warnings.warn(msg) 2024-08-06T21:24:13.0832606Z 2024-08-06T21:24:13.0832946Z --- Parse Warning: 21 / 100 --- 2024-08-06T21:24:13.0834983Z /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. 2024-08-06T21:24:13.0835448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0835598Z 2024-08-06T21:24:13.0836063Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-08-06T21:24:13.0836264Z size and sharding spec on each rank. 2024-08-06T21:24:13.0836404Z 2024-08-06T21:24:13.0836542Z Args: 2024-08-06T21:24:13.0836950Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-08-06T21:24:13.0837417Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-08-06T21:24:13.0837708Z The specification describing how to shard the Tensor. 2024-08-06T21:24:13.0838011Z global_size (Sequence[int]): Size of the sharded tensor. 2024-08-06T21:24:13.0838456Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-08-06T21:24:13.0838614Z Default: None 2024-08-06T21:24:13.0838920Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-06T21:24:13.0839275Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-06T21:24:13.0839608Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-06T21:24:13.0839781Z Default: ``False``. 2024-08-06T21:24:13.0839919Z 2024-08-06T21:24:13.0840062Z Returns: 2024-08-06T21:24:13.0840510Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-08-06T21:24:13.0840748Z tensor stored in the current rank. 2024-08-06T21:24:13.0840883Z 2024-08-06T21:24:13.0841046Z Examples: 2024-08-06T21:24:13.0841208Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.0841450Z >>> # All tensors below are of torch.int64 type. 2024-08-06T21:24:13.0841644Z >>> # We have 2 process groups, 2 ranks. 2024-08-06T21:24:13.0841947Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-08-06T21:24:13.0842308Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-08-06T21:24:13.0842604Z >>> local_tensor 2024-08-06T21:24:13.0842771Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-08-06T21:24:13.0842949Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-08-06T21:24:13.0843108Z >>> sharding_dim = 0 2024-08-06T21:24:13.0843391Z >>> sharding_spec = ChunkShardingSpec( 2024-08-06T21:24:13.0843571Z dim=sharding_dim, 2024-08-06T21:24:13.0843730Z placements=[ 2024-08-06T21:24:13.0843891Z "rank:0/cuda:0", 2024-08-06T21:24:13.0844073Z "rank:1/cuda:1", 2024-08-06T21:24:13.0844219Z ], 2024-08-06T21:24:13.0844376Z ) 2024-08-06T21:24:13.0844851Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-08-06T21:24:13.0844994Z >>> st 2024-08-06T21:24:13.0845155Z ShardedTensor( 2024-08-06T21:24:13.0845345Z ShardedTensorMetadata( 2024-08-06T21:24:13.0845508Z shards_metadata=[ 2024-08-06T21:24:13.0845989Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-08-06T21:24:13.0846472Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-08-06T21:24:13.0846618Z ], 2024-08-06T21:24:13.0846805Z size=torch.Size([2, 4]) 2024-08-06T21:24:13.0846944Z ) 2024-08-06T21:24:13.0847105Z >>> st.local_tensor() 2024-08-06T21:24:13.0847280Z tensor([1, 2, 3, 4]) # Rank 0 2024-08-06T21:24:13.0847544Z tensor([3, 4, 5, 6]) # Rank 1 2024-08-06T21:24:13.0847680Z 2024-08-06T21:24:13.0848176Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-08-06T21:24:13.0848605Z rank validations, and we only validate the local shard on the current rank. 2024-08-06T21:24:13.0848983Z We fully rely on the user to ensure local tensor is sharded based on the 2024-08-06T21:24:13.0849160Z sharding spec. 2024-08-06T21:24:13.0849298Z 2024-08-06T21:24:13.0849755Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0849909Z 2024-08-06T21:24:13.0850078Z warnings.warn(msg) 2024-08-06T21:24:13.0850218Z 2024-08-06T21:24:13.0850570Z --- Parse Warning: 22 / 100 --- 2024-08-06T21:24:13.0852512Z /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. 2024-08-06T21:24:13.0852991Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0853148Z 2024-08-06T21:24:13.0853602Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-08-06T21:24:13.0853777Z single local shard. 2024-08-06T21:24:13.0853914Z 2024-08-06T21:24:13.0854301Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-08-06T21:24:13.0854752Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-08-06T21:24:13.0854932Z we swap local shards directly. 2024-08-06T21:24:13.0855400Z For more generic cases, we merge different shards across different ranks and split 2024-08-06T21:24:13.0855869Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-08-06T21:24:13.0856061Z 2024-08-06T21:24:13.0856200Z Args: 2024-08-06T21:24:13.0856732Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-08-06T21:24:13.0857015Z specification describing how the tensor is sharded. 2024-08-06T21:24:13.0857153Z 2024-08-06T21:24:13.0857309Z Returns: 2024-08-06T21:24:13.0857668Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-08-06T21:24:13.0857809Z 2024-08-06T21:24:13.0857973Z Examples: 2024-08-06T21:24:13.0858141Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.0858342Z >>> # We have 2 process groups, 2 ranks. 2024-08-06T21:24:13.0858657Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-08-06T21:24:13.0858891Z >>> tensor = torch.stack([tensor, tensor]) 2024-08-06T21:24:13.0859030Z >>> tensor 2024-08-06T21:24:13.0859242Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-08-06T21:24:13.0859441Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-08-06T21:24:13.0859651Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-08-06T21:24:13.0859852Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-08-06T21:24:13.0860015Z >>> sharding_dim = 0 2024-08-06T21:24:13.0860210Z >>> spec = ChunkShardingSpec( 2024-08-06T21:24:13.0860374Z dim=sharding_dim, 2024-08-06T21:24:13.0860534Z placements=[ 2024-08-06T21:24:13.0860709Z "rank:0/cuda:0", 2024-08-06T21:24:13.0860863Z "rank:1/cuda:1", 2024-08-06T21:24:13.0861019Z "rank:2/cuda:2", 2024-08-06T21:24:13.0861187Z "rank:3/cuda:3", 2024-08-06T21:24:13.0861330Z ], 2024-08-06T21:24:13.0861473Z ) 2024-08-06T21:24:13.0861659Z >>> current_offsets = [0] * 2 2024-08-06T21:24:13.0861838Z >>> current_offsets[0] = rank * 2 2024-08-06T21:24:13.0862034Z >>> shard_metadata = ShardMetadata( 2024-08-06T21:24:13.0862368Z shard_offsets=copy.deepcopy(current_offsets), 2024-08-06T21:24:13.0862553Z shard_sizes=tensor.size(), 2024-08-06T21:24:13.0862760Z placement=spec.placements[rank], 2024-08-06T21:24:13.0862915Z ) 2024-08-06T21:24:13.0863075Z >>> local_shards = [ 2024-08-06T21:24:13.0863219Z Shard( 2024-08-06T21:24:13.0863389Z tensor=tensor, 2024-08-06T21:24:13.0863572Z metadata=shard_metadata, 2024-08-06T21:24:13.0863715Z ) 2024-08-06T21:24:13.0863869Z ] 2024-08-06T21:24:13.0864272Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-08-06T21:24:13.0864436Z >>> sharding_dim = 1 2024-08-06T21:24:13.0864662Z >>> resharding_spec = ChunkShardingSpec( 2024-08-06T21:24:13.0864820Z dim=sharding_dim, 2024-08-06T21:24:13.0864997Z placements=[ 2024-08-06T21:24:13.0865155Z "rank:0/cuda:0", 2024-08-06T21:24:13.0865317Z "rank:1/cuda:1", 2024-08-06T21:24:13.0865487Z "rank:2/cuda:2", 2024-08-06T21:24:13.0865644Z "rank:3/cuda:3", 2024-08-06T21:24:13.0865783Z ], 2024-08-06T21:24:13.0865931Z ) 2024-08-06T21:24:13.0866118Z >>> st.reshard(resharding_spec) 2024-08-06T21:24:13.0866315Z >>> tensor = st.local_shards()[0].tensor 2024-08-06T21:24:13.0866474Z >>> tensor 2024-08-06T21:24:13.0866785Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-08-06T21:24:13.0867018Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-08-06T21:24:13.0867260Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-08-06T21:24:13.0867505Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-08-06T21:24:13.0867649Z 2024-08-06T21:24:13.0868158Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0868298Z 2024-08-06T21:24:13.0868468Z warnings.warn(msg) 2024-08-06T21:24:13.0868620Z 2024-08-06T21:24:13.0868953Z --- Parse Warning: 23 / 100 --- 2024-08-06T21:24:13.0870794Z /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. 2024-08-06T21:24:13.0871280Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0871419Z 2024-08-06T21:24:13.0871818Z Representation of a sharding plan, describes how to shard a module 2024-08-06T21:24:13.0872342Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-08-06T21:24:13.0872859Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-08-06T21:24:13.0873323Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-08-06T21:24:13.0873472Z 2024-08-06T21:24:13.0873617Z Args: 2024-08-06T21:24:13.0874108Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-08-06T21:24:13.0874399Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-08-06T21:24:13.0874886Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-08-06T21:24:13.0875352Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-08-06T21:24:13.0875560Z a parameter to a `ShardingSpec`. 2024-08-06T21:24:13.0876034Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-08-06T21:24:13.0876213Z to a `Sharder` object. 2024-08-06T21:24:13.0876808Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-08-06T21:24:13.0877350Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-08-06T21:24:13.0877766Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-08-06T21:24:13.0877921Z Default: `None` 2024-08-06T21:24:13.0878391Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-08-06T21:24:13.0878825Z a module's sharded output to be returned as a Tensor from its local shards to 2024-08-06T21:24:13.0879272Z ensure further processing in a data parallel fashion. ("" in list means the 2024-08-06T21:24:13.0879440Z root module). 2024-08-06T21:24:13.0879600Z Default: None 2024-08-06T21:24:13.0879748Z Example: 2024-08-06T21:24:13.0880270Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-08-06T21:24:13.0880784Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-08-06T21:24:13.0880932Z 2024-08-06T21:24:13.0881227Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-08-06T21:24:13.0881407Z >>> class MyModule(nn.Module): 2024-08-06T21:24:13.0881602Z >>> def __init__(self) -> None: 2024-08-06T21:24:13.0881766Z >>> super().__init__() 2024-08-06T21:24:13.0881938Z >>> self.fc1 = nn.Linear() 2024-08-06T21:24:13.0882121Z >>> self.gelu = nn.GELU() 2024-08-06T21:24:13.0882288Z >>> self.fc2 = nn.Linear() 2024-08-06T21:24:13.0882460Z >>> self.relu = nn.Linear() 2024-08-06T21:24:13.0882620Z >>> 2024-08-06T21:24:13.0882801Z >>> def forward(self, input): 2024-08-06T21:24:13.0883104Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-08-06T21:24:13.0883255Z 2024-08-06T21:24:13.0883417Z 2024-08-06T21:24:13.0883636Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-08-06T21:24:13.0883838Z >>> sharding_plan = ShardingPlan( 2024-08-06T21:24:13.0883982Z >>> plan={ 2024-08-06T21:24:13.0884147Z >>> "fc1.weight": spec1, 2024-08-06T21:24:13.0884328Z >>> "fc2.weight": spec2 2024-08-06T21:24:13.0884468Z >>> }, 2024-08-06T21:24:13.0884622Z >>> output_plan={ 2024-08-06T21:24:13.0884800Z >>> "fc2": output_spec 2024-08-06T21:24:13.0884938Z >>> }, 2024-08-06T21:24:13.0885118Z >>> return_local_tensor=["fc2"] 2024-08-06T21:24:13.0885272Z >>> ) 2024-08-06T21:24:13.0885405Z 2024-08-06T21:24:13.0885857Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0886048Z 2024-08-06T21:24:13.0886214Z warnings.warn(msg) 2024-08-06T21:24:13.0886348Z 2024-08-06T21:24:13.0886681Z --- Parse Warning: 24 / 100 --- 2024-08-06T21:24:13.0888539Z /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/local_map.py line=30. 2024-08-06T21:24:13.0889023Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0889158Z 2024-08-06T21:24:13.0889622Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-08-06T21:24:13.0890006Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-08-06T21:24:13.0890144Z 2024-08-06T21:24:13.0890281Z Args: 2024-08-06T21:24:13.0890658Z func (Callable): the function to be applied on each local shard of 2024-08-06T21:24:13.0890824Z :class:`DTensor`s. 2024-08-06T21:24:13.0891223Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-08-06T21:24:13.0891684Z the desired placements of the :class:`DTensor`s in `func`'s flattened output. 2024-08-06T21:24:13.0892162Z If the flattened `output` is a single value, the `out_placements` should be 2024-08-06T21:24:13.0892601Z of type `PlacementType`. Otherwise if the flattened `output` has multiple 2024-08-06T21:24:13.0893036Z values, the `out_placements` should be a tuple of `PlacementType` values 1:1 2024-08-06T21:24:13.0893238Z mapping to the flattened `output`. 2024-08-06T21:24:13.0893614Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-08-06T21:24:13.0894019Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-08-06T21:24:13.0894224Z the `PlacementType` should be `None`. 2024-08-06T21:24:13.0894653Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-08-06T21:24:13.0895042Z in. In this case, even if `out_placements` is not `None`, the result function 2024-08-06T21:24:13.0895454Z should ignore the desired placements because the application is not on 2024-08-06T21:24:13.0895636Z :class:`DTensors`. 2024-08-06T21:24:13.0895920Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-08-06T21:24:13.0896366Z the required placements of the :class:`DTensor`s in `func`'s flattened input. 2024-08-06T21:24:13.0896758Z If `in_placements` is specified, `local_map` would examine whether the 2024-08-06T21:24:13.0897161Z placements of each :class:`DTensor` argument is the same as the required 2024-08-06T21:24:13.0897496Z placements or not. If the placements are not the same and 2024-08-06T21:24:13.0897922Z `redistribute_inputs` is `False`, an exception will be raised. Otherwise if 2024-08-06T21:24:13.0898357Z `redistribute_inputs` is `True`, the argument will be first redistributed to 2024-08-06T21:24:13.0898822Z the required sharding placements before passing its local tensor to `func`. 2024-08-06T21:24:13.0899245Z The only exception is when required placements are not `None` and the 2024-08-06T21:24:13.0899667Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-08-06T21:24:13.0900047Z will be skipped and the argument will be directly passed to `func`. 2024-08-06T21:24:13.0900436Z If `in_placements` is `None`, no placements examination will be performed. 2024-08-06T21:24:13.0900616Z Default: `None` 2024-08-06T21:24:13.0900845Z device_mesh (:class:`DeviceMesh`, optional): 2024-08-06T21:24:13.0901216Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-08-06T21:24:13.0901642Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-08-06T21:24:13.0902203Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-08-06T21:24:13.0902391Z device mesh. Default: `None`. 2024-08-06T21:24:13.0902612Z redistribute_inputs (bool, optional): 2024-08-06T21:24:13.0903067Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-08-06T21:24:13.0903510Z their placements are different from the required input placements. If this 2024-08-06T21:24:13.0903925Z value is `False` and some :class:`DTensor` input has a different placement, 2024-08-06T21:24:13.0904164Z an exception will be raised. Default: `False`. 2024-08-06T21:24:13.0904307Z 2024-08-06T21:24:13.0904464Z Returns: 2024-08-06T21:24:13.0904918Z A `Callable` that applies `func` to each local shard of the input :class:`DTensor` 2024-08-06T21:24:13.0905347Z and returns a :class:`DTensor` constructed from the return value of `func`. 2024-08-06T21:24:13.0905482Z 2024-08-06T21:24:13.0905623Z Raises: 2024-08-06T21:24:13.0906093Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-08-06T21:24:13.0906495Z mesh, or if they are placed on a different device mesh than the `device_mesh` 2024-08-06T21:24:13.0906801Z argument passed in. 2024-08-06T21:24:13.0906956Z 2024-08-06T21:24:13.0907436Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-08-06T21:24:13.0907900Z output placement in `out_placements` be `None`. An AssertionError will be raised 2024-08-06T21:24:13.0908091Z if this is not the case. 2024-08-06T21:24:13.0908230Z 2024-08-06T21:24:13.0908692Z ValueError: If `redistribute_inputs=False` but the input :class:`DTensor` needs 2024-08-06T21:24:13.0908949Z a redistribution according to `in_placements`. 2024-08-06T21:24:13.0909090Z 2024-08-06T21:24:13.0909234Z Example: 2024-08-06T21:24:13.0909447Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:13.0909682Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-08-06T21:24:13.0909887Z >>> partial_sum_tensor = torch.mm(W, X) 2024-08-06T21:24:13.0910341Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-08-06T21:24:13.0910519Z >>> return reduced_tensor 2024-08-06T21:24:13.0910660Z >>> 2024-08-06T21:24:13.0910886Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-08-06T21:24:13.0911094Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-08-06T21:24:13.0911265Z >>> Y = torch.mm(W, X) 2024-08-06T21:24:13.0911590Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-08-06T21:24:13.0911911Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-08-06T21:24:13.0912068Z >>> 2024-08-06T21:24:13.0912560Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-08-06T21:24:13.0912773Z >>> local_mm_allreduce_forward = local_map( 2024-08-06T21:24:13.0912996Z >>> mm_allreduce_forward, 2024-08-06T21:24:13.0913188Z >>> out_placements=[Replicate()], 2024-08-06T21:24:13.0913392Z >>> in_placements=[col_wise, row_wise], 2024-08-06T21:24:13.0913580Z >>> device_mesh=device_mesh, 2024-08-06T21:24:13.0913720Z >>> ) 2024-08-06T21:24:13.0913859Z >>> 2024-08-06T21:24:13.0914323Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-08-06T21:24:13.0914777Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-08-06T21:24:13.0915373Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-08-06T21:24:13.0915522Z 2024-08-06T21:24:13.0915847Z NOTE: This API is currently experimental and subject to change 2024-08-06T21:24:13.0916028Z 2024-08-06T21:24:13.0916475Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0916617Z 2024-08-06T21:24:13.0916790Z warnings.warn(msg) 2024-08-06T21:24:13.0916928Z 2024-08-06T21:24:13.0917270Z --- Parse Warning: 25 / 100 --- 2024-08-06T21:24:13.0919281Z /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=22. 2024-08-06T21:24:13.0919751Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0919885Z 2024-08-06T21:24:13.0920372Z ``register_sharding`` is an experimental API that allows users to register sharding 2024-08-06T21:24:13.0920859Z strategies for an operator when the tensor inputs and outputs are :class:`DTensor`s. 2024-08-06T21:24:13.0921308Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-08-06T21:24:13.0921748Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-08-06T21:24:13.0922303Z when users would like to overwrite default sharding strategies of existing operators. 2024-08-06T21:24:13.0922455Z 2024-08-06T21:24:13.0922594Z Args: 2024-08-06T21:24:13.0922810Z op (Union[OpOverload, List[OpOverload]]): 2024-08-06T21:24:13.0923158Z An op or a list of ops to register the customized sharding function. 2024-08-06T21:24:13.0923297Z 2024-08-06T21:24:13.0923440Z Returns: 2024-08-06T21:24:13.0923937Z A function decorator which can be used to wrap a function that defines the sharding 2024-08-06T21:24:13.0924430Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-08-06T21:24:13.0924929Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-08-06T21:24:13.0925494Z already implemented the operator. The customized sharding function takes the same inputs 2024-08-06T21:24:13.0925921Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-08-06T21:24:13.0926408Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-08-06T21:24:13.0926913Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-08-06T21:24:13.0927113Z corresponding intput placements. 2024-08-06T21:24:13.0927264Z 2024-08-06T21:24:13.0927412Z Example: 2024-08-06T21:24:13.0927600Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:13.0927833Z >>> @register_sharding(aten._softmax.default) 2024-08-06T21:24:13.0928096Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-08-06T21:24:13.0928334Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-08-06T21:24:13.0928537Z >>> acceptable_shardings = [] 2024-08-06T21:24:13.0928676Z >>> 2024-08-06T21:24:13.0928985Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-08-06T21:24:13.0929269Z >>> acceptable_shardings.append(all_replicate) 2024-08-06T21:24:13.0929415Z >>> 2024-08-06T21:24:13.0929609Z >>> for sharding_dim in range(x.ndim): 2024-08-06T21:24:13.0929818Z >>> if sharding_dim != softmax_dim: 2024-08-06T21:24:13.0929981Z >>> all_sharded = ( 2024-08-06T21:24:13.0930168Z >>> [Shard(sharding_dim)], 2024-08-06T21:24:13.0930387Z >>> [Shard(sharding_dim), None, None], 2024-08-06T21:24:13.0930536Z >>> ) 2024-08-06T21:24:13.0930773Z >>> acceptable_shardings.append(all_sharded) 2024-08-06T21:24:13.0930927Z >>> 2024-08-06T21:24:13.0931117Z >>> return acceptable_shardings 2024-08-06T21:24:13.0931290Z 2024-08-06T21:24:13.0931762Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0931904Z 2024-08-06T21:24:13.0932072Z warnings.warn(msg) 2024-08-06T21:24:13.0932229Z 2024-08-06T21:24:13.0932550Z --- Parse Warning: 26 / 100 --- 2024-08-06T21:24:13.0934646Z /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. 2024-08-06T21:24:13.0935121Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0935261Z 2024-08-06T21:24:13.0935461Z Run post-localSGD algorithm. 2024-08-06T21:24:13.0935599Z 2024-08-06T21:24:13.0936020Z This DDP communication hook is used for running post-localSGD algorithm, 2024-08-06T21:24:13.0936302Z by combining with a model averaging component (e.g., 2024-08-06T21:24:13.0936886Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-08-06T21:24:13.0937082Z that runs after the optimizer step. 2024-08-06T21:24:13.0937241Z 2024-08-06T21:24:13.0937381Z Args: 2024-08-06T21:24:13.0937842Z state (PostLocalSGDState): State information to run post-localSGD. 2024-08-06T21:24:13.0938350Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-08-06T21:24:13.0939125Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-06T21:24:13.0939581Z Note that since DDP comm hook only supports single process single device mode, 2024-08-06T21:24:13.0939842Z only exactly one tensor is stored in this bucket. 2024-08-06T21:24:13.0939988Z 2024-08-06T21:24:13.0940144Z Returns: 2024-08-06T21:24:13.0940573Z Future handler of the communication, which updates the gradients in place. 2024-08-06T21:24:13.0940720Z 2024-08-06T21:24:13.0940903Z Example:: 2024-08-06T21:24:13.0941071Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.0941525Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-08-06T21:24:13.0941735Z start_localSGD_iter=10) 2024-08-06T21:24:13.0942032Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-08-06T21:24:13.0942810Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-08-06T21:24:13.0943452Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-08-06T21:24:13.0943590Z 2024-08-06T21:24:13.0944046Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0944194Z 2024-08-06T21:24:13.0944358Z warnings.warn(msg) 2024-08-06T21:24:13.0944514Z 2024-08-06T21:24:13.0944843Z --- Parse Warning: 27 / 100 --- 2024-08-06T21:24:13.0946918Z /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=342. 2024-08-06T21:24:13.0947486Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0947622Z 2024-08-06T21:24:13.0947804Z Implement PowerSGD algorithm. 2024-08-06T21:24:13.0947958Z 2024-08-06T21:24:13.0948353Z This DDP communication hook implements PowerSGD gradient compression 2024-08-06T21:24:13.0948767Z algorithm described in the `paper `_. 2024-08-06T21:24:13.0949214Z Once gradient tensors are aggregated across all workers, this hook applies 2024-08-06T21:24:13.0949383Z compression as follows: 2024-08-06T21:24:13.0949567Z 2024-08-06T21:24:13.0950383Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-08-06T21:24:13.0950526Z 2024-08-06T21:24:13.0951315Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-08-06T21:24:13.0951450Z 2024-08-06T21:24:13.0952198Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-08-06T21:24:13.0952352Z 2024-08-06T21:24:13.0952537Z 2. Handles uncompressed tensors: 2024-08-06T21:24:13.0952674Z 2024-08-06T21:24:13.0953639Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-08-06T21:24:13.0953779Z 2024-08-06T21:24:13.0954401Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-08-06T21:24:13.0954555Z 2024-08-06T21:24:13.0954972Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-08-06T21:24:13.0955108Z 2024-08-06T21:24:13.0955545Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-08-06T21:24:13.0956174Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-08-06T21:24:13.0956312Z 2024-08-06T21:24:13.0956571Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-08-06T21:24:13.0956705Z 2024-08-06T21:24:13.0956884Z 3.3. Allreduces Ps as a batch; 2024-08-06T21:24:13.0957037Z 2024-08-06T21:24:13.0957227Z 3.4. Orthogonalizes each P in Ps; 2024-08-06T21:24:13.0957359Z 2024-08-06T21:24:13.0957712Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-08-06T21:24:13.0957852Z 2024-08-06T21:24:13.0958042Z 3.6. Allreduces Qs as a batch; 2024-08-06T21:24:13.0958186Z 2024-08-06T21:24:13.0958724Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-08-06T21:24:13.0958878Z 2024-08-06T21:24:13.0959640Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-08-06T21:24:13.0960153Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-08-06T21:24:13.0960957Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-08-06T21:24:13.0961114Z 2024-08-06T21:24:13.0961252Z Args: 2024-08-06T21:24:13.0962061Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-08-06T21:24:13.0962715Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-08-06T21:24:13.0962903Z and ``min_compression_rate``. 2024-08-06T21:24:13.0963684Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-06T21:24:13.0964184Z Note that since DDP comm hook only supports single process single device mode, 2024-08-06T21:24:13.0964453Z only exactly one tensor is stored in this bucket. 2024-08-06T21:24:13.0964594Z 2024-08-06T21:24:13.0964740Z Returns: 2024-08-06T21:24:13.0965187Z Future handler of the communication, which updates the gradients in place. 2024-08-06T21:24:13.0965329Z 2024-08-06T21:24:13.0965479Z Example:: 2024-08-06T21:24:13.0965653Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.0966140Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-08-06T21:24:13.0966418Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-08-06T21:24:13.0966699Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-08-06T21:24:13.0966869Z 2024-08-06T21:24:13.0967325Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0967471Z 2024-08-06T21:24:13.0967636Z warnings.warn(msg) 2024-08-06T21:24:13.0967777Z 2024-08-06T21:24:13.0968129Z --- Parse Warning: 28 / 100 --- 2024-08-06T21:24:13.0970194Z /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=36. 2024-08-06T21:24:13.0970678Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0970812Z 2024-08-06T21:24:13.0971139Z Averages parameters periodically after the warm-up stage. 2024-08-06T21:24:13.0971293Z 2024-08-06T21:24:13.0971744Z This can be used for running `post-local SGD `_, 2024-08-06T21:24:13.0972074Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-08-06T21:24:13.0972514Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-08-06T21:24:13.0972652Z 2024-08-06T21:24:13.0972787Z Args: 2024-08-06T21:24:13.0973146Z period (int): The number of steps per model averaging. 2024-08-06T21:24:13.0973627Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-08-06T21:24:13.0973844Z Otherwise, only DDP needs to be used. 2024-08-06T21:24:13.0974221Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-08-06T21:24:13.0974418Z model averaging is skipped. 2024-08-06T21:24:13.0974741Z process_group: The process group to be used for all-reduce. 2024-08-06T21:24:13.0974995Z If ``None``, the default process group, which 2024-08-06T21:24:13.0975327Z is created by :func:`torch.distributed.init_process_group`, 2024-08-06T21:24:13.0975541Z will be used. (default: ``None``) 2024-08-06T21:24:13.0975689Z 2024-08-06T21:24:13.0975841Z Example:: 2024-08-06T21:24:13.0975988Z 2024-08-06T21:24:13.0976208Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.0976366Z >>> import torch 2024-08-06T21:24:13.0976575Z >>> import torch.distributed as dist 2024-08-06T21:24:13.0977140Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-08-06T21:24:13.0977618Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-06T21:24:13.0977803Z >>> import torch.nn as nn 2024-08-06T21:24:13.0977942Z >>> 2024-08-06T21:24:13.0978252Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-06T21:24:13.0978441Z >>> torch.cuda.set_device(rank) 2024-08-06T21:24:13.0978657Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-08-06T21:24:13.0978908Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-06T21:24:13.0979194Z >>> module, device_ids=[rank], output_device=rank 2024-08-06T21:24:13.0979339Z >>> ) 2024-08-06T21:24:13.0979586Z >>> # Register a post-localSGD communication hook. 2024-08-06T21:24:13.0980126Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-06T21:24:13.0980401Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-06T21:24:13.0980557Z >>> 2024-08-06T21:24:13.0981030Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-06T21:24:13.0981295Z >>> # After 100 steps, run model averaging every 4 steps. 2024-08-06T21:24:13.0981872Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-06T21:24:13.0982347Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-06T21:24:13.0982520Z >>> for step in range(0, 200): 2024-08-06T21:24:13.0982707Z >>> optimizer.zero_grad() 2024-08-06T21:24:13.0982892Z >>> loss = loss_fn(output, labels) 2024-08-06T21:24:13.0983058Z >>> loss.backward() 2024-08-06T21:24:13.0983244Z >>> optimizer.step() 2024-08-06T21:24:13.0983582Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-08-06T21:24:13.0983936Z >>> # inter-node communication only occurs every 4 iterations after 2024-08-06T21:24:13.0984157Z >>> # the initial ``warmup_steps`` period. 2024-08-06T21:24:13.0984427Z >>> averager.average_parameters(model.parameters()) 2024-08-06T21:24:13.0984582Z 2024-08-06T21:24:13.0985035Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.0985176Z 2024-08-06T21:24:13.0985353Z warnings.warn(msg) 2024-08-06T21:24:13.0985490Z 2024-08-06T21:24:13.0985804Z --- Parse Warning: 29 / 100 --- 2024-08-06T21:24:13.0989540Z /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. 2024-08-06T21:24:13.0990016Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.0990154Z 2024-08-06T21:24:13.0990775Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-08-06T21:24:13.0990915Z 2024-08-06T21:24:13.0991474Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-08-06T21:24:13.0991853Z by using different periods concurrently after the warm-up stage. 2024-08-06T21:24:13.0992587Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-08-06T21:24:13.0993204Z that supports `post-local SGD `_, which essentially only supports 2024-08-06T21:24:13.0993763Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-08-06T21:24:13.0994399Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-08-06T21:24:13.0994958Z Similarly, the process groups within this class do not have such an intra-machine process 2024-08-06T21:24:13.0995451Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-08-06T21:24:13.0995593Z 2024-08-06T21:24:13.0995752Z Args: 2024-08-06T21:24:13.0996224Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-08-06T21:24:13.0996585Z process group size, used for initializing process groups of 2024-08-06T21:24:13.0996991Z different sizes in a hierarchy to average parameters concurrently. 2024-08-06T21:24:13.0997400Z Particularly, at each iteration, there will be at most a single 2024-08-06T21:24:13.0997832Z process group that runs averaging -- the period of such group should 2024-08-06T21:24:13.0998208Z have the largest period which the current step can be divided by. 2024-08-06T21:24:13.0998491Z For example, if the dict has three keys: 2, 4, and 8, 2024-08-06T21:24:13.0998874Z then this means totally three process groups will be created to 2024-08-06T21:24:13.0999241Z average parameters every 2, 4, and 8 iterations, respectively. 2024-08-06T21:24:13.0999576Z At the 4th iteration, only the second process group will run 2024-08-06T21:24:13.0999931Z averaging, because the first process group should be a 2024-08-06T21:24:13.1000325Z subset of the second process group, and no need to execute the first 2024-08-06T21:24:13.1000548Z process group redundantly. 2024-08-06T21:24:13.1000897Z On the other hand, the third process group can only be triggered 2024-08-06T21:24:13.1001292Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-08-06T21:24:13.1001848Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-08-06T21:24:13.1002663Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-08-06T21:24:13.1002953Z If ``None``, the default process group, which is created 2024-08-06T21:24:13.1003328Z by :func:`torch.distributed.init_process_group`, will be used. 2024-08-06T21:24:13.1003530Z (default: ``None``) 2024-08-06T21:24:13.1003682Z 2024-08-06T21:24:13.1003838Z Example:: 2024-08-06T21:24:13.1004095Z >>> # xdoctest: +SKIP('undefined rank') 2024-08-06T21:24:13.1004313Z >>> from collections import OrderedDict 2024-08-06T21:24:13.1004467Z >>> import torch 2024-08-06T21:24:13.1004668Z >>> import torch.distributed as dist 2024-08-06T21:24:13.1005169Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-06T21:24:13.1005349Z >>> PostLocalSGDState, 2024-08-06T21:24:13.1005521Z >>> post_localSGD_hook, 2024-08-06T21:24:13.1005673Z >>> ) 2024-08-06T21:24:13.1006359Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-08-06T21:24:13.1006535Z >>> import torch.nn as nn 2024-08-06T21:24:13.1006685Z >>> 2024-08-06T21:24:13.1006988Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-06T21:24:13.1007168Z >>> torch.cuda.set_device(rank) 2024-08-06T21:24:13.1007417Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-08-06T21:24:13.1007674Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-06T21:24:13.1007915Z >>> module, device_ids=[rank], output_device=rank 2024-08-06T21:24:13.1008069Z >>> ) 2024-08-06T21:24:13.1008318Z >>> # Register a post-localSGD communication hook. 2024-08-06T21:24:13.1008818Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-08-06T21:24:13.1009014Z >>> subgroup, _ = dist.new_subgroups() 2024-08-06T21:24:13.1009577Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-08-06T21:24:13.1009865Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-06T21:24:13.1010005Z >>> 2024-08-06T21:24:13.1010551Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-08-06T21:24:13.1010768Z >>> # the 16 processes every 16 iterations. 2024-08-06T21:24:13.1011079Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-08-06T21:24:13.1011491Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-08-06T21:24:13.1012067Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-06T21:24:13.1012537Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-06T21:24:13.1012815Z >>> # After 100 steps, run model averaging at two levels. 2024-08-06T21:24:13.1012993Z >>> for step in range(0, 200): 2024-08-06T21:24:13.1013201Z >>> optimizer.zero_grad() 2024-08-06T21:24:13.1013402Z >>> loss = loss_fn(output, labels) 2024-08-06T21:24:13.1013565Z >>> loss.backward() 2024-08-06T21:24:13.1013731Z >>> optimizer.step() 2024-08-06T21:24:13.1014005Z >>> # Average parameters after ``optimizer.step()``. 2024-08-06T21:24:13.1014517Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-08-06T21:24:13.1014786Z >>> averager.average_parameters(model.parameters()) 2024-08-06T21:24:13.1014937Z 2024-08-06T21:24:13.1015090Z .. warning :: 2024-08-06T21:24:13.1015538Z The last group size in the dict must be the size of the provided ``process_group``, 2024-08-06T21:24:13.1015966Z which indicates model averaging at the highest level of the hierarchy. 2024-08-06T21:24:13.1016509Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-08-06T21:24:13.1016649Z 2024-08-06T21:24:13.1016814Z .. warning :: 2024-08-06T21:24:13.1017230Z `HierarchicalModelAverager` is experimental and subject to change. 2024-08-06T21:24:13.1017385Z 2024-08-06T21:24:13.1030757Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1030984Z 2024-08-06T21:24:13.1031363Z warnings.warn(msg) 2024-08-06T21:24:13.1031523Z 2024-08-06T21:24:13.1031926Z --- Parse Warning: 30 / 100 --- 2024-08-06T21:24:13.1033948Z /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. 2024-08-06T21:24:13.1034432Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1034569Z 2024-08-06T21:24:13.1035113Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-08-06T21:24:13.1035550Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-08-06T21:24:13.1035699Z 2024-08-06T21:24:13.1035989Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-08-06T21:24:13.1036121Z 2024-08-06T21:24:13.1036287Z .. warning:: 2024-08-06T21:24:13.1036594Z Current implementation only supports loading Tensors. 2024-08-06T21:24:13.1036728Z 2024-08-06T21:24:13.1036918Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.1037090Z >>> sd = {"mode": model} 2024-08-06T21:24:13.1037239Z >>> dcp.load( 2024-08-06T21:24:13.1037382Z >>> sd, 2024-08-06T21:24:13.1037652Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-06T21:24:13.1037863Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-06T21:24:13.1038057Z >>> checkpoint_id="path_to_model.pt" 2024-08-06T21:24:13.1038214Z >>> ) 2024-08-06T21:24:13.1038352Z 2024-08-06T21:24:13.1038811Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1038968Z 2024-08-06T21:24:13.1039127Z warnings.warn(msg) 2024-08-06T21:24:13.1039312Z 2024-08-06T21:24:13.1039642Z --- Parse Warning: 31 / 100 --- 2024-08-06T21:24:13.1041616Z /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. 2024-08-06T21:24:13.1042099Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1042240Z 2024-08-06T21:24:13.1043077Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-08-06T21:24:13.1043680Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-08-06T21:24:13.1043877Z metadata file, like Torch Save files. 2024-08-06T21:24:13.1044114Z 2024-08-06T21:24:13.1044445Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-08-06T21:24:13.1044592Z 2024-08-06T21:24:13.1044745Z .. warning:: 2024-08-06T21:24:13.1045059Z Current implementation only supports loading Tensors. 2024-08-06T21:24:13.1045203Z 2024-08-06T21:24:13.1045393Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.1045575Z >>> sd = {"mode": model} 2024-08-06T21:24:13.1045728Z >>> dcp.load( 2024-08-06T21:24:13.1045868Z >>> sd, 2024-08-06T21:24:13.1046146Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-06T21:24:13.1046355Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-06T21:24:13.1046552Z >>> checkpoint_id="path_to_model.pt" 2024-08-06T21:24:13.1046707Z >>> ) 2024-08-06T21:24:13.1046849Z 2024-08-06T21:24:13.1047302Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1047462Z 2024-08-06T21:24:13.1047628Z warnings.warn(msg) 2024-08-06T21:24:13.1047765Z 2024-08-06T21:24:13.1048102Z --- Parse Warning: 32 / 100 --- 2024-08-06T21:24:13.1050177Z /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. 2024-08-06T21:24:13.1050657Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1050797Z 2024-08-06T21:24:13.1051166Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-08-06T21:24:13.1051321Z 2024-08-06T21:24:13.1051602Z This is the current recommended way to checkpoint FSDP. 2024-08-06T21:24:13.1051767Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1052044Z >>> import torch.distributed.checkpoint as dist_cp 2024-08-06T21:24:13.1052191Z >>> # Save 2024-08-06T21:24:13.1052362Z >>> model: torch.nn.Model 2024-08-06T21:24:13.1052579Z >>> optim_params = model.parameters() 2024-08-06T21:24:13.1052824Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-08-06T21:24:13.1052967Z >>> # Save 2024-08-06T21:24:13.1053373Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-08-06T21:24:13.1053530Z >>> state_dict = { 2024-08-06T21:24:13.1053798Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-08-06T21:24:13.1053997Z >>> "model": model.state_dict() 2024-08-06T21:24:13.1054136Z >>> } 2024-08-06T21:24:13.1054313Z >>> dist_cp.save_state_dict( 2024-08-06T21:24:13.1054511Z >>> state_dict=optim_state, 2024-08-06T21:24:13.1054823Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-08-06T21:24:13.1055063Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-08-06T21:24:13.1055201Z >>> ) 2024-08-06T21:24:13.1055344Z >>> 2024-08-06T21:24:13.1055504Z >>> # Load 2024-08-06T21:24:13.1055909Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-08-06T21:24:13.1056214Z >>> model_state_dict = model_tp.state_dict() 2024-08-06T21:24:13.1056388Z >>> checkpoint = { 2024-08-06T21:24:13.1056570Z >>> "model": model_state_dict 2024-08-06T21:24:13.1056713Z >>> } 2024-08-06T21:24:13.1056898Z >>> dist_cp.load_state_dict( 2024-08-06T21:24:13.1057074Z >>> state_dict=checkpoint, 2024-08-06T21:24:13.1057407Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-08-06T21:24:13.1057645Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-08-06T21:24:13.1057784Z >>> ) 2024-08-06T21:24:13.1058044Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-08-06T21:24:13.1058198Z >>> 2024-08-06T21:24:13.1058495Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-08-06T21:24:13.1058690Z >>> model_state_dict, 2024-08-06T21:24:13.1058896Z >>> optimizer_key="optimizer", 2024-08-06T21:24:13.1059203Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-08-06T21:24:13.1059347Z >>> ) 2024-08-06T21:24:13.1059497Z >>> 2024-08-06T21:24:13.1059748Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-08-06T21:24:13.1059957Z >>> model, optim, optim_state["optimizer"] 2024-08-06T21:24:13.1060113Z >>> ) 2024-08-06T21:24:13.1060252Z >>> 2024-08-06T21:24:13.1060464Z >>> optim.load_state_dict(flattened_osd) 2024-08-06T21:24:13.1060603Z 2024-08-06T21:24:13.1061053Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1061204Z 2024-08-06T21:24:13.1061367Z warnings.warn(msg) 2024-08-06T21:24:13.1061503Z 2024-08-06T21:24:13.1061841Z --- Parse Warning: 33 / 100 --- 2024-08-06T21:24:13.1063629Z /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. 2024-08-06T21:24:13.1064105Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1064325Z 2024-08-06T21:24:13.1064842Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-08-06T21:24:13.1064985Z 2024-08-06T21:24:13.1065531Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-08-06T21:24:13.1065671Z 2024-08-06T21:24:13.1066175Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-06T21:24:13.1066380Z will be visible to the whole process. 2024-08-06T21:24:13.1066516Z 2024-08-06T21:24:13.1067096Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-08-06T21:24:13.1067256Z 2024-08-06T21:24:13.1067462Z 1) set_up_planner - called on all ranks. 2024-08-06T21:24:13.1067686Z Signals the start of a checkpoint save. 2024-08-06T21:24:13.1067830Z 2024-08-06T21:24:13.1068040Z 2) create_local_plan - called on all ranks. 2024-08-06T21:24:13.1068568Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-08-06T21:24:13.1068709Z 2024-08-06T21:24:13.1069023Z 3) create_global_plan - called on the coordinator rank only. 2024-08-06T21:24:13.1069386Z Takes the SavePlan from all ranks and make any global decision. 2024-08-06T21:24:13.1069522Z 2024-08-06T21:24:13.1069712Z 4) finish_plan - called on all ranks. 2024-08-06T21:24:13.1070109Z This gives each rank a chance to adjust to global planning decisions. 2024-08-06T21:24:13.1070253Z 2024-08-06T21:24:13.1070511Z 5) resolve_data - called multiple times on each rank 2024-08-06T21:24:13.1070879Z Lookups a value on the `state_dict` for the storage layer to write. 2024-08-06T21:24:13.1071023Z 2024-08-06T21:24:13.1071575Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-08-06T21:24:13.1071945Z most changes can be expressed by changes in a single method. 2024-08-06T21:24:13.1072082Z 2024-08-06T21:24:13.1072298Z There are 3 usual patterns of extension: 2024-08-06T21:24:13.1072450Z 2024-08-06T21:24:13.1072906Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-08-06T21:24:13.1073311Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-08-06T21:24:13.1073462Z 2024-08-06T21:24:13.1073659Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.1073904Z >>> class RenamePlanner(DefaultSavePlanner): 2024-08-06T21:24:13.1074070Z >>> def set_up_planner( 2024-08-06T21:24:13.1074220Z >>> self, 2024-08-06T21:24:13.1074424Z >>> state_dict: STATE_DICT_TYPE, 2024-08-06T21:24:13.1074668Z >>> storage_meta: Optional[StorageMeta], 2024-08-06T21:24:13.1074849Z >>> is_coordinator: bool, 2024-08-06T21:24:13.1075016Z >>> ) -> None: 2024-08-06T21:24:13.1075208Z >>> # prefix all keys with `foo_`` 2024-08-06T21:24:13.1075731Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-08-06T21:24:13.1075885Z 2024-08-06T21:24:13.1076485Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-08-06T21:24:13.1076623Z 2024-08-06T21:24:13.1076838Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.1077052Z >>> class FP16Planner(DefaultSavePlanner): 2024-08-06T21:24:13.1077236Z >>> def create_local_plan(self): 2024-08-06T21:24:13.1077453Z >>> plan = super().create_local_plan() 2024-08-06T21:24:13.1077620Z >>> for p in plan: 2024-08-06T21:24:13.1077813Z >>> if p.tensor_data is not None: 2024-08-06T21:24:13.1078111Z >>> p.tensor_data.properties.dtype = torch.float16 2024-08-06T21:24:13.1078272Z >>> return plan 2024-08-06T21:24:13.1078411Z >>> 2024-08-06T21:24:13.1078626Z >>> def resolve_data(self, write_item): 2024-08-06T21:24:13.1078902Z >>> item = super().resolve_data(write_item) 2024-08-06T21:24:13.1079407Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-08-06T21:24:13.1079546Z 2024-08-06T21:24:13.1080170Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-08-06T21:24:13.1080328Z 2024-08-06T21:24:13.1080521Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.1080705Z >>> from itertools import islice 2024-08-06T21:24:13.1080911Z >>> from dataclasses import replace 2024-08-06T21:24:13.1081193Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-08-06T21:24:13.1081690Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-08-06T21:24:13.1081932Z >>> # This sample doesn't handle ShardedTensors 2024-08-06T21:24:13.1082150Z >>> def create_global_plan(self, all_plans): 2024-08-06T21:24:13.1082330Z >>> def chunk(it, size): 2024-08-06T21:24:13.1082499Z >>> it = iter(it) 2024-08-06T21:24:13.1082790Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-08-06T21:24:13.1082951Z >>> all_plans = [ 2024-08-06T21:24:13.1083201Z >>> replace(plan, items=items) for plan, items in 2024-08-06T21:24:13.1083499Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-08-06T21:24:13.1083667Z >>> ] 2024-08-06T21:24:13.1083908Z >>> return super().create_global_plan(all_plans) 2024-08-06T21:24:13.1084051Z 2024-08-06T21:24:13.1084539Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-08-06T21:24:13.1085028Z accomplished by having each rank contribute their data items in the local plan and 2024-08-06T21:24:13.1085258Z the global planner aggregate them: 2024-08-06T21:24:13.1085407Z 2024-08-06T21:24:13.1085602Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.1085868Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-08-06T21:24:13.1086089Z >>> def create_local_plan(self) -> SavePlan: 2024-08-06T21:24:13.1086285Z >>> plan = super().create_local_plan() 2024-08-06T21:24:13.1086556Z >>> return replace(plan, planner_data="per-rank-data") 2024-08-06T21:24:13.1086711Z >>> 2024-08-06T21:24:13.1087241Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-08-06T21:24:13.1087599Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-08-06T21:24:13.1087869Z >>> merged_data = [p.planner_data for p in global_plan] 2024-08-06T21:24:13.1088192Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-08-06T21:24:13.1088397Z >>> return global_plan, metadata 2024-08-06T21:24:13.1088538Z 2024-08-06T21:24:13.1088992Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1089142Z 2024-08-06T21:24:13.1089305Z warnings.warn(msg) 2024-08-06T21:24:13.1089444Z 2024-08-06T21:24:13.1089803Z --- Parse Warning: 34 / 100 --- 2024-08-06T21:24:13.1091588Z /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=275. 2024-08-06T21:24:13.1092060Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1092212Z 2024-08-06T21:24:13.1092729Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-08-06T21:24:13.1092867Z 2024-08-06T21:24:13.1093401Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-08-06T21:24:13.1093540Z 2024-08-06T21:24:13.1094136Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-06T21:24:13.1094338Z will be visible to the whole process. 2024-08-06T21:24:13.1094474Z 2024-08-06T21:24:13.1094988Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-08-06T21:24:13.1095127Z 2024-08-06T21:24:13.1095329Z 1) set_up_planner - called on all ranks. 2024-08-06T21:24:13.1095564Z Signals the start of loading a checkpoint. 2024-08-06T21:24:13.1095700Z 2024-08-06T21:24:13.1095910Z 2) create_local_plan - called on all ranks. 2024-08-06T21:24:13.1096434Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-08-06T21:24:13.1096575Z 2024-08-06T21:24:13.1096888Z 3) create_global_plan - called on the coordinator rank only. 2024-08-06T21:24:13.1097244Z Takes the LoadPlan from all ranks and make any global decision. 2024-08-06T21:24:13.1097382Z 2024-08-06T21:24:13.1097631Z 4) load_bytes - called multiple times on each rank 2024-08-06T21:24:13.1097935Z This is called once per non-tensor value in state_dict. 2024-08-06T21:24:13.1098068Z 2024-08-06T21:24:13.1098463Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-08-06T21:24:13.1098787Z They are called in pair for each Tensor value in state_dict. 2024-08-06T21:24:13.1098923Z 2024-08-06T21:24:13.1099478Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-08-06T21:24:13.1099812Z most changes can be expressed by changes in a single method. 2024-08-06T21:24:13.1099951Z 2024-08-06T21:24:13.1100187Z There are two usual patterns of extension: 2024-08-06T21:24:13.1100324Z 2024-08-06T21:24:13.1100776Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-08-06T21:24:13.1101276Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-08-06T21:24:13.1101683Z to keep a reference to the original state_dict as load happens in place so 2024-08-06T21:24:13.1101891Z we need to be able to perform it in place 2024-08-06T21:24:13.1102043Z 2024-08-06T21:24:13.1102238Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.1102459Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-08-06T21:24:13.1102639Z >>> def set_up_planner( 2024-08-06T21:24:13.1102787Z >>> self, 2024-08-06T21:24:13.1102974Z >>> state_dict: STATE_DICT_TYPE, 2024-08-06T21:24:13.1103158Z >>> metadata: Metadata, 2024-08-06T21:24:13.1103335Z >>> is_coordinator: bool, 2024-08-06T21:24:13.1103482Z >>> ) -> None: 2024-08-06T21:24:13.1103737Z >>> self.original_state_dict = state_dict 2024-08-06T21:24:13.1104032Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-08-06T21:24:13.1104174Z >>> 2024-08-06T21:24:13.1104389Z >>> if self.flatten_sharded_tensors: 2024-08-06T21:24:13.1104655Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-08-06T21:24:13.1104788Z >>> 2024-08-06T21:24:13.1104985Z >>> if self.flatten_state_dict: 2024-08-06T21:24:13.1105309Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-08-06T21:24:13.1105460Z >>> 2024-08-06T21:24:13.1105643Z >>> self.state_dict = state_dict 2024-08-06T21:24:13.1105823Z >>> self.metadata = metadata 2024-08-06T21:24:13.1106040Z >>> self.is_coordinator = is_coordinator 2024-08-06T21:24:13.1106174Z >>> 2024-08-06T21:24:13.1106381Z >>> def load_bytes(self, read_item, value): 2024-08-06T21:24:13.1106575Z >>> # Remove the "foo_" prefix 2024-08-06T21:24:13.1107226Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-08-06T21:24:13.1107367Z 2024-08-06T21:24:13.1107518Z 2024-08-06T21:24:13.1108059Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-08-06T21:24:13.1108195Z 2024-08-06T21:24:13.1108398Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.1108660Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-08-06T21:24:13.1108859Z >>> def resolve_tensor(self, read_item): 2024-08-06T21:24:13.1109093Z >>> tensor = super().resolve_tensor(read_item) 2024-08-06T21:24:13.1109346Z >>> return torch.empty_like(tensor, device="cpu") 2024-08-06T21:24:13.1109486Z >>> 2024-08-06T21:24:13.1109718Z >>> def commit_tensor(self, read_item, tensor): 2024-08-06T21:24:13.1109983Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-08-06T21:24:13.1110125Z 2024-08-06T21:24:13.1110592Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1110730Z 2024-08-06T21:24:13.1110895Z warnings.warn(msg) 2024-08-06T21:24:13.1111045Z 2024-08-06T21:24:13.1111380Z --- Parse Warning: 35 / 100 --- 2024-08-06T21:24:13.1113205Z /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. 2024-08-06T21:24:13.1113677Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1113810Z 2024-08-06T21:24:13.1114067Z Load a distributed ``state_dict`` in SPMD style. 2024-08-06T21:24:13.1114207Z 2024-08-06T21:24:13.1114521Z Each rank will try to read the least amount of data necessary 2024-08-06T21:24:13.1114963Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-08-06T21:24:13.1115408Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-08-06T21:24:13.1115578Z 2024-08-06T21:24:13.1116045Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-06T21:24:13.1116511Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-08-06T21:24:13.1116797Z ``load_state_dict`` once the deserialization is complete. 2024-08-06T21:24:13.1116949Z 2024-08-06T21:24:13.1117102Z .. warning:: 2024-08-06T21:24:13.1117399Z All tensors in ``state_dict`` must be allocated on their 2024-08-06T21:24:13.1117683Z destination device *prior to* calling this function. 2024-08-06T21:24:13.1117822Z 2024-08-06T21:24:13.1118230Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-08-06T21:24:13.1118384Z on state_dict. 2024-08-06T21:24:13.1118517Z 2024-08-06T21:24:13.1118713Z .. warning:: 2024-08-06T21:24:13.1119071Z Users must call `load_state_dict` on the root module to ensure load 2024-08-06T21:24:13.1119380Z pos-processing and non-tensor data properly propagates. 2024-08-06T21:24:13.1119532Z 2024-08-06T21:24:13.1119671Z .. note: 2024-08-06T21:24:13.1120068Z If no process group is initialized, this function will assume the intent 2024-08-06T21:24:13.1120471Z is to load a checkpoint into the local process. This can be useful in the 2024-08-06T21:24:13.1120905Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-08-06T21:24:13.1121067Z or ShardedTensor) 2024-08-06T21:24:13.1121218Z 2024-08-06T21:24:13.1121359Z .. note: 2024-08-06T21:24:13.1121593Z Rank 0 is assumed to be the coordinator rank. 2024-08-06T21:24:13.1121743Z 2024-08-06T21:24:13.1121880Z Args: 2024-08-06T21:24:13.1122139Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-06T21:24:13.1122396Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-06T21:24:13.1122765Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-06T21:24:13.1123124Z depends on the storage. It can be a path to a folder or to a file. 2024-08-06T21:24:13.1123481Z It can also be a key if the storage is a key-value store. 2024-08-06T21:24:13.1123645Z (Default: ``None``) 2024-08-06T21:24:13.1123878Z storage_reader (Optional[StorageReader]): 2024-08-06T21:24:13.1124233Z Instance of StorageWriter used to perform reads. If this is not 2024-08-06T21:24:13.1124586Z specified, DCP will automatically infer the reader based on the 2024-08-06T21:24:13.1124949Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-06T21:24:13.1125136Z be raised. (Default: ``None``) 2024-08-06T21:24:13.1125331Z planner (Optional[LoadPlanner]): 2024-08-06T21:24:13.1125699Z Instance of LoadPlanner. If this is not specificed, the default 2024-08-06T21:24:13.1125911Z planner will be used. (Default: ``None``) 2024-08-06T21:24:13.1126133Z process_group (Optional[ProcessGroup]): 2024-08-06T21:24:13.1126461Z ProcessGroup to be used for cross-rank synchronization. 2024-08-06T21:24:13.1126622Z (Default: ``None``) 2024-08-06T21:24:13.1126764Z 2024-08-06T21:24:13.1126920Z Returns: 2024-08-06T21:24:13.1127060Z None. 2024-08-06T21:24:13.1127197Z 2024-08-06T21:24:13.1127358Z Examples 2024-08-06T21:24:13.1127522Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1127686Z >>> my_model = MyModule() 2024-08-06T21:24:13.1127925Z >>> optimizer = Adagrad(my_model.parameters()) 2024-08-06T21:24:13.1128143Z >>> model_state_dict = my_model.state_dict() 2024-08-06T21:24:13.1128691Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-08-06T21:24:13.1128832Z 2024-08-06T21:24:13.1129093Z >>> torch.distributed.checkpoint.load_state_dict( 2024-08-06T21:24:13.1129289Z >>> state_dict=model_state_dict, 2024-08-06T21:24:13.1129527Z >>> storage_reader=fs_storage_reader, 2024-08-06T21:24:13.1129670Z >>> ) 2024-08-06T21:24:13.1129823Z 2024-08-06T21:24:13.1130164Z >>> # module.load_state_dict() function might have customized steps 2024-08-06T21:24:13.1130375Z >>> # to flush the state_dict, must call it to 2024-08-06T21:24:13.1130566Z >>> # ensure correct behavior. 2024-08-06T21:24:13.1130785Z >>> my_model.load_state_dict(model_state_dict) 2024-08-06T21:24:13.1130922Z 2024-08-06T21:24:13.1131080Z .. note:: 2024-08-06T21:24:13.1131453Z load_state_dict uses collectives to coordinate reads across ranks. 2024-08-06T21:24:13.1131827Z For NCCL-based process groups, internal tensor representations of 2024-08-06T21:24:13.1132244Z objects must be moved to the GPU device before communication takes place. 2024-08-06T21:24:13.1132662Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-06T21:24:13.1133060Z and it is the user's responsibility to ensure that this is set so that each 2024-08-06T21:24:13.1133393Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-06T21:24:13.1133530Z 2024-08-06T21:24:13.1133987Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1134122Z 2024-08-06T21:24:13.1134301Z warnings.warn(msg) 2024-08-06T21:24:13.1134434Z 2024-08-06T21:24:13.1134758Z --- Parse Warning: 36 / 100 --- 2024-08-06T21:24:13.1136566Z /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. 2024-08-06T21:24:13.1137037Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1137189Z 2024-08-06T21:24:13.1137395Z Save a distributed model in SPMD style. 2024-08-06T21:24:13.1137539Z 2024-08-06T21:24:13.1137884Z This function is different from ``torch.save()`` as it handles 2024-08-06T21:24:13.1138406Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-08-06T21:24:13.1138540Z 2024-08-06T21:24:13.1139004Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-06T21:24:13.1139258Z save will call ``state_dict`` before serialization. 2024-08-06T21:24:13.1139393Z 2024-08-06T21:24:13.1139553Z .. warning:: 2024-08-06T21:24:13.1139963Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-08-06T21:24:13.1140130Z for saved state_dicts. 2024-08-06T21:24:13.1140282Z 2024-08-06T21:24:13.1140428Z .. warning:: 2024-08-06T21:24:13.1140792Z If using the `process_group` argument, make sure that only its ranks 2024-08-06T21:24:13.1141162Z call `save_state_dict` and that all data in state_dict belong to it. 2024-08-06T21:24:13.1141304Z 2024-08-06T21:24:13.1141458Z .. note:: 2024-08-06T21:24:13.1141920Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-08-06T21:24:13.1142528Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-08-06T21:24:13.1142719Z group needs to be passed in. 2024-08-06T21:24:13.1142855Z 2024-08-06T21:24:13.1143003Z .. note:: 2024-08-06T21:24:13.1143494Z If no process group is available, this function assumes the intention is to save the 2024-08-06T21:24:13.1143680Z state_dict in the local process. 2024-08-06T21:24:13.1143813Z 2024-08-06T21:24:13.1143964Z .. note: 2024-08-06T21:24:13.1144194Z Rank 0 is assumed to be the coordinator rank. 2024-08-06T21:24:13.1144328Z 2024-08-06T21:24:13.1144481Z 2024-08-06T21:24:13.1144619Z Args: 2024-08-06T21:24:13.1144877Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-06T21:24:13.1145208Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-06T21:24:13.1145579Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-06T21:24:13.1145925Z depends on the storage. It can be a path to a folder or to a file. 2024-08-06T21:24:13.1146221Z It can also be a key if the storage is a key-value store. 2024-08-06T21:24:13.1146379Z (Default: ``None``) 2024-08-06T21:24:13.1146650Z storage_writer (Optional[StorageWriter]): 2024-08-06T21:24:13.1147030Z Instance of StorageWriter used to perform writes. If this is not 2024-08-06T21:24:13.1147386Z specified, DCP will automatically infer the writer based on the 2024-08-06T21:24:13.1147752Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-06T21:24:13.1147999Z be raised. (Default: ``None``) 2024-08-06T21:24:13.1148194Z planner (Optional[SavePlanner]): 2024-08-06T21:24:13.1148563Z Instance of SavePlanner. If this is not specificed, the default 2024-08-06T21:24:13.1148771Z planner will be used. (Default: ``None``) 2024-08-06T21:24:13.1148990Z process_group (Optional[ProcessGroup]): 2024-08-06T21:24:13.1149312Z ProcessGroup to be used for cross-rank synchronization. 2024-08-06T21:24:13.1149474Z (Default: ``None``) 2024-08-06T21:24:13.1149607Z 2024-08-06T21:24:13.1149762Z Returns: 2024-08-06T21:24:13.1150029Z Metadata: Metadata object for the saved checkpoint. 2024-08-06T21:24:13.1150164Z 2024-08-06T21:24:13.1150320Z Example: 2024-08-06T21:24:13.1150483Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1150647Z >>> my_model = MyModule() 2024-08-06T21:24:13.1150801Z 2024-08-06T21:24:13.1150986Z >>> state_dict = {"model": my_model} 2024-08-06T21:24:13.1151122Z 2024-08-06T21:24:13.1151664Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-06T21:24:13.1151880Z >>> torch.distributed.checkpoint.save( 2024-08-06T21:24:13.1152051Z >>> state_dict=state_dict, 2024-08-06T21:24:13.1152361Z >>> storage_writer=fs_storage_writer, 2024-08-06T21:24:13.1152504Z >>> ) 2024-08-06T21:24:13.1152633Z 2024-08-06T21:24:13.1152791Z .. note:: 2024-08-06T21:24:13.1153168Z save_state_dict uses collectives to coordinate writes across ranks. 2024-08-06T21:24:13.1153548Z For NCCL-based process groups, internal tensor representations of 2024-08-06T21:24:13.1153949Z objects must be moved to the GPU device before communication takes place. 2024-08-06T21:24:13.1154332Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-06T21:24:13.1154702Z and it is the user's responsibility to ensure that this is set so that 2024-08-06T21:24:13.1155049Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-06T21:24:13.1155186Z 2024-08-06T21:24:13.1155646Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1155784Z 2024-08-06T21:24:13.1155949Z warnings.warn(msg) 2024-08-06T21:24:13.1156099Z 2024-08-06T21:24:13.1156439Z --- Parse Warning: 37 / 100 --- 2024-08-06T21:24:13.1158286Z /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. 2024-08-06T21:24:13.1158769Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1159238Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-08-06T21:24:13.1159758Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-08-06T21:24:13.1159896Z 2024-08-06T21:24:13.1160041Z .. warning:: 2024-08-06T21:24:13.1160393Z This feature is experimental and subject to change. 2024-08-06T21:24:13.1160524Z 2024-08-06T21:24:13.1160662Z Args: 2024-08-06T21:24:13.1160936Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-06T21:24:13.1161180Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-06T21:24:13.1161549Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-06T21:24:13.1161921Z depends on the storage. It can be a path to a folder or to a file. 2024-08-06T21:24:13.1162211Z It can also be a key if the storage is a key-value store. 2024-08-06T21:24:13.1162371Z (Default: ``None``) 2024-08-06T21:24:13.1162607Z storage_writer (Optional[StorageWriter]): 2024-08-06T21:24:13.1163016Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-08-06T21:24:13.1163447Z this is not specified, DCP will automatically infer the writer based on the 2024-08-06T21:24:13.1163808Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-06T21:24:13.1164000Z be raised. (Default: ``None``) 2024-08-06T21:24:13.1164213Z planner (Optional[SavePlanner]): 2024-08-06T21:24:13.1164572Z Instance of SavePlanner. If this is not specificed, the default 2024-08-06T21:24:13.1164789Z planner will be used. (Default: ``None``) 2024-08-06T21:24:13.1165018Z process_group (Optional[ProcessGroup]): 2024-08-06T21:24:13.1165331Z ProcessGroup to be used for cross-rank synchronization. 2024-08-06T21:24:13.1165491Z (Default: ``None``) 2024-08-06T21:24:13.1165641Z 2024-08-06T21:24:13.1165785Z Returns: 2024-08-06T21:24:13.1166153Z Future: A future holding the resultant Metadata object from `save`. 2024-08-06T21:24:13.1166299Z 2024-08-06T21:24:13.1166440Z Example: 2024-08-06T21:24:13.1166601Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1166781Z >>> my_model = MyModule() 2024-08-06T21:24:13.1166916Z 2024-08-06T21:24:13.1167155Z >>> state_dict = {"model": my_model} 2024-08-06T21:24:13.1167305Z 2024-08-06T21:24:13.1167831Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-06T21:24:13.1168198Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-08-06T21:24:13.1168386Z >>> state_dict=state_dict, 2024-08-06T21:24:13.1168585Z >>> storage_writer=fs_storage_writer, 2024-08-06T21:24:13.1168746Z >>> ) 2024-08-06T21:24:13.1168885Z >>> 2024-08-06T21:24:13.1169046Z >>> # ... do some work ... 2024-08-06T21:24:13.1169202Z >>> 2024-08-06T21:24:13.1169388Z >>> checkpoint_future.result() 2024-08-06T21:24:13.1169521Z 2024-08-06T21:24:13.1169669Z 2024-08-06T21:24:13.1170120Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1170261Z 2024-08-06T21:24:13.1170440Z warnings.warn(msg) 2024-08-06T21:24:13.1170574Z 2024-08-06T21:24:13.1170889Z --- Parse Warning: 38 / 100 --- 2024-08-06T21:24:13.1172853Z /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. 2024-08-06T21:24:13.1173322Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1173460Z 2024-08-06T21:24:13.1173827Z Initialize rendezvous event object and record its operations. 2024-08-06T21:24:13.1173957Z 2024-08-06T21:24:13.1174100Z Args: 2024-08-06T21:24:13.1174328Z run_id (str): The run id of the rendezvous. 2024-08-06T21:24:13.1174576Z message (str): The message describing the event. 2024-08-06T21:24:13.1175058Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-08-06T21:24:13.1175389Z name (str): Event name. (E.g. Current action being performed). 2024-08-06T21:24:13.1175578Z hostname (str): Hostname of the node. 2024-08-06T21:24:13.1175822Z pid (Optional[int]): The process id of the node. 2024-08-06T21:24:13.1176249Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-08-06T21:24:13.1176716Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-08-06T21:24:13.1176994Z rank (Optional[int]): The rank of the node, if known. 2024-08-06T21:24:13.1177132Z Returns: 2024-08-06T21:24:13.1177270Z None 2024-08-06T21:24:13.1177457Z Example: 2024-08-06T21:24:13.1177667Z >>> # See DynamicRendezvousHandler class 2024-08-06T21:24:13.1177814Z >>> def _record( 2024-08-06T21:24:13.1177969Z ... self, 2024-08-06T21:24:13.1178117Z ... message: str, 2024-08-06T21:24:13.1178353Z ... node_state: NodeState = NodeState.RUNNING, 2024-08-06T21:24:13.1178543Z ... rank: Optional[int] = None, 2024-08-06T21:24:13.1178690Z ... ) -> None: 2024-08-06T21:24:13.1178888Z ... construct_and_record_rdzv_event( 2024-08-06T21:24:13.1179178Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-08-06T21:24:13.1179372Z ... run_id=self._settings.run_id, 2024-08-06T21:24:13.1179529Z ... message=message, 2024-08-06T21:24:13.1179711Z ... node_state=node_state, 2024-08-06T21:24:13.1179910Z ... hostname=self._this_node.addr, 2024-08-06T21:24:13.1180087Z ... pid=self._this_node.pid, 2024-08-06T21:24:13.1180305Z ... local_id=self._this_node.local_id, 2024-08-06T21:24:13.1180455Z ... rank=rank, 2024-08-06T21:24:13.1180611Z ... ) 2024-08-06T21:24:13.1180752Z 2024-08-06T21:24:13.1181258Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1181406Z 2024-08-06T21:24:13.1181564Z warnings.warn(msg) 2024-08-06T21:24:13.1181696Z 2024-08-06T21:24:13.1182018Z --- Parse Warning: 39 / 100 --- 2024-08-06T21:24:13.1183734Z /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. 2024-08-06T21:24:13.1184198Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1184344Z 2024-08-06T21:24:13.1184631Z This configures FSDP-native mixed precision training. 2024-08-06T21:24:13.1184773Z 2024-08-06T21:24:13.1184934Z Attributes: 2024-08-06T21:24:13.1185347Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-08-06T21:24:13.1185702Z parameters during forward and backward and thus the dtype for 2024-08-06T21:24:13.1186111Z forward and backward computation. Outside forward and backward, the 2024-08-06T21:24:13.1186450Z *sharded* parameters are kept in full precision (e.g. for the 2024-08-06T21:24:13.1186887Z optimizer step), and for model checkpointing, the parameters are 2024-08-06T21:24:13.1187153Z always saved in full precision. (Default: ``None``) 2024-08-06T21:24:13.1187533Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-06T21:24:13.1187928Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-08-06T21:24:13.1188236Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-08-06T21:24:13.1188595Z the ``param_dtype`` value, still running gradient reduction in low 2024-08-06T21:24:13.1188982Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-08-06T21:24:13.1189370Z to force gradient reduction to run in full precision. (Default: 2024-08-06T21:24:13.1189522Z ``None``) 2024-08-06T21:24:13.1189910Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-06T21:24:13.1190260Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-08-06T21:24:13.1190606Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-08-06T21:24:13.1190994Z dtype thereafter. For model checkpointing, the buffers are saved 2024-08-06T21:24:13.1191312Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-08-06T21:24:13.1191471Z ``None``) 2024-08-06T21:24:13.1191820Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-08-06T21:24:13.1192238Z gradients to full precision after the backward pass in preparation 2024-08-06T21:24:13.1192614Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-08-06T21:24:13.1192974Z in the dtype used for gradient reduction, which can save memory if 2024-08-06T21:24:13.1193333Z using a custom optimizer that supports running in low precision. 2024-08-06T21:24:13.1193513Z (Default: ``False``) 2024-08-06T21:24:13.1193889Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-08-06T21:24:13.1194243Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-08-06T21:24:13.1194633Z that parameter and input dtypes match for forward computation, as 2024-08-06T21:24:13.1195001Z required by many ops. This may need to be set to ``True`` when only 2024-08-06T21:24:13.1195407Z applying mixed precision to some but not all FSDP modules, in which 2024-08-06T21:24:13.1195775Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-08-06T21:24:13.1195943Z (Default: ``False``) 2024-08-06T21:24:13.1196352Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-08-06T21:24:13.1196767Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-08-06T21:24:13.1197100Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-08-06T21:24:13.1197356Z this does not do anything. (Default: ``True``) 2024-08-06T21:24:13.1197739Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-08-06T21:24:13.1198060Z module classes to ignore for mixed precision when using an 2024-08-06T21:24:13.1198390Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-08-06T21:24:13.1198758Z applied to them separately with mixed precision disabled (meaning 2024-08-06T21:24:13.1199125Z that the final FSDP construction would deviate from the specified 2024-08-06T21:24:13.1199457Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-08-06T21:24:13.1199809Z not do anything. This API is experimental and subject to change. 2024-08-06T21:24:13.1200015Z (Default: ``(_BatchNorm,)``) 2024-08-06T21:24:13.1200160Z 2024-08-06T21:24:13.1200449Z .. note:: This API is experimental and subject to change. 2024-08-06T21:24:13.1200608Z 2024-08-06T21:24:13.1200996Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-08-06T21:24:13.1201141Z 2024-08-06T21:24:13.1201468Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-08-06T21:24:13.1201658Z precision, but buffers are not. 2024-08-06T21:24:13.1201802Z 2024-08-06T21:24:13.1202174Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-08-06T21:24:13.1202542Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-08-06T21:24:13.1202954Z Disabling FSDP's mixed precision for those norm modules only means that 2024-08-06T21:24:13.1203389Z the affine parameters are kept in ``float32``. However, this incurs 2024-08-06T21:24:13.1203809Z separate all-gathers and reduce-scatters for those norm modules, which 2024-08-06T21:24:13.1204197Z may be inefficient, so if the workload permits, the user should prefer 2024-08-06T21:24:13.1204445Z to still apply mixed precision to those modules. 2024-08-06T21:24:13.1204584Z 2024-08-06T21:24:13.1204947Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-08-06T21:24:13.1205306Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-08-06T21:24:13.1205709Z modules will have FSDP applied to them separately with mixed precision 2024-08-06T21:24:13.1206022Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-08-06T21:24:13.1206193Z 2024-08-06T21:24:13.1206556Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-08-06T21:24:13.1206952Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-08-06T21:24:13.1207251Z its ``cast_root_forward_inputs`` takes precedence over its 2024-08-06T21:24:13.1207564Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-08-06T21:24:13.1207945Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-08-06T21:24:13.1208336Z sufficient for the typical case where each FSDP instance has the same 2024-08-06T21:24:13.1208749Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-08-06T21:24:13.1209061Z ``param_dtype`` at the beginning of the model's forward pass. 2024-08-06T21:24:13.1209200Z 2024-08-06T21:24:13.1209567Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-08-06T21:24:13.1209985Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-08-06T21:24:13.1210340Z values to configure casting inputs or not before each instance's 2024-08-06T21:24:13.1210692Z forward. In such a case, since the casts happen before each FSDP 2024-08-06T21:24:13.1211131Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-08-06T21:24:13.1211560Z submodules run before its FSDP submodules to avoid the activation dtype 2024-08-06T21:24:13.1211923Z being changed due to a different ``MixedPrecision`` configuration. 2024-08-06T21:24:13.1212064Z 2024-08-06T21:24:13.1212229Z Example:: 2024-08-06T21:24:13.1212367Z 2024-08-06T21:24:13.1212583Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.1212883Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-08-06T21:24:13.1213037Z >>> model[1] = FSDP( 2024-08-06T21:24:13.1213194Z >>> model[1], 2024-08-06T21:24:13.1213758Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-08-06T21:24:13.1213904Z >>> ) 2024-08-06T21:24:13.1214060Z >>> model = FSDP( 2024-08-06T21:24:13.1214220Z >>> model, 2024-08-06T21:24:13.1214781Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-08-06T21:24:13.1214923Z >>> ) 2024-08-06T21:24:13.1215078Z 2024-08-06T21:24:13.1215446Z The above shows a working example. On the other hand, if ``model[1]`` 2024-08-06T21:24:13.1215796Z were replaced with ``model[0]``, meaning that the submodule using 2024-08-06T21:24:13.1216207Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-08-06T21:24:13.1216594Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-08-06T21:24:13.1216747Z ones. 2024-08-06T21:24:13.1216888Z 2024-08-06T21:24:13.1217028Z 2024-08-06T21:24:13.1217492Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1217664Z 2024-08-06T21:24:13.1217827Z warnings.warn(msg) 2024-08-06T21:24:13.1217978Z 2024-08-06T21:24:13.1218329Z --- Parse Warning: 40 / 100 --- 2024-08-06T21:24:13.1220555Z /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=648. 2024-08-06T21:24:13.1221039Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1221482Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-06T21:24:13.1221626Z 2024-08-06T21:24:13.1222119Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-08-06T21:24:13.1222520Z The target module does not have to be a FSDP module. If the target 2024-08-06T21:24:13.1222892Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-08-06T21:24:13.1223050Z 2024-08-06T21:24:13.1223391Z .. note:: This API should be called for only the top-level (root) 2024-08-06T21:24:13.1223549Z module. 2024-08-06T21:24:13.1223688Z 2024-08-06T21:24:13.1224052Z .. note:: This API enables users to transparently use the conventional 2024-08-06T21:24:13.1224392Z ``state_dict`` API to take model checkpoints in cases where the 2024-08-06T21:24:13.1224759Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-08-06T21:24:13.1225122Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-08-06T21:24:13.1225550Z instances, while dispatching into `sharded_state_dict` implementation 2024-08-06T21:24:13.1225702Z for FSDP: 2024-08-06T21:24:13.1225846Z 2024-08-06T21:24:13.1226012Z Example:: 2024-08-06T21:24:13.1226151Z 2024-08-06T21:24:13.1226374Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.1226703Z >>> model = DDP(FSDP(...)) 2024-08-06T21:24:13.1226897Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:13.1227048Z >>> model, 2024-08-06T21:24:13.1227285Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-06T21:24:13.1227663Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-08-06T21:24:13.1228079Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-08-06T21:24:13.1228222Z >>> ) 2024-08-06T21:24:13.1228433Z >>> param_state_dict = model.state_dict() 2024-08-06T21:24:13.1228742Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-06T21:24:13.1228890Z 2024-08-06T21:24:13.1229033Z Args: 2024-08-06T21:24:13.1229253Z module (torch.nn.Module): Root module. 2024-08-06T21:24:13.1229676Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-06T21:24:13.1230102Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-08-06T21:24:13.1230304Z target ``state_dict_type``. 2024-08-06T21:24:13.1230756Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-08-06T21:24:13.1230949Z for the optimizer state dict. 2024-08-06T21:24:13.1231102Z 2024-08-06T21:24:13.1231245Z Returns: 2024-08-06T21:24:13.1231644Z A StateDictSettings that include the previous state_dict type and 2024-08-06T21:24:13.1231855Z configuration for the module. 2024-08-06T21:24:13.1231998Z 2024-08-06T21:24:13.1232464Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1232599Z 2024-08-06T21:24:13.1232794Z warnings.warn(msg) 2024-08-06T21:24:13.1232942Z 2024-08-06T21:24:13.1233270Z --- Parse Warning: 41 / 100 --- 2024-08-06T21:24:13.1235460Z /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=804. 2024-08-06T21:24:13.1235938Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1236376Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-06T21:24:13.1236518Z 2024-08-06T21:24:13.1237103Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-08-06T21:24:13.1237355Z :meth:`set_state_dict_type` for the detail. 2024-08-06T21:24:13.1237490Z 2024-08-06T21:24:13.1237653Z Example:: 2024-08-06T21:24:13.1237794Z 2024-08-06T21:24:13.1238011Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.1238211Z >>> model = DDP(FSDP(...)) 2024-08-06T21:24:13.1238401Z >>> with FSDP.state_dict_type( 2024-08-06T21:24:13.1238564Z >>> model, 2024-08-06T21:24:13.1238776Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-06T21:24:13.1238919Z >>> ): 2024-08-06T21:24:13.1239136Z >>> checkpoint = model.state_dict() 2024-08-06T21:24:13.1239275Z 2024-08-06T21:24:13.1239416Z Args: 2024-08-06T21:24:13.1239640Z module (torch.nn.Module): Root module. 2024-08-06T21:24:13.1240059Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-06T21:24:13.1240468Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-08-06T21:24:13.1240752Z configuration for the target ``state_dict_type``. 2024-08-06T21:24:13.1241172Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-08-06T21:24:13.1241576Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-08-06T21:24:13.1241728Z 2024-08-06T21:24:13.1242182Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1242319Z 2024-08-06T21:24:13.1242716Z warnings.warn(msg) 2024-08-06T21:24:13.1242851Z 2024-08-06T21:24:13.1243177Z --- Parse Warning: 42 / 100 --- 2024-08-06T21:24:13.1245407Z /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=1801. 2024-08-06T21:24:13.1245884Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1246037Z 2024-08-06T21:24:13.1246464Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-08-06T21:24:13.1246611Z 2024-08-06T21:24:13.1246960Z The given state-dict can be transformed to one of three types: 2024-08-06T21:24:13.1247498Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-08-06T21:24:13.1247640Z 2024-08-06T21:24:13.1248069Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-08-06T21:24:13.1248445Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-08-06T21:24:13.1248595Z avoid OOM. 2024-08-06T21:24:13.1248761Z 2024-08-06T21:24:13.1249180Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-08-06T21:24:13.1249546Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-08-06T21:24:13.1249705Z memory. 2024-08-06T21:24:13.1249950Z 2024-08-06T21:24:13.1250329Z For local state_dict, no transformation will be performed. But a state 2024-08-06T21:24:13.1250774Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-08-06T21:24:13.1250968Z nature (this is not supported yet). 2024-08-06T21:24:13.1251121Z 2024-08-06T21:24:13.1251272Z Example:: 2024-08-06T21:24:13.1251409Z 2024-08-06T21:24:13.1251640Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.1252055Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-06T21:24:13.1252319Z >>> from torch.distributed.fsdp import StateDictType 2024-08-06T21:24:13.1252640Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-06T21:24:13.1252988Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-06T21:24:13.1253211Z >>> # Save a checkpoint 2024-08-06T21:24:13.1253396Z >>> model, optim = ... 2024-08-06T21:24:13.1253582Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:13.1253728Z >>> model, 2024-08-06T21:24:13.1253953Z >>> StateDictType.FULL_STATE_DICT, 2024-08-06T21:24:13.1254171Z >>> FullStateDictConfig(rank0_only=False), 2024-08-06T21:24:13.1254416Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-06T21:24:13.1254578Z >>> ) 2024-08-06T21:24:13.1254767Z >>> state_dict = model.state_dict() 2024-08-06T21:24:13.1255055Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-06T21:24:13.1255315Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-06T21:24:13.1255481Z >>> # Load a checkpoint 2024-08-06T21:24:13.1255642Z >>> model, optim = ... 2024-08-06T21:24:13.1255916Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-06T21:24:13.1256099Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:13.1256258Z >>> model, 2024-08-06T21:24:13.1256460Z >>> StateDictType.FULL_STATE_DICT, 2024-08-06T21:24:13.1256680Z >>> FullStateDictConfig(rank0_only=False), 2024-08-06T21:24:13.1257034Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-06T21:24:13.1257171Z >>> ) 2024-08-06T21:24:13.1257367Z >>> model.load_state_dict(state_dict) 2024-08-06T21:24:13.1257635Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-06T21:24:13.1257829Z >>> model, optim, optim_state_dict 2024-08-06T21:24:13.1257969Z >>> ) 2024-08-06T21:24:13.1258200Z >>> optim.load_state_dict(optim_state_dict) 2024-08-06T21:24:13.1258340Z 2024-08-06T21:24:13.1258486Z Args: 2024-08-06T21:24:13.1258842Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-06T21:24:13.1259189Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-06T21:24:13.1259417Z were passed into the optimizer ``optim``. 2024-08-06T21:24:13.1259748Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-06T21:24:13.1259907Z parameters. 2024-08-06T21:24:13.1260284Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-08-06T21:24:13.1260665Z transform. If the value is None, optim.state_dict() will be used. ( 2024-08-06T21:24:13.1260830Z Default: ``None``) 2024-08-06T21:24:13.1261266Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-06T21:24:13.1261583Z are sharded or ``None`` if using the default process group. ( 2024-08-06T21:24:13.1261743Z Default: ``None``) 2024-08-06T21:24:13.1261900Z 2024-08-06T21:24:13.1262039Z Returns: 2024-08-06T21:24:13.1262367Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-08-06T21:24:13.1262667Z ``model``. The sharding of the optimizer state is based on 2024-08-06T21:24:13.1262832Z ``state_dict_type``. 2024-08-06T21:24:13.1262970Z 2024-08-06T21:24:13.1263472Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1263611Z 2024-08-06T21:24:13.1263779Z warnings.warn(msg) 2024-08-06T21:24:13.1263925Z 2024-08-06T21:24:13.1264252Z --- Parse Warning: 43 / 100 --- 2024-08-06T21:24:13.1266514Z /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=1899. 2024-08-06T21:24:13.1267078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1267219Z 2024-08-06T21:24:13.1267876Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-08-06T21:24:13.1268054Z 2024-08-06T21:24:13.1268344Z Given a ``optim_state_dict`` that is transformed through 2024-08-06T21:24:13.1268733Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-08-06T21:24:13.1269108Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-08-06T21:24:13.1269431Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-08-06T21:24:13.1269581Z 2024-08-06T21:24:13.1269798Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.1270208Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-06T21:24:13.1270488Z >>> from torch.distributed.fsdp import StateDictType 2024-08-06T21:24:13.1270794Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-06T21:24:13.1271143Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-06T21:24:13.1271318Z >>> # Save a checkpoint 2024-08-06T21:24:13.1271480Z >>> model, optim = ... 2024-08-06T21:24:13.1271660Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:13.1271821Z >>> model, 2024-08-06T21:24:13.1272023Z >>> StateDictType.FULL_STATE_DICT, 2024-08-06T21:24:13.1272305Z >>> FullStateDictConfig(rank0_only=False), 2024-08-06T21:24:13.1272564Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-06T21:24:13.1272711Z >>> ) 2024-08-06T21:24:13.1272911Z >>> state_dict = model.state_dict() 2024-08-06T21:24:13.1273107Z >>> original_osd = optim.state_dict() 2024-08-06T21:24:13.1273324Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-08-06T21:24:13.1273480Z >>> model, 2024-08-06T21:24:13.1273622Z >>> optim, 2024-08-06T21:24:13.1273815Z >>> optim_state_dict=original_osd 2024-08-06T21:24:13.1273970Z >>> ) 2024-08-06T21:24:13.1274218Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-06T21:24:13.1274382Z >>> # Load a checkpoint 2024-08-06T21:24:13.1274563Z >>> model, optim = ... 2024-08-06T21:24:13.1274822Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-06T21:24:13.1275002Z >>> FSDP.set_state_dict_type( 2024-08-06T21:24:13.1275166Z >>> model, 2024-08-06T21:24:13.1275365Z >>> StateDictType.FULL_STATE_DICT, 2024-08-06T21:24:13.1275583Z >>> FullStateDictConfig(rank0_only=False), 2024-08-06T21:24:13.1275838Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-06T21:24:13.1275980Z >>> ) 2024-08-06T21:24:13.1276174Z >>> model.load_state_dict(state_dict) 2024-08-06T21:24:13.1276440Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-06T21:24:13.1276633Z >>> model, optim, optim_state_dict 2024-08-06T21:24:13.1276773Z >>> ) 2024-08-06T21:24:13.1277001Z >>> optim.load_state_dict(optim_state_dict) 2024-08-06T21:24:13.1277142Z 2024-08-06T21:24:13.1277283Z Args: 2024-08-06T21:24:13.1277635Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-06T21:24:13.1278015Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-06T21:24:13.1278256Z were passed into the optimizer ``optim``. 2024-08-06T21:24:13.1278566Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-06T21:24:13.1278714Z parameters. 2024-08-06T21:24:13.1279101Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-08-06T21:24:13.1279453Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-08-06T21:24:13.1279792Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-08-06T21:24:13.1280112Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-08-06T21:24:13.1280448Z load_directly (bool): If this is set to True, this API will also 2024-08-06T21:24:13.1280832Z call optim.load_state_dict(result) before returning the result. 2024-08-06T21:24:13.1281236Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-08-06T21:24:13.1281397Z (Default: ``False``) 2024-08-06T21:24:13.1281827Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-06T21:24:13.1282163Z are sharded or ``None`` if using the default process group. ( 2024-08-06T21:24:13.1282323Z Default: ``None``) 2024-08-06T21:24:13.1282475Z 2024-08-06T21:24:13.1282921Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1283051Z 2024-08-06T21:24:13.1283225Z warnings.warn(msg) 2024-08-06T21:24:13.1283361Z 2024-08-06T21:24:13.1283687Z --- Parse Warning: 44 / 100 --- 2024-08-06T21:24:13.1285569Z /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. 2024-08-06T21:24:13.1286048Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1286183Z 2024-08-06T21:24:13.1286661Z RemoteModule instance can only be created after RPC initialization. 2024-08-06T21:24:13.1286797Z 2024-08-06T21:24:13.1287137Z It creates a user-specified module on a specified remote node. 2024-08-06T21:24:13.1287567Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-06T21:24:13.1287748Z executed on the remote node. 2024-08-06T21:24:13.1288164Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-06T21:24:13.1288442Z gradients back to the corresponding remote module. 2024-08-06T21:24:13.1289073Z It can be shared across processors using `RPC framework `__, 2024-08-06T21:24:13.1289431Z without incurring any overheads of copying the actual module, 2024-08-06T21:24:13.1289777Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-08-06T21:24:13.1289962Z pointing to the remote module. 2024-08-06T21:24:13.1290117Z 2024-08-06T21:24:13.1290471Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-08-06T21:24:13.1290817Z the ``forward`` method of the module returned by the ``module_cls``. 2024-08-06T21:24:13.1290966Z 2024-08-06T21:24:13.1291517Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-08-06T21:24:13.1291655Z 2024-08-06T21:24:13.1292114Z Particularly, to create a hybrid model, typically the local modules should be 2024-08-06T21:24:13.1292797Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-08-06T21:24:13.1292949Z Hybrid Example: 2024-08-06T21:24:13.1293154Z >>> class HybridModel(nn.Module): 2024-08-06T21:24:13.1293342Z >>> def __init__(self) -> None: 2024-08-06T21:24:13.1293529Z >>> nn.Module.__init__(self) 2024-08-06T21:24:13.1293813Z >>> self.remote_embedding = RemoteModule(...) 2024-08-06T21:24:13.1294026Z >>> self.local_linear = nn.Linear(...) 2024-08-06T21:24:13.1294179Z 2024-08-06T21:24:13.1294521Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-06T21:24:13.1294963Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-08-06T21:24:13.1295340Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-08-06T21:24:13.1295557Z ``def forward(input: Tensor) -> Tensor:`` and 2024-08-06T21:24:13.1295839Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-08-06T21:24:13.1295992Z 2024-08-06T21:24:13.1296141Z .. note:: 2024-08-06T21:24:13.1296385Z If the remote module is placed on a cuda device, 2024-08-06T21:24:13.1296850Z any input CPU tensors will be automatically moved to the same cuda device, 2024-08-06T21:24:13.1297585Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-08-06T21:24:13.1297721Z 2024-08-06T21:24:13.1297885Z Args: 2024-08-06T21:24:13.1298421Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-06T21:24:13.1298939Z The device can be a local device or a remote device specified by one of the following remote 2024-08-06T21:24:13.1299102Z formats: 2024-08-06T21:24:13.1299241Z 2024-08-06T21:24:13.1299487Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-06T21:24:13.1299740Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-06T21:24:13.1299879Z 2024-08-06T21:24:13.1300324Z In addition, the device field can be optional and the default value is "cpu". 2024-08-06T21:24:13.1300526Z module_cls (nn.Module): For example, 2024-08-06T21:24:13.1300710Z >>> class MyModule(nn.Module): 2024-08-06T21:24:13.1300899Z >>> def forward(input): 2024-08-06T21:24:13.1301070Z >>> return input + 1 2024-08-06T21:24:13.1301270Z >>> 2024-08-06T21:24:13.1301455Z >>> module_cls = MyModule 2024-08-06T21:24:13.1301799Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-06T21:24:13.1302134Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-06T21:24:13.1302649Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-06T21:24:13.1303061Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-06T21:24:13.1303445Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-06T21:24:13.1303878Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-06T21:24:13.1304024Z 2024-08-06T21:24:13.1304182Z Returns: 2024-08-06T21:24:13.1304620Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-06T21:24:13.1305031Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-06T21:24:13.1305533Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-06T21:24:13.1305774Z on the user-provided module on the remote side. 2024-08-06T21:24:13.1305913Z 2024-08-06T21:24:13.1306077Z Example:: 2024-08-06T21:24:13.1306339Z Run the following code in two different processes: 2024-08-06T21:24:13.1306479Z 2024-08-06T21:24:13.1306760Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:13.1306918Z >>> # On worker 0: 2024-08-06T21:24:13.1307074Z >>> import torch 2024-08-06T21:24:13.1307299Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:13.1307485Z >>> from torch import nn, Tensor 2024-08-06T21:24:13.1307872Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-06T21:24:13.1308066Z >>> 2024-08-06T21:24:13.1308300Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-06T21:24:13.1308510Z >>> remote_linear_module = RemoteModule( 2024-08-06T21:24:13.1308730Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-06T21:24:13.1308870Z >>> ) 2024-08-06T21:24:13.1309045Z >>> input = torch.randn(128, 20) 2024-08-06T21:24:13.1309317Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-06T21:24:13.1309482Z >>> ret = ret_fut.wait() 2024-08-06T21:24:13.1309653Z >>> rpc.shutdown() 2024-08-06T21:24:13.1309792Z 2024-08-06T21:24:13.1309941Z >>> # On worker 1: 2024-08-06T21:24:13.1310109Z >>> import torch 2024-08-06T21:24:13.1310318Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:13.1310495Z >>> 2024-08-06T21:24:13.1310745Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-06T21:24:13.1310904Z >>> rpc.shutdown() 2024-08-06T21:24:13.1311041Z 2024-08-06T21:24:13.1311509Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1311650Z 2024-08-06T21:24:13.1311809Z warnings.warn(msg) 2024-08-06T21:24:13.1311968Z 2024-08-06T21:24:13.1312298Z --- Parse Warning: 45 / 100 --- 2024-08-06T21:24:13.1314286Z /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. 2024-08-06T21:24:13.1314778Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1314909Z 2024-08-06T21:24:13.1315496Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-08-06T21:24:13.1315652Z 2024-08-06T21:24:13.1316239Z This alternate initialization method can be particularly useful if we want to create multiple 2024-08-06T21:24:13.1316914Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-08-06T21:24:13.1317048Z 2024-08-06T21:24:13.1317541Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-08-06T21:24:13.1317859Z which is not supported. The recommended way is as follows: 2024-08-06T21:24:13.1317998Z 2024-08-06T21:24:13.1318193Z 1. the sender creates a RemoteModule; 2024-08-06T21:24:13.1318442Z 2. the sender sends its ``module_rref`` over RPC; 2024-08-06T21:24:13.1319055Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-08-06T21:24:13.1319195Z 2024-08-06T21:24:13.1319359Z Example:: 2024-08-06T21:24:13.1319614Z Run the following code in two different processes: 2024-08-06T21:24:13.1319753Z 2024-08-06T21:24:13.1319951Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:13.1320107Z >>> # On worker 0: 2024-08-06T21:24:13.1320260Z >>> import torch 2024-08-06T21:24:13.1320486Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:13.1320668Z >>> from torch import nn, Tensor 2024-08-06T21:24:13.1321052Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-06T21:24:13.1321210Z >>> 2024-08-06T21:24:13.1321445Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-06T21:24:13.1321630Z >>> remote_module = RemoteModule( 2024-08-06T21:24:13.1321852Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-06T21:24:13.1321993Z >>> ) 2024-08-06T21:24:13.1322128Z >>> 2024-08-06T21:24:13.1322328Z >>> remote_module1 = rpc.rpc_sync( 2024-08-06T21:24:13.1322486Z >>> "worker1/cpu", 2024-08-06T21:24:13.1322707Z >>> RemoteModule.init_from_module_rref, 2024-08-06T21:24:13.1322976Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-08-06T21:24:13.1323149Z >>> ) 2024-08-06T21:24:13.1323320Z >>> rpc.shutdown() 2024-08-06T21:24:13.1323459Z 2024-08-06T21:24:13.1323605Z >>> # On worker 1: 2024-08-06T21:24:13.1323770Z >>> import torch 2024-08-06T21:24:13.1323977Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:13.1324115Z >>> 2024-08-06T21:24:13.1324360Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-06T21:24:13.1324515Z >>> rpc.shutdown() 2024-08-06T21:24:13.1324651Z 2024-08-06T21:24:13.1324803Z Args: 2024-08-06T21:24:13.1325334Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-06T21:24:13.1325852Z The device can be a local device or a remote device specified by one of the following remote 2024-08-06T21:24:13.1326043Z formats: 2024-08-06T21:24:13.1326181Z 2024-08-06T21:24:13.1326411Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-06T21:24:13.1326684Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-06T21:24:13.1326824Z 2024-08-06T21:24:13.1327264Z In addition, the device field can be optional and the default value is "cpu". 2024-08-06T21:24:13.1327718Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-08-06T21:24:13.1327900Z the created remote module. 2024-08-06T21:24:13.1328394Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-06T21:24:13.1328820Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-06T21:24:13.1329202Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-06T21:24:13.1329631Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-06T21:24:13.1329774Z 2024-08-06T21:24:13.1329916Z Returns: 2024-08-06T21:24:13.1330363Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-06T21:24:13.1330838Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-08-06T21:24:13.1331325Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-06T21:24:13.1331577Z on the user-provided module on the remote side. 2024-08-06T21:24:13.1331717Z 2024-08-06T21:24:13.1332166Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1332320Z 2024-08-06T21:24:13.1332483Z warnings.warn(msg) 2024-08-06T21:24:13.1332613Z 2024-08-06T21:24:13.1332949Z --- Parse Warning: 46 / 100 --- 2024-08-06T21:24:13.1334770Z /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. 2024-08-06T21:24:13.1335265Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1335406Z 2024-08-06T21:24:13.1335825Z A RemoteModule instance can only be created after RPC initialization. 2024-08-06T21:24:13.1335972Z 2024-08-06T21:24:13.1336312Z It creates a user-specified module on a specified remote node. 2024-08-06T21:24:13.1336727Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-06T21:24:13.1336926Z executed on the remote node. 2024-08-06T21:24:13.1337341Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-06T21:24:13.1337611Z gradients back to the corresponding remote module. 2024-08-06T21:24:13.1337759Z 2024-08-06T21:24:13.1338148Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-08-06T21:24:13.1338524Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-08-06T21:24:13.1339025Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-08-06T21:24:13.1339372Z and ``forward`` are the same as the ``forward`` method of the module 2024-08-06T21:24:13.1339567Z returned by the ``module_cls``. 2024-08-06T21:24:13.1339704Z 2024-08-06T21:24:13.1340046Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-06T21:24:13.1340501Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-08-06T21:24:13.1340900Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-08-06T21:24:13.1341041Z 2024-08-06T21:24:13.1341262Z | ``def forward(input: Tensor) -> Tensor:`` 2024-08-06T21:24:13.1341543Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-08-06T21:24:13.1341711Z 2024-08-06T21:24:13.1341866Z Args: 2024-08-06T21:24:13.1342543Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-06T21:24:13.1343180Z The format should be "/", where the device field can be parsed as torch.device type. 2024-08-06T21:24:13.1343428Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-08-06T21:24:13.1343862Z In addition, the device field can be optional and the default value is "cpu". 2024-08-06T21:24:13.1344305Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-08-06T21:24:13.1344456Z 2024-08-06T21:24:13.1344640Z >>> class MyModule(nn.Module): 2024-08-06T21:24:13.1344824Z >>> def forward(input): 2024-08-06T21:24:13.1344997Z >>> return input + 1 2024-08-06T21:24:13.1345135Z >>> 2024-08-06T21:24:13.1345324Z >>> module_cls = MyModule 2024-08-06T21:24:13.1345463Z 2024-08-06T21:24:13.1345810Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-06T21:24:13.1346159Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-06T21:24:13.1346296Z 2024-08-06T21:24:13.1346549Z Returns: 2024-08-06T21:24:13.1347070Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-06T21:24:13.1347472Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-06T21:24:13.1347957Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-06T21:24:13.1348212Z on the user-provided module on the remote side. 2024-08-06T21:24:13.1348355Z 2024-08-06T21:24:13.1348506Z Example:: 2024-08-06T21:24:13.1348777Z Run the following code in two different processes: 2024-08-06T21:24:13.1348914Z 2024-08-06T21:24:13.1349105Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:13.1349281Z >>> # On worker 0: 2024-08-06T21:24:13.1349437Z >>> import torch 2024-08-06T21:24:13.1349645Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:13.1349847Z >>> from torch import nn, Tensor 2024-08-06T21:24:13.1350236Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-06T21:24:13.1350378Z >>> 2024-08-06T21:24:13.1350625Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-06T21:24:13.1350829Z >>> remote_linear_module = RemoteModule( 2024-08-06T21:24:13.1351049Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-06T21:24:13.1351193Z >>> ) 2024-08-06T21:24:13.1351373Z >>> input = torch.randn(128, 20) 2024-08-06T21:24:13.1351649Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-06T21:24:13.1351812Z >>> ret = ret_fut.wait() 2024-08-06T21:24:13.1351968Z >>> rpc.shutdown() 2024-08-06T21:24:13.1352118Z 2024-08-06T21:24:13.1352266Z >>> # On worker 1: 2024-08-06T21:24:13.1352418Z >>> import torch 2024-08-06T21:24:13.1352643Z >>> import torch.distributed.rpc as rpc 2024-08-06T21:24:13.1352832Z >>> 2024-08-06T21:24:13.1353065Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-06T21:24:13.1353240Z >>> rpc.shutdown() 2024-08-06T21:24:13.1353382Z 2024-08-06T21:24:13.1353717Z Furthermore, a more practical example that is combined with 2024-08-06T21:24:13.1354595Z `DistributedDataParallel `__ (DDP) 2024-08-06T21:24:13.1355202Z can be found in this `tutorial `__. 2024-08-06T21:24:13.1355344Z 2024-08-06T21:24:13.1355807Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1355946Z 2024-08-06T21:24:13.1356108Z warnings.warn(msg) 2024-08-06T21:24:13.1356308Z 2024-08-06T21:24:13.1356657Z --- Parse Warning: 47 / 100 --- 2024-08-06T21:24:13.1358510Z /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. 2024-08-06T21:24:13.1358980Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1359120Z 2024-08-06T21:24:13.1359547Z Custom reducer class that can be used to specify a custom operation that 2024-08-06T21:24:13.1359851Z reduces losses of multiple microbatches into one value. 2024-08-06T21:24:13.1359993Z 2024-08-06T21:24:13.1360152Z Example: 2024-08-06T21:24:13.1360319Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1360504Z >>> sum_reducer = _CustomReducer( 2024-08-06T21:24:13.1360689Z >>> torch.tensor(0.0), 2024-08-06T21:24:13.1360853Z >>> lambda a, b: a + b 2024-08-06T21:24:13.1360999Z >>> ) 2024-08-06T21:24:13.1361154Z 2024-08-06T21:24:13.1361609Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1361752Z 2024-08-06T21:24:13.1361932Z warnings.warn(msg) 2024-08-06T21:24:13.1362068Z 2024-08-06T21:24:13.1362452Z --- Parse Warning: 48 / 100 --- 2024-08-06T21:24:13.1364190Z /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. 2024-08-06T21:24:13.1364660Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1364814Z 2024-08-06T21:24:13.1365242Z A decorator for a function indicating that the return value of the function 2024-08-06T21:24:13.1365604Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-08-06T21:24:13.1366052Z function can run asynchronously on the RPC callee. More specifically, the 2024-08-06T21:24:13.1366474Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-08-06T21:24:13.1366898Z function and installs subsequent processing steps as a callback to that 2024-08-06T21:24:13.1367332Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-08-06T21:24:13.1367689Z from the :class:`~torch.futures.Future` when completed and send the 2024-08-06T21:24:13.1368005Z value back as the RPC response. That also means the returned 2024-08-06T21:24:13.1368421Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-08-06T21:24:13.1368814Z sent through RPC. This decorator is useful when the wrapped function's 2024-08-06T21:24:13.1369157Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-08-06T21:24:13.1369573Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-08-06T21:24:13.1369713Z 2024-08-06T21:24:13.1370103Z .. note:: To enable asynchronous execution, applications must pass the 2024-08-06T21:24:13.1370552Z function object returned by this decorator to RPC APIs. If RPC detected 2024-08-06T21:24:13.1370946Z attributes installed by this decorator, it knows that this function 2024-08-06T21:24:13.1371282Z returns a ``Future`` object and will handle that accordingly. 2024-08-06T21:24:13.1371661Z However, this does not mean this decorator has to be outmost one when 2024-08-06T21:24:13.1372061Z defining a function. For example, when combined with ``@staticmethod`` 2024-08-06T21:24:13.1372450Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-08-06T21:24:13.1372842Z inner decorator to allow the target function be recognized as a static 2024-08-06T21:24:13.1373246Z or class function. This target function can still execute asynchronously 2024-08-06T21:24:13.1373710Z because, when accessed, the static or class method preserves attributes 2024-08-06T21:24:13.1373968Z installed by ``@rpc.functions.async_execution``. 2024-08-06T21:24:13.1374127Z 2024-08-06T21:24:13.1374262Z 2024-08-06T21:24:13.1374406Z Example:: 2024-08-06T21:24:13.1374780Z The returned :class:`~torch.futures.Future` object can come from 2024-08-06T21:24:13.1375000Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-08-06T21:24:13.1375389Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-08-06T21:24:13.1375710Z constructor. The example below shows directly using the 2024-08-06T21:24:13.1375926Z :class:`~torch.futures.Future` returned by 2024-08-06T21:24:13.1376125Z :meth:`~torch.futures.Future.then`. 2024-08-06T21:24:13.1376277Z 2024-08-06T21:24:13.1376477Z >>> from torch.distributed import rpc 2024-08-06T21:24:13.1376621Z >>> 2024-08-06T21:24:13.1376830Z >>> # omitting setup and shutdown RPC 2024-08-06T21:24:13.1376971Z >>> 2024-08-06T21:24:13.1377123Z >>> # On all workers 2024-08-06T21:24:13.1377331Z >>> @rpc.functions.async_execution 2024-08-06T21:24:13.1377527Z >>> def async_add_chained(to, x, y, z): 2024-08-06T21:24:13.1377931Z >>> # This function runs on "worker1" and returns immediately when 2024-08-06T21:24:13.1378280Z >>> # the callback is installed through the `then(cb)` API. In the 2024-08-06T21:24:13.1378598Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-08-06T21:24:13.1378888Z >>> # When the return value of that `rpc_async` arrives at 2024-08-06T21:24:13.1379214Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-08-06T21:24:13.1379534Z >>> # and set the value for the previously returned `Future`, which 2024-08-06T21:24:13.1379865Z >>> # will then trigger RPC to send the result back to "worker0". 2024-08-06T21:24:13.1380151Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-06T21:24:13.1380339Z >>> lambda fut: fut.wait() + z 2024-08-06T21:24:13.1380492Z >>> ) 2024-08-06T21:24:13.1380633Z >>> 2024-08-06T21:24:13.1380785Z >>> # On worker0 2024-08-06T21:24:13.1380967Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1381130Z >>> ret = rpc.rpc_sync( 2024-08-06T21:24:13.1381279Z >>> "worker1", 2024-08-06T21:24:13.1381458Z >>> async_add_chained, 2024-08-06T21:24:13.1381656Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-08-06T21:24:13.1381793Z >>> ) 2024-08-06T21:24:13.1382007Z >>> print(ret) # prints tensor([3., 3.]) 2024-08-06T21:24:13.1382141Z 2024-08-06T21:24:13.1382543Z When combined with TorchScript decorators, this decorator must be the 2024-08-06T21:24:13.1382708Z outmost one. 2024-08-06T21:24:13.1382845Z 2024-08-06T21:24:13.1383021Z >>> from torch import Tensor 2024-08-06T21:24:13.1383236Z >>> from torch.futures import Future 2024-08-06T21:24:13.1383434Z >>> from torch.distributed import rpc 2024-08-06T21:24:13.1383610Z >>> 2024-08-06T21:24:13.1383817Z >>> # omitting setup and shutdown RPC 2024-08-06T21:24:13.1383966Z >>> 2024-08-06T21:24:13.1384123Z >>> # On all workers 2024-08-06T21:24:13.1384300Z >>> @torch.jit.script 2024-08-06T21:24:13.1384541Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-08-06T21:24:13.1384714Z >>> return x + y 2024-08-06T21:24:13.1384856Z >>> 2024-08-06T21:24:13.1385047Z >>> @rpc.functions.async_execution 2024-08-06T21:24:13.1385225Z >>> @torch.jit.script 2024-08-06T21:24:13.1385554Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-08-06T21:24:13.1385790Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-08-06T21:24:13.1385946Z >>> 2024-08-06T21:24:13.1386132Z >>> # On worker0 2024-08-06T21:24:13.1386297Z >>> ret = rpc.rpc_sync( 2024-08-06T21:24:13.1386462Z >>> "worker1", 2024-08-06T21:24:13.1386690Z >>> async_add, 2024-08-06T21:24:13.1386886Z >>> args=("worker2", torch.ones(2), 1) 2024-08-06T21:24:13.1387052Z >>> ) 2024-08-06T21:24:13.1387246Z >>> print(ret) # prints tensor([2., 2.]) 2024-08-06T21:24:13.1387390Z 2024-08-06T21:24:13.1387792Z When combined with static or class method, this decorator must be the 2024-08-06T21:24:13.1387943Z inner one. 2024-08-06T21:24:13.1388084Z 2024-08-06T21:24:13.1388300Z >>> from torch.distributed import rpc 2024-08-06T21:24:13.1388443Z >>> 2024-08-06T21:24:13.1388638Z >>> # omitting setup and shutdown RPC 2024-08-06T21:24:13.1388794Z >>> 2024-08-06T21:24:13.1388949Z >>> # On all workers 2024-08-06T21:24:13.1389139Z >>> class AsyncExecutionClass: 2024-08-06T21:24:13.1389295Z >>> 2024-08-06T21:24:13.1389448Z >>> @staticmethod 2024-08-06T21:24:13.1389649Z >>> @rpc.functions.async_execution 2024-08-06T21:24:13.1389855Z >>> def static_async_add(to, x, y, z): 2024-08-06T21:24:13.1390260Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-06T21:24:13.1390448Z >>> lambda fut: fut.wait() + z 2024-08-06T21:24:13.1390617Z >>> ) 2024-08-06T21:24:13.1390756Z >>> 2024-08-06T21:24:13.1390912Z >>> @classmethod 2024-08-06T21:24:13.1391127Z >>> @rpc.functions.async_execution 2024-08-06T21:24:13.1391328Z >>> def class_async_add(cls, to, x, y, z): 2024-08-06T21:24:13.1391545Z >>> ret_fut = torch.futures.Future() 2024-08-06T21:24:13.1391791Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-06T21:24:13.1392042Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-08-06T21:24:13.1392204Z >>> ) 2024-08-06T21:24:13.1392368Z >>> return ret_fut 2024-08-06T21:24:13.1392507Z >>> 2024-08-06T21:24:13.1392716Z >>> @rpc.functions.async_execution 2024-08-06T21:24:13.1392918Z >>> def bound_async_add(self, to, x, y, z): 2024-08-06T21:24:13.1393207Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-06T21:24:13.1393403Z >>> lambda fut: fut.wait() + z 2024-08-06T21:24:13.1393554Z >>> ) 2024-08-06T21:24:13.1393692Z >>> 2024-08-06T21:24:13.1393850Z >>> # On worker0 2024-08-06T21:24:13.1394005Z >>> ret = rpc.rpc_sync( 2024-08-06T21:24:13.1394153Z >>> "worker1", 2024-08-06T21:24:13.1394387Z >>> AsyncExecutionClass.static_async_add, 2024-08-06T21:24:13.1394585Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-06T21:24:13.1394726Z >>> ) 2024-08-06T21:24:13.1394936Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:13.1395077Z >>> 2024-08-06T21:24:13.1395239Z >>> ret = rpc.rpc_sync( 2024-08-06T21:24:13.1395402Z >>> "worker1", 2024-08-06T21:24:13.1395650Z >>> AsyncExecutionClass.class_async_add, 2024-08-06T21:24:13.1395854Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-06T21:24:13.1396009Z >>> ) 2024-08-06T21:24:13.1396202Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:13.1396340Z 2024-08-06T21:24:13.1396627Z This decorator also works with RRef helpers, i.e., . 2024-08-06T21:24:13.1396865Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-08-06T21:24:13.1397139Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-08-06T21:24:13.1397366Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-08-06T21:24:13.1397505Z 2024-08-06T21:24:13.1397717Z >>> from torch.distributed import rpc 2024-08-06T21:24:13.1397860Z >>> 2024-08-06T21:24:13.1398088Z >>> # reuse the AsyncExecutionClass class above 2024-08-06T21:24:13.1398394Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-06T21:24:13.1398763Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-08-06T21:24:13.1398961Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:13.1399126Z >>> 2024-08-06T21:24:13.1399381Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-06T21:24:13.1399796Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-08-06T21:24:13.1400007Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:13.1400151Z >>> 2024-08-06T21:24:13.1400403Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-06T21:24:13.1400828Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-08-06T21:24:13.1401024Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-06T21:24:13.1401167Z 2024-08-06T21:24:13.1401636Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1401774Z 2024-08-06T21:24:13.1401953Z warnings.warn(msg) 2024-08-06T21:24:13.1402089Z 2024-08-06T21:24:13.1402434Z --- Parse Warning: 49 / 100 --- 2024-08-06T21:24:13.1404526Z /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. 2024-08-06T21:24:13.1404998Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1405138Z 2024-08-06T21:24:13.1405507Z Set device mapping between each RPC caller and callee pair. This 2024-08-06T21:24:13.1405828Z function can be called multiple times to incrementally add 2024-08-06T21:24:13.1406018Z device placement configurations. 2024-08-06T21:24:13.1406173Z 2024-08-06T21:24:13.1406317Z Args: 2024-08-06T21:24:13.1406483Z to (str): Callee name. 2024-08-06T21:24:13.1406844Z device_map (Dict of int, str, or torch.device): Device placement 2024-08-06T21:24:13.1407163Z mappings from this worker to the callee. This map must be 2024-08-06T21:24:13.1407318Z invertible. 2024-08-06T21:24:13.1407472Z 2024-08-06T21:24:13.1407622Z Example: 2024-08-06T21:24:13.1407810Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:13.1407976Z >>> # both workers 2024-08-06T21:24:13.1408131Z >>> def add(x, y): 2024-08-06T21:24:13.1408358Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-08-06T21:24:13.1408546Z >>> return x + y, (x + y).to(2) 2024-08-06T21:24:13.1408688Z >>> 2024-08-06T21:24:13.1408853Z >>> # on worker 0 2024-08-06T21:24:13.1409082Z >>> options = TensorPipeRpcBackendOptions( 2024-08-06T21:24:13.1409252Z >>> num_worker_threads=8, 2024-08-06T21:24:13.1409460Z >>> device_maps={"worker1": {0: 1}} 2024-08-06T21:24:13.1409679Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-08-06T21:24:13.1409855Z >>> ) 2024-08-06T21:24:13.1410091Z >>> options.set_device_map("worker1", {1: 2}) 2024-08-06T21:24:13.1410309Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-08-06T21:24:13.1410450Z >>> 2024-08-06T21:24:13.1410623Z >>> rpc.init_rpc( 2024-08-06T21:24:13.1410775Z >>> "worker0", 2024-08-06T21:24:13.1410922Z >>> rank=0, 2024-08-06T21:24:13.1411090Z >>> world_size=2, 2024-08-06T21:24:13.1411304Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-08-06T21:24:13.1411493Z >>> rpc_backend_options=options 2024-08-06T21:24:13.1411650Z >>> ) 2024-08-06T21:24:13.1411790Z >>> 2024-08-06T21:24:13.1411949Z >>> x = torch.ones(2) 2024-08-06T21:24:13.1412237Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-08-06T21:24:13.1412595Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-08-06T21:24:13.1412921Z >>> # sending the return value back, it will follow the invert of 2024-08-06T21:24:13.1413243Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-08-06T21:24:13.1413402Z >>> # cuda:1 on worker0 2024-08-06T21:24:13.1413647Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-08-06T21:24:13.1413908Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-08-06T21:24:13.1414052Z 2024-08-06T21:24:13.1414515Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1414656Z 2024-08-06T21:24:13.1414817Z warnings.warn(msg) 2024-08-06T21:24:13.1414967Z 2024-08-06T21:24:13.1415286Z --- Parse Warning: 50 / 100 --- 2024-08-06T21:24:13.1417186Z /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. 2024-08-06T21:24:13.1417672Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1417810Z 2024-08-06T21:24:13.1418577Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-08-06T21:24:13.1419173Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-08-06T21:24:13.1419312Z 2024-08-06T21:24:13.1419464Z Keyword Args: 2024-08-06T21:24:13.1419829Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-06T21:24:13.1420422Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-08-06T21:24:13.1421083Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-08-06T21:24:13.1421286Z as a placeholder. default: None. 2024-08-06T21:24:13.1421694Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-06T21:24:13.1422402Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-06T21:24:13.1423126Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-08-06T21:24:13.1423348Z input_kwarg_layouts (Dict[str, Placement]): 2024-08-06T21:24:13.1424046Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-08-06T21:24:13.1424202Z default: None 2024-08-06T21:24:13.1424485Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-08-06T21:24:13.1425159Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-06T21:24:13.1425411Z have the desired DTensor layouts. default: None. 2024-08-06T21:24:13.1425665Z use_local_output (bool, optional): 2024-08-06T21:24:13.1426313Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-08-06T21:24:13.1426457Z Returns: 2024-08-06T21:24:13.1427124Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-08-06T21:24:13.1427266Z 2024-08-06T21:24:13.1427418Z Example:: 2024-08-06T21:24:13.1427603Z >>> # xdoctest: +SKIP(failing) 2024-08-06T21:24:13.1428160Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-08-06T21:24:13.1428495Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-06T21:24:13.1428650Z >>> ... 2024-08-06T21:24:13.1429196Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-06T21:24:13.1429461Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-06T21:24:13.1429601Z >>> 2024-08-06T21:24:13.1430214Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-08-06T21:24:13.1430471Z >>> # and then redistributed to Replicated DTensor. 2024-08-06T21:24:13.1430636Z >>> parallelize_module( 2024-08-06T21:24:13.1430861Z >>> block, # this can be a submodule or module 2024-08-06T21:24:13.1431029Z >>> tp_mesh, 2024-08-06T21:24:13.1431201Z >>> parallelize_plan={ 2024-08-06T21:24:13.1431398Z >>> "attn": PrepareModuleInput( 2024-08-06T21:24:13.1431641Z >>> input_layouts=(Shard(0), None, None, ...), 2024-08-06T21:24:13.1431924Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-08-06T21:24:13.1432073Z >>> ), 2024-08-06T21:24:13.1432239Z >>> } 2024-08-06T21:24:13.1432372Z >>> ) 2024-08-06T21:24:13.1432508Z 2024-08-06T21:24:13.1432975Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1433121Z 2024-08-06T21:24:13.1433281Z warnings.warn(msg) 2024-08-06T21:24:13.1433496Z 2024-08-06T21:24:13.1433826Z --- Parse Warning: 51 / 100 --- 2024-08-06T21:24:13.1435748Z /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. 2024-08-06T21:24:13.1436233Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1436375Z 2024-08-06T21:24:13.1437118Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-08-06T21:24:13.1437723Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-08-06T21:24:13.1437861Z 2024-08-06T21:24:13.1438030Z Keyword Args: 2024-08-06T21:24:13.1438313Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-06T21:24:13.1438930Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-08-06T21:24:13.1439631Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-08-06T21:24:13.1439873Z ``None`` need to be specified as a placeholder. 2024-08-06T21:24:13.1440215Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-06T21:24:13.1440950Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-08-06T21:24:13.1441148Z have the desired DTensor layouts. 2024-08-06T21:24:13.1441355Z use_local_output (bool, optional): 2024-08-06T21:24:13.1442003Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-08-06T21:24:13.1442186Z Returns: 2024-08-06T21:24:13.1442883Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-08-06T21:24:13.1443021Z 2024-08-06T21:24:13.1443175Z Example:: 2024-08-06T21:24:13.1443365Z >>> # xdoctest: +SKIP(failing) 2024-08-06T21:24:13.1443936Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-08-06T21:24:13.1444266Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-06T21:24:13.1444425Z >>> ... 2024-08-06T21:24:13.1444968Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-06T21:24:13.1445180Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-06T21:24:13.1445429Z >>> 2024-08-06T21:24:13.1446154Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-08-06T21:24:13.1446398Z >>> # and then redistributed to Sharded DTensor. 2024-08-06T21:24:13.1446569Z >>> parallelize_module( 2024-08-06T21:24:13.1446797Z >>> block, # this can be a submodule or module 2024-08-06T21:24:13.1446956Z >>> tp_mesh, 2024-08-06T21:24:13.1447189Z >>> parallelize_plan = PrepareModuleOutput( 2024-08-06T21:24:13.1447380Z >>> output_layouts=Replicate(), 2024-08-06T21:24:13.1447595Z >>> desired_output_layouts=Shard(0) 2024-08-06T21:24:13.1447739Z >>> ) 2024-08-06T21:24:13.1447878Z >>> ) 2024-08-06T21:24:13.1448030Z 2024-08-06T21:24:13.1448479Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1448617Z 2024-08-06T21:24:13.1448788Z warnings.warn(msg) 2024-08-06T21:24:13.1448924Z 2024-08-06T21:24:13.1449245Z --- Parse Warning: 52 / 100 --- 2024-08-06T21:24:13.1451239Z /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=230. 2024-08-06T21:24:13.1451714Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1452042Z Return a new dict with new, potentially nested, key value pair 2024-08-06T21:24:13.1452190Z 2024-08-06T21:24:13.1452365Z >>> purchase = {'name': 'Alice', 2024-08-06T21:24:13.1452589Z ... 'order': {'items': ['Apple', 'Orange'], 2024-08-06T21:24:13.1452778Z ... 'costs': [0.50, 1.25]}, 2024-08-06T21:24:13.1452971Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-06T21:24:13.1453340Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-08-06T21:24:13.1453519Z {'credit card': '5555-1234-1234-1234', 2024-08-06T21:24:13.1453675Z 'name': 'Alice', 2024-08-06T21:24:13.1453966Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-08-06T21:24:13.1454113Z 2024-08-06T21:24:13.1454561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1454710Z 2024-08-06T21:24:13.1454876Z warnings.warn(msg) 2024-08-06T21:24:13.1455010Z 2024-08-06T21:24:13.1455330Z --- Parse Warning: 53 / 100 --- 2024-08-06T21:24:13.1457243Z /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=245. 2024-08-06T21:24:13.1457714Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1457985Z Update value in a (potentially) nested dictionary 2024-08-06T21:24:13.1458122Z 2024-08-06T21:24:13.1458329Z inputs: 2024-08-06T21:24:13.1458521Z d - dictionary on which to operate 2024-08-06T21:24:13.1458910Z keys - list or tuple giving the location of the value to be changed in d 2024-08-06T21:24:13.1459131Z func - function to operate on that value 2024-08-06T21:24:13.1459270Z 2024-08-06T21:24:13.1459591Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-08-06T21:24:13.1460021Z original dictionary with v replaced by func(v), but does not mutate the 2024-08-06T21:24:13.1460188Z original dictionary. 2024-08-06T21:24:13.1460325Z 2024-08-06T21:24:13.1460702Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-08-06T21:24:13.1461078Z specified by the keys, with the innermost value set to func(default). 2024-08-06T21:24:13.1461251Z 2024-08-06T21:24:13.1461432Z >>> inc = lambda x: x + 1 2024-08-06T21:24:13.1461610Z >>> update_in({'a': 0}, ['a'], inc) 2024-08-06T21:24:13.1461757Z {'a': 1} 2024-08-06T21:24:13.1461906Z 2024-08-06T21:24:13.1462088Z >>> transaction = {'name': 'Alice', 2024-08-06T21:24:13.1462326Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-06T21:24:13.1462535Z ... 'costs': [0.50, 1.25]}, 2024-08-06T21:24:13.1462731Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-06T21:24:13.1463106Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-08-06T21:24:13.1463296Z {'credit card': '5555-1234-1234-1234', 2024-08-06T21:24:13.1463446Z 'name': 'Alice', 2024-08-06T21:24:13.1463768Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-08-06T21:24:13.1463905Z 2024-08-06T21:24:13.1464103Z >>> # updating a value when k0 is not in d 2024-08-06T21:24:13.1464341Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-08-06T21:24:13.1464491Z {1: {2: {3: 'bar'}}} 2024-08-06T21:24:13.1464685Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-08-06T21:24:13.1464854Z {1: 'foo', 2: {3: {4: 1}}} 2024-08-06T21:24:13.1464996Z 2024-08-06T21:24:13.1465513Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1465664Z 2024-08-06T21:24:13.1465827Z warnings.warn(msg) 2024-08-06T21:24:13.1465964Z 2024-08-06T21:24:13.1466292Z --- Parse Warning: 54 / 100 --- 2024-08-06T21:24:13.1468229Z /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=303. 2024-08-06T21:24:13.1468704Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1469008Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-08-06T21:24:13.1469150Z 2024-08-06T21:24:13.1469471Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-08-06T21:24:13.1469832Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-08-06T21:24:13.1469972Z 2024-08-06T21:24:13.1470344Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-08-06T21:24:13.1470565Z structures such as dictionaries and lists. 2024-08-06T21:24:13.1470703Z 2024-08-06T21:24:13.1470901Z >>> transaction = {'name': 'Alice', 2024-08-06T21:24:13.1471135Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-06T21:24:13.1471329Z ... 'costs': [0.50, 1.25]}, 2024-08-06T21:24:13.1471541Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-06T21:24:13.1471769Z >>> get_in(['purchase', 'items', 0], transaction) 2024-08-06T21:24:13.1471918Z 'Apple' 2024-08-06T21:24:13.1472111Z >>> get_in(['name'], transaction) 2024-08-06T21:24:13.1472253Z 'Alice' 2024-08-06T21:24:13.1472509Z >>> get_in(['purchase', 'total'], transaction) 2024-08-06T21:24:13.1472776Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-08-06T21:24:13.1472995Z >>> get_in(['purchase', 'items', 10], transaction) 2024-08-06T21:24:13.1473226Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-08-06T21:24:13.1473385Z 0 2024-08-06T21:24:13.1473563Z >>> get_in(['y'], {}, no_default=True) 2024-08-06T21:24:13.1473757Z Traceback (most recent call last): 2024-08-06T21:24:13.1473919Z ... 2024-08-06T21:24:13.1474074Z KeyError: 'y' 2024-08-06T21:24:13.1474212Z 2024-08-06T21:24:13.1474372Z See Also: 2024-08-06T21:24:13.1474527Z itertoolz.get 2024-08-06T21:24:13.1474687Z operator.getitem 2024-08-06T21:24:13.1474839Z 2024-08-06T21:24:13.1475324Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1475483Z 2024-08-06T21:24:13.1475644Z warnings.warn(msg) 2024-08-06T21:24:13.1475778Z 2024-08-06T21:24:13.1476099Z --- Parse Warning: 55 / 100 --- 2024-08-06T21:24:13.1478004Z /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=355. 2024-08-06T21:24:13.1478473Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1478673Z Group a collection by a key function 2024-08-06T21:24:13.1478809Z 2024-08-06T21:24:13.1479089Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-08-06T21:24:13.1479298Z >>> groupby(len, names) # doctest: +SKIP 2024-08-06T21:24:13.1479565Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-08-06T21:24:13.1479700Z 2024-08-06T21:24:13.1479888Z >>> iseven = lambda x: x % 2 == 0 2024-08-06T21:24:13.1480176Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-08-06T21:24:13.1480440Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-08-06T21:24:13.1480590Z 2024-08-06T21:24:13.1480829Z Non-callable keys imply grouping on a member. 2024-08-06T21:24:13.1480963Z 2024-08-06T21:24:13.1481234Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-08-06T21:24:13.1481436Z ... {'name': 'Bob', 'gender': 'M'}, 2024-08-06T21:24:13.1481704Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-08-06T21:24:13.1481889Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-08-06T21:24:13.1482063Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-08-06T21:24:13.1482262Z {'gender': 'M', 'name': 'Charlie'}]} 2024-08-06T21:24:13.1482399Z 2024-08-06T21:24:13.1482631Z Not to be confused with ``itertools.groupby`` 2024-08-06T21:24:13.1482780Z 2024-08-06T21:24:13.1482931Z See Also: 2024-08-06T21:24:13.1483073Z countby 2024-08-06T21:24:13.1483224Z 2024-08-06T21:24:13.1483686Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1483826Z 2024-08-06T21:24:13.1483998Z warnings.warn(msg) 2024-08-06T21:24:13.1484135Z 2024-08-06T21:24:13.1484451Z --- Parse Warning: 56 / 100 --- 2024-08-06T21:24:13.1486160Z /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. 2024-08-06T21:24:13.1486630Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1486933Z Applies Batch Normalization over a N-Dimensional input. 2024-08-06T21:24:13.1487087Z 2024-08-06T21:24:13.1487695Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-08-06T21:24:13.1488153Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-08-06T21:24:13.1488534Z Internal Covariate Shift `__ . 2024-08-06T21:24:13.1488674Z 2024-08-06T21:24:13.1488834Z .. math:: 2024-08-06T21:24:13.1488968Z 2024-08-06T21:24:13.1489335Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-08-06T21:24:13.1489489Z 2024-08-06T21:24:13.1489889Z The mean and standard-deviation are calculated per-dimension over all 2024-08-06T21:24:13.1490298Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-08-06T21:24:13.1490748Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-08-06T21:24:13.1491088Z By default, the elements of :math:`\gamma` are sampled from 2024-08-06T21:24:13.1491431Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-08-06T21:24:13.1491911Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-08-06T21:24:13.1492116Z `torch.var(input, unbiased=False)`. 2024-08-06T21:24:13.1492254Z 2024-08-06T21:24:13.1492690Z Also by default, during training this layer keeps running estimates of its 2024-08-06T21:24:13.1493104Z computed mean and variance, which are then used for normalization during 2024-08-06T21:24:13.1493554Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-08-06T21:24:13.1493699Z of 0.1. 2024-08-06T21:24:13.1493841Z 2024-08-06T21:24:13.1494258Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-08-06T21:24:13.1494647Z keep running estimates, and batch statistics are instead used during 2024-08-06T21:24:13.1494827Z evaluation time as well. 2024-08-06T21:24:13.1494983Z 2024-08-06T21:24:13.1495136Z .. note:: 2024-08-06T21:24:13.1495528Z This :attr:`momentum` argument is different from one used in optimizer 2024-08-06T21:24:13.1496006Z classes and the conventional notion of momentum. Mathematically, the 2024-08-06T21:24:13.1496241Z update rule for running statistics here is 2024-08-06T21:24:13.1496704Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-08-06T21:24:13.1497073Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-08-06T21:24:13.1497240Z new observed value. 2024-08-06T21:24:13.1497380Z 2024-08-06T21:24:13.1497937Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-08-06T21:24:13.1498375Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-08-06T21:24:13.1498699Z Normalization or Spatio-temporal Batch Normalization. 2024-08-06T21:24:13.1498841Z 2024-08-06T21:24:13.1499089Z Currently :class:`SyncBatchNorm` only supports 2024-08-06T21:24:13.1499608Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-08-06T21:24:13.1499979Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-08-06T21:24:13.1500337Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-08-06T21:24:13.1500518Z Network with DDP. 2024-08-06T21:24:13.1500661Z 2024-08-06T21:24:13.1500801Z Args: 2024-08-06T21:24:13.1501094Z num_features: :math:`C` from an expected input of size 2024-08-06T21:24:13.1501255Z :math:`(N, C, +)` 2024-08-06T21:24:13.1501578Z eps: a value added to the denominator for numerical stability. 2024-08-06T21:24:13.1501753Z Default: ``1e-5`` 2024-08-06T21:24:13.1502096Z momentum: the value used for the running_mean and running_var 2024-08-06T21:24:13.1502463Z computation. Can be set to ``None`` for cumulative moving average 2024-08-06T21:24:13.1502713Z (i.e. simple average). Default: 0.1 2024-08-06T21:24:13.1503068Z affine: a boolean value that when set to ``True``, this module has 2024-08-06T21:24:13.1503336Z learnable affine parameters. Default: ``True`` 2024-08-06T21:24:13.1503709Z track_running_stats: a boolean value that when set to ``True``, this 2024-08-06T21:24:13.1504113Z module tracks the running mean and variance, and when set to ``False``, 2024-08-06T21:24:13.1504518Z this module does not track such statistics, and initializes statistics 2024-08-06T21:24:13.1504873Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-08-06T21:24:13.1505273Z When these buffers are ``None``, this module always uses batch statistics. 2024-08-06T21:24:13.1505571Z in both training and eval modes. Default: ``True`` 2024-08-06T21:24:13.1506014Z process_group: synchronization of stats happen within each process group 2024-08-06T21:24:13.1506416Z individually. Default behavior is synchronization across the whole 2024-08-06T21:24:13.1506575Z world 2024-08-06T21:24:13.1506784Z 2024-08-06T21:24:13.1506942Z Shape: 2024-08-06T21:24:13.1507118Z - Input: :math:`(N, C, +)` 2024-08-06T21:24:13.1507364Z - Output: :math:`(N, C, +)` (same shape as input) 2024-08-06T21:24:13.1507515Z 2024-08-06T21:24:13.1507664Z .. note:: 2024-08-06T21:24:13.1508101Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-08-06T21:24:13.1508469Z synchronization is disabled when ``model.eval()`` is set or if 2024-08-06T21:24:13.1508676Z ``self.training`` is otherwise ``False``. 2024-08-06T21:24:13.1508822Z 2024-08-06T21:24:13.1508987Z Examples:: 2024-08-06T21:24:13.1509123Z 2024-08-06T21:24:13.1509288Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1509487Z >>> # With Learnable Parameters 2024-08-06T21:24:13.1509732Z >>> m = nn.SyncBatchNorm(100) 2024-08-06T21:24:13.1509937Z >>> # creating process group (optional) 2024-08-06T21:24:13.1510188Z >>> # ranks is a list of int identifying rank ids. 2024-08-06T21:24:13.1510361Z >>> ranks = list(range(8)) 2024-08-06T21:24:13.1510540Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-06T21:24:13.1510801Z >>> # Note: every rank calls into new_group for every 2024-08-06T21:24:13.1511054Z >>> # process group created, even if that rank is not 2024-08-06T21:24:13.1511222Z >>> # part of the group. 2024-08-06T21:24:13.1511672Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-06T21:24:13.1512030Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-06T21:24:13.1512241Z >>> # Without Learnable Parameters 2024-08-06T21:24:13.1512603Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-08-06T21:24:13.1512814Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-08-06T21:24:13.1512992Z >>> output = m(input) 2024-08-06T21:24:13.1513133Z 2024-08-06T21:24:13.1513328Z >>> # network is nn.BatchNorm layer 2024-08-06T21:24:13.1513830Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-08-06T21:24:13.1514111Z >>> # only single gpu per process is currently supported 2024-08-06T21:24:13.1514487Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-08-06T21:24:13.1514686Z >>> sync_bn_network, 2024-08-06T21:24:13.1514902Z >>> device_ids=[args.local_rank], 2024-08-06T21:24:13.1515118Z >>> output_device=args.local_rank) 2024-08-06T21:24:13.1515312Z 2024-08-06T21:24:13.1524793Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1525002Z 2024-08-06T21:24:13.1525177Z warnings.warn(msg) 2024-08-06T21:24:13.1525311Z 2024-08-06T21:24:13.1525727Z --- Parse Warning: 57 / 100 --- 2024-08-06T21:24:13.1527655Z /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. 2024-08-06T21:24:13.1528122Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1528692Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-08-06T21:24:13.1528932Z 2024-08-06T21:24:13.1529093Z Args: 2024-08-06T21:24:13.1529519Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-08-06T21:24:13.1529914Z process_group (optional): process group to scope synchronization, 2024-08-06T21:24:13.1530118Z default is the whole world 2024-08-06T21:24:13.1530257Z 2024-08-06T21:24:13.1530404Z Returns: 2024-08-06T21:24:13.1530864Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-08-06T21:24:13.1531238Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-08-06T21:24:13.1531603Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-08-06T21:24:13.1531771Z instead. 2024-08-06T21:24:13.1531903Z 2024-08-06T21:24:13.1532062Z Example:: 2024-08-06T21:24:13.1532217Z 2024-08-06T21:24:13.1532427Z >>> # Network with nn.BatchNorm layer 2024-08-06T21:24:13.1532668Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:13.1532885Z >>> module = torch.nn.Sequential( 2024-08-06T21:24:13.1533157Z >>> torch.nn.Linear(20, 100), 2024-08-06T21:24:13.1533360Z >>> torch.nn.BatchNorm1d(100), 2024-08-06T21:24:13.1533531Z >>> ).cuda() 2024-08-06T21:24:13.1533743Z >>> # creating process group (optional) 2024-08-06T21:24:13.1534000Z >>> # ranks is a list of int identifying rank ids. 2024-08-06T21:24:13.1534177Z >>> ranks = list(range(8)) 2024-08-06T21:24:13.1534358Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-06T21:24:13.1534630Z >>> # Note: every rank calls into new_group for every 2024-08-06T21:24:13.1534898Z >>> # process group created, even if that rank is not 2024-08-06T21:24:13.1535074Z >>> # part of the group. 2024-08-06T21:24:13.1535296Z >>> # xdoctest: +SKIP("distributed") 2024-08-06T21:24:13.1535746Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-06T21:24:13.1536116Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-06T21:24:13.1536665Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-08-06T21:24:13.1536804Z 2024-08-06T21:24:13.1536946Z 2024-08-06T21:24:13.1537414Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1537550Z 2024-08-06T21:24:13.1537714Z warnings.warn(msg) 2024-08-06T21:24:13.1537868Z 2024-08-06T21:24:13.1538194Z --- Parse Warning: 58 / 100 --- 2024-08-06T21:24:13.1539846Z /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. 2024-08-06T21:24:13.1540354Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1540494Z 2024-08-06T21:24:13.1541074Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-08-06T21:24:13.1541219Z 2024-08-06T21:24:13.1541701Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-08-06T21:24:13.1542110Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-08-06T21:24:13.1542253Z 2024-08-06T21:24:13.1543016Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-08-06T21:24:13.1543475Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-08-06T21:24:13.1543756Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-08-06T21:24:13.1543991Z 2024-08-06T21:24:13.1544161Z Shape: 2024-08-06T21:24:13.1544538Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-08-06T21:24:13.1545005Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-08-06T21:24:13.1545381Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-08-06T21:24:13.1545589Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-08-06T21:24:13.1545747Z 2024-08-06T21:24:13.1545891Z Args: 2024-08-06T21:24:13.1546144Z dim (Union[int, str]): Dimension to be unflattened 2024-08-06T21:24:13.1546859Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-08-06T21:24:13.1546999Z 2024-08-06T21:24:13.1547145Z Examples: 2024-08-06T21:24:13.1547342Z >>> input = torch.randn(2, 50) 2024-08-06T21:24:13.1547512Z >>> # With tuple of ints 2024-08-06T21:24:13.1547676Z >>> m = nn.Sequential( 2024-08-06T21:24:13.1547855Z >>> nn.Linear(50, 50), 2024-08-06T21:24:13.1548033Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-08-06T21:24:13.1548176Z >>> ) 2024-08-06T21:24:13.1548445Z >>> output = m(input) 2024-08-06T21:24:13.1548600Z >>> output.size() 2024-08-06T21:24:13.1548763Z torch.Size([2, 2, 5, 5]) 2024-08-06T21:24:13.1548941Z >>> # With torch.Size 2024-08-06T21:24:13.1549101Z >>> m = nn.Sequential( 2024-08-06T21:24:13.1549267Z >>> nn.Linear(50, 50), 2024-08-06T21:24:13.1549489Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-08-06T21:24:13.1549633Z >>> ) 2024-08-06T21:24:13.1549794Z >>> output = m(input) 2024-08-06T21:24:13.1549965Z >>> output.size() 2024-08-06T21:24:13.1550129Z torch.Size([2, 2, 5, 5]) 2024-08-06T21:24:13.1550335Z >>> # With namedshape (tuple of tuples) 2024-08-06T21:24:13.1550611Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-08-06T21:24:13.1550963Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-08-06T21:24:13.1551158Z >>> output = unflatten(input) 2024-08-06T21:24:13.1551315Z >>> output.size() 2024-08-06T21:24:13.1551475Z torch.Size([2, 2, 5, 5]) 2024-08-06T21:24:13.1551624Z 2024-08-06T21:24:13.1552076Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1552212Z 2024-08-06T21:24:13.1552383Z warnings.warn(msg) 2024-08-06T21:24:13.1552520Z 2024-08-06T21:24:13.1552852Z --- Parse Warning: 59 / 100 --- 2024-08-06T21:24:13.1554718Z /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=1696. 2024-08-06T21:24:13.1555190Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1555534Z Creates a criterion that measures the triplet loss given input 2024-08-06T21:24:13.1555931Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-08-06T21:24:13.1556309Z positive, and negative examples, respectively), and a nonnegative, 2024-08-06T21:24:13.1556759Z real-valued function ("distance function") used to compute the relationship 2024-08-06T21:24:13.1557153Z between the anchor and positive example ("positive distance") and the 2024-08-06T21:24:13.1557415Z anchor and negative example ("negative distance"). 2024-08-06T21:24:13.1557567Z 2024-08-06T21:24:13.1557931Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-08-06T21:24:13.1558098Z can be described as: 2024-08-06T21:24:13.1558253Z 2024-08-06T21:24:13.1558400Z .. math:: 2024-08-06T21:24:13.1558663Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-08-06T21:24:13.1558936Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-08-06T21:24:13.1559074Z 2024-08-06T21:24:13.1559504Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-08-06T21:24:13.1560047Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-08-06T21:24:13.1560468Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-08-06T21:24:13.1560913Z between the positive and negative distances that is required for the loss to 2024-08-06T21:24:13.1561330Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-08-06T21:24:13.1561538Z that the distance function can handle. 2024-08-06T21:24:13.1561691Z 2024-08-06T21:24:13.1561885Z If :attr:`reduction` is not ``'none'`` 2024-08-06T21:24:13.1562054Z (default ``'mean'``), then: 2024-08-06T21:24:13.1562206Z 2024-08-06T21:24:13.1562353Z .. math:: 2024-08-06T21:24:13.1562504Z \ell(x, y) = 2024-08-06T21:24:13.1562676Z \begin{cases} 2024-08-06T21:24:13.1563028Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-08-06T21:24:13.1563427Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-08-06T21:24:13.1563594Z \end{cases} 2024-08-06T21:24:13.1563728Z 2024-08-06T21:24:13.1564140Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-08-06T21:24:13.1564595Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-08-06T21:24:13.1564734Z 2024-08-06T21:24:13.1564876Z Args: 2024-08-06T21:24:13.1565383Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-08-06T21:24:13.1565716Z quantifies the closeness of two tensors. If not specified, 2024-08-06T21:24:13.1566010Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-08-06T21:24:13.1566508Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-08-06T21:24:13.1567010Z between the positive and negative distances required for the loss to be 0. Larger 2024-08-06T21:24:13.1567531Z margins penalize cases where the negative examples are not distant enough from the 2024-08-06T21:24:13.1567832Z anchors, relative to the positives. Default: :math:`1`. 2024-08-06T21:24:13.1568272Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-08-06T21:24:13.1568752Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-08-06T21:24:13.1569162Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-08-06T21:24:13.1569644Z negative example than the anchor is, swaps the positive example and the anchor in 2024-08-06T21:24:13.1569890Z the loss computation. Default: ``False``. 2024-08-06T21:24:13.1570422Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-08-06T21:24:13.1570761Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-08-06T21:24:13.1571078Z ``'mean'``: the sum of the output will be divided by the number of 2024-08-06T21:24:13.1571505Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-08-06T21:24:13.1571661Z 2024-08-06T21:24:13.1571801Z 2024-08-06T21:24:13.1571947Z Shape: 2024-08-06T21:24:13.1572388Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-08-06T21:24:13.1572604Z as supported by the distance function. 2024-08-06T21:24:13.1573044Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-08-06T21:24:13.1573244Z otherwise. 2024-08-06T21:24:13.1573381Z 2024-08-06T21:24:13.1573531Z Examples:: 2024-08-06T21:24:13.1573690Z 2024-08-06T21:24:13.1573863Z >>> # Initialize embeddings 2024-08-06T21:24:13.1574065Z >>> embedding = nn.Embedding(1000, 128) 2024-08-06T21:24:13.1574297Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-08-06T21:24:13.1574516Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-08-06T21:24:13.1574727Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-08-06T21:24:13.1574935Z >>> anchor = embedding(anchor_ids) 2024-08-06T21:24:13.1575134Z >>> positive = embedding(positive_ids) 2024-08-06T21:24:13.1575329Z >>> negative = embedding(negative_ids) 2024-08-06T21:24:13.1575487Z >>> 2024-08-06T21:24:13.1575669Z >>> # Built-in Distance Function 2024-08-06T21:24:13.1575837Z >>> triplet_loss = \ 2024-08-06T21:24:13.1576358Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-08-06T21:24:13.1576620Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-06T21:24:13.1576807Z >>> output.backward() 2024-08-06T21:24:13.1576948Z >>> 2024-08-06T21:24:13.1577211Z >>> # Custom Distance Function 2024-08-06T21:24:13.1577398Z >>> def l_infinity(x1, x2): 2024-08-06T21:24:13.1577658Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-08-06T21:24:13.1577803Z >>> 2024-08-06T21:24:13.1578150Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-08-06T21:24:13.1578311Z >>> triplet_loss = ( 2024-08-06T21:24:13.1578818Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-08-06T21:24:13.1579089Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-06T21:24:13.1579259Z >>> output.backward() 2024-08-06T21:24:13.1579426Z >>> 2024-08-06T21:24:13.1579624Z >>> # Custom Distance Function (Lambda) 2024-08-06T21:24:13.1579782Z >>> triplet_loss = ( 2024-08-06T21:24:13.1580020Z >>> nn.TripletMarginWithDistanceLoss( 2024-08-06T21:24:13.1580406Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-08-06T21:24:13.1580662Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-06T21:24:13.1580842Z >>> output.backward() 2024-08-06T21:24:13.1580978Z 2024-08-06T21:24:13.1581123Z Reference: 2024-08-06T21:24:13.1581677Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-08-06T21:24:13.1582003Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-08-06T21:24:13.1582144Z 2024-08-06T21:24:13.1582614Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-08-06T21:24:13.1582749Z 2024-08-06T21:24:13.1582910Z warnings.warn(msg) 2024-08-06T21:24:13.1583066Z 2024-08-06T21:24:13.1583397Z --- Parse Warning: 60 / 100 --- 2024-08-06T21:24:13.1585100Z /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. 2024-08-06T21:24:13.1585582Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1585837Z Computes a partial inverse of :class:`MaxPool2d`. 2024-08-06T21:24:13.1585988Z 2024-08-06T21:24:13.1586448Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-08-06T21:24:13.1586645Z 2024-08-06T21:24:13.1587052Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-08-06T21:24:13.1587481Z including the indices of the maximal values and computes a partial inverse 2024-08-06T21:24:13.1587767Z in which all non-maximal values are set to zero. 2024-08-06T21:24:13.1587918Z 2024-08-06T21:24:13.1588061Z Note: 2024-08-06T21:24:13.1588636Z This operation may behave nondeterministically when the input indices has repeat values. 2024-08-06T21:24:13.1589330Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-08-06T21:24:13.1589468Z 2024-08-06T21:24:13.1589866Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-08-06T21:24:13.1590177Z sizes. Hence, the inversion process can get ambiguous. 2024-08-06T21:24:13.1590506Z To accommodate this, you can provide the needed output size 2024-08-06T21:24:13.1590894Z as an additional argument :attr:`output_size` in the forward call. 2024-08-06T21:24:13.1591093Z See the Inputs and Example below. 2024-08-06T21:24:13.1591232Z 2024-08-06T21:24:13.1591389Z Args: 2024-08-06T21:24:13.1591702Z kernel_size (int or tuple): Size of the max pooling window. 2024-08-06T21:24:13.1591991Z stride (int or tuple): Stride of the max pooling window. 2024-08-06T21:24:13.1592232Z It is set to :attr:`kernel_size` by default. 2024-08-06T21:24:13.1592612Z padding (int or tuple): Padding that was added to the input 2024-08-06T21:24:13.1592748Z 2024-08-06T21:24:13.1592905Z Inputs: 2024-08-06T21:24:13.1593110Z - `input`: the input Tensor to invert 2024-08-06T21:24:13.1593464Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-08-06T21:24:13.1593743Z - `output_size` (optional): the targeted output size 2024-08-06T21:24:13.1593880Z 2024-08-06T21:24:13.1594024Z Shape: 2024-08-06T21:24:13.1594350Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-08-06T21:24:13.1594713Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-08-06T21:24:13.1594849Z 2024-08-06T21:24:13.1595013Z .. math:: 2024-08-06T21:24:13.1595483Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-08-06T21:24:13.1595643Z 2024-08-06T21:24:13.1595792Z .. math:: 2024-08-06T21:24:13.1596244Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-08-06T21:24:13.1596392Z 2024-08-06T21:24:13.1596667Z or as given by :attr:`output_size` in the call operator 2024-08-06T21:24:13.1596802Z 2024-08-06T21:24:13.1596967Z Example:: 2024-08-06T21:24:13.1597103Z 2024-08-06T21:24:13.1597380Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-08-06T21:24:13.1597596Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-08-06T21:24:13.1597812Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-08-06T21:24:13.1597991Z [ 5., 6., 7., 8.], 2024-08-06T21:24:13.1598178Z [ 9., 10., 11., 12.], 2024-08-06T21:24:13.1598399Z [13., 14., 15., 16.]]]]) 2024-08-06T21:24:13.1598595Z >>> output, indices = pool(input) 2024-08-06T21:24:13.1598784Z >>> unpool(output, indices) 2024-08-06T21:24:13.1598959Z tensor([[[[ 0., 0., 0., 0.], 2024-08-06T21:24:13.1599122Z [ 0., 6., 0., 8.], 2024-08-06T21:24:13.1599304Z [ 0., 0., 0., 0.], 2024-08-06T21:24:13.1599472Z [ 0., 14., 0., 16.]]]]) 2024-08-06T21:24:13.1599854Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-08-06T21:24:13.1600082Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-08-06T21:24:13.1600261Z [ 6., 7., 8., 9., 10.], 2024-08-06T21:24:13.1600491Z [11., 12., 13., 14., 15.], 2024-08-06T21:24:13.1600673Z [16., 17., 18., 19., 20.]]]]) 2024-08-06T21:24:13.1600862Z >>> output, indices = pool(input) 2024-08-06T21:24:13.1601179Z >>> # This call will not work without specifying output_size 2024-08-06T21:24:13.1601446Z >>> unpool(output, indices, output_size=input.size()) 2024-08-06T21:24:13.1601620Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-08-06T21:24:13.1601799Z [ 0., 7., 0., 9., 0.], 2024-08-06T21:24:13.1601958Z [ 0., 0., 0., 0., 0.], 2024-08-06T21:24:13.1602126Z [ 0., 17., 0., 19., 0.]]]]) 2024-08-06T21:24:13.1602275Z 2024-08-06T21:24:13.1602413Z 2024-08-06T21:24:13.1602557Z 2024-08-06T21:24:13.1603014Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1603152Z 2024-08-06T21:24:13.1603313Z warnings.warn(msg) 2024-08-06T21:24:13.1603464Z 2024-08-06T21:24:13.1603798Z --- Parse Warning: 61 / 100 --- 2024-08-06T21:24:13.1605549Z /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. 2024-08-06T21:24:13.1606025Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1606593Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-08-06T21:24:13.1606747Z 2024-08-06T21:24:13.1607323Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-08-06T21:24:13.1607502Z and with 2D inputs, this class 2024-08-06T21:24:13.1607653Z 2024-08-06T21:24:13.1608199Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-08-06T21:24:13.1608759Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-08-06T21:24:13.1609318Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-08-06T21:24:13.1609452Z 2024-08-06T21:24:13.1610103Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-08-06T21:24:13.1610251Z operations. 2024-08-06T21:24:13.1610387Z 2024-08-06T21:24:13.1610858Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-08-06T21:24:13.1611271Z pass. This scales the output of the Embedding before performing a weighted 2024-08-06T21:24:13.1611711Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-08-06T21:24:13.1612142Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-08-06T21:24:13.1612321Z :attr:`per_sample_weights`. 2024-08-06T21:24:13.1612500Z 2024-08-06T21:24:13.1612655Z Args: 2024-08-06T21:24:13.1612977Z num_embeddings (int): size of the dictionary of embeddings 2024-08-06T21:24:13.1613265Z embedding_dim (int): the size of each embedding vector 2024-08-06T21:24:13.1613844Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-08-06T21:24:13.1614100Z is renormalized to have norm :attr:`max_norm`. 2024-08-06T21:24:13.1614716Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-08-06T21:24:13.1615303Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-08-06T21:24:13.1615574Z the words in the mini-batch. Default ``False``. 2024-08-06T21:24:13.1615945Z Note: this option is not supported when ``mode="max"``. 2024-08-06T21:24:13.1616393Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-08-06T21:24:13.1616775Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-08-06T21:24:13.1617173Z into consideration. ``"mean"`` computes the average of the values 2024-08-06T21:24:13.1617474Z in the bag, ``"max"`` computes the max value over each bag. 2024-08-06T21:24:13.1617669Z Default: ``"mean"`` 2024-08-06T21:24:13.1618240Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-08-06T21:24:13.1618695Z Notes for more details regarding sparse gradients. Note: this option is not 2024-08-06T21:24:13.1618921Z supported when ``mode="max"``. 2024-08-06T21:24:13.1619659Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-08-06T21:24:13.1620053Z is equivalent to the size of `indices`. This matches the CSR format. 2024-08-06T21:24:13.1620666Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-08-06T21:24:13.1621153Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-08-06T21:24:13.1621593Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-08-06T21:24:13.1622078Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-08-06T21:24:13.1622518Z zeros, but can be updated to another value to be used as the padding vector. 2024-08-06T21:24:13.1622979Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-08-06T21:24:13.1623156Z reduction. 2024-08-06T21:24:13.1623292Z 2024-08-06T21:24:13.1623452Z Attributes: 2024-08-06T21:24:13.1624017Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-08-06T21:24:13.1624250Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-08-06T21:24:13.1624402Z 2024-08-06T21:24:13.1624555Z Examples:: 2024-08-06T21:24:13.1624687Z 2024-08-06T21:24:13.1624994Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-08-06T21:24:13.1625264Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-08-06T21:24:13.1625488Z >>> # a batch of 2 samples of 4 indices each 2024-08-06T21:24:13.1625845Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-08-06T21:24:13.1626098Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-06T21:24:13.1626346Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-06T21:24:13.1626533Z >>> embedding_sum(input, offsets) 2024-08-06T21:24:13.1626808Z tensor([[-0.8861, -5.4350, -0.0523], 2024-08-06T21:24:13.1626993Z [ 1.1306, -2.5798, -1.0044]]) 2024-08-06T21:24:13.1627126Z 2024-08-06T21:24:13.1627312Z >>> # Example with padding_idx 2024-08-06T21:24:13.1627690Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-08-06T21:24:13.1628012Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-08-06T21:24:13.1628304Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-06T21:24:13.1628507Z >>> embedding_sum(input, offsets) 2024-08-06T21:24:13.1628680Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-08-06T21:24:13.1628845Z [-0.7082, 3.2145, -2.6251]]) 2024-08-06T21:24:13.1628998Z 2024-08-06T21:24:13.1629302Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-08-06T21:24:13.1629564Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-08-06T21:24:13.1629832Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-08-06T21:24:13.1630004Z embedding.weight, 2024-08-06T21:24:13.1630229Z padding_idx=embedding.padding_idx, 2024-08-06T21:24:13.1630385Z mode='sum') 2024-08-06T21:24:13.1630521Z 2024-08-06T21:24:13.1630987Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1631123Z 2024-08-06T21:24:13.1631288Z warnings.warn(msg) 2024-08-06T21:24:13.1631443Z 2024-08-06T21:24:13.1631785Z --- Parse Warning: 62 / 100 --- 2024-08-06T21:24:13.1633822Z /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=1745. 2024-08-06T21:24:13.1634306Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1634442Z 2024-08-06T21:24:13.1634860Z Context manager for training with uneven inputs across processes in DDP. 2024-08-06T21:24:13.1635015Z 2024-08-06T21:24:13.1635408Z This context manager will keep track of already-joined DDP processes, 2024-08-06T21:24:13.1635787Z and "shadow" the forward and backward passes by inserting collective 2024-08-06T21:24:13.1636214Z communication operations to match with the ones created by non-joined 2024-08-06T21:24:13.1636628Z DDP processes. This will ensure each collective call has a corresponding 2024-08-06T21:24:13.1637010Z call by already-joined DDP processes, preventing hangs or errors that 2024-08-06T21:24:13.1637380Z would otherwise happen when training with uneven inputs across 2024-08-06T21:24:13.1637805Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-08-06T21:24:13.1638172Z specified to be ``True``, all trainers will throw an error once one rank 2024-08-06T21:24:13.1638532Z runs out of inputs, allowing these errors to be caught and handled 2024-08-06T21:24:13.1638720Z according to application logic. 2024-08-06T21:24:13.1638863Z 2024-08-06T21:24:13.1639263Z Once all DDP processes have joined, the context manager will broadcast 2024-08-06T21:24:13.1639660Z the model corresponding to the last joined process to all processes to 2024-08-06T21:24:13.1639909Z ensure the model is the same across all processes 2024-08-06T21:24:13.1640106Z (which is guaranteed by DDP). 2024-08-06T21:24:13.1640243Z 2024-08-06T21:24:13.1640598Z To use this to enable training with uneven inputs across processes, 2024-08-06T21:24:13.1641037Z simply wrap this context manager around your training loop. No further 2024-08-06T21:24:13.1641342Z modifications to the model or data loading is required. 2024-08-06T21:24:13.1641499Z 2024-08-06T21:24:13.1641655Z .. warning:: 2024-08-06T21:24:13.1642021Z If the model or training loop this context manager is wrapped around 2024-08-06T21:24:13.1642353Z has additional distributed collective operations, such as 2024-08-06T21:24:13.1642833Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-08-06T21:24:13.1643198Z ``throw_on_early_termination`` must be enabled. This is because this 2024-08-06T21:24:13.1643582Z context manager is not aware of non-DDP collective communication. 2024-08-06T21:24:13.1643877Z This flag will cause all ranks to throw when any one rank 2024-08-06T21:24:13.1644323Z exhausts inputs, allowing these errors to be caught and recovered 2024-08-06T21:24:13.1644509Z from across all ranks. 2024-08-06T21:24:13.1644650Z 2024-08-06T21:24:13.1644792Z Args: 2024-08-06T21:24:13.1645132Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-08-06T21:24:13.1645488Z gradients by the initial ``world_size`` DDP training was launched 2024-08-06T21:24:13.1645770Z with. If ``False``, will compute the effective world size 2024-08-06T21:24:13.1646110Z (number of ranks that have not depleted their inputs yet) and 2024-08-06T21:24:13.1646356Z divide gradients by that during allreduce. Set 2024-08-06T21:24:13.1646676Z ``divide_by_initial_world_size=True`` to ensure every input 2024-08-06T21:24:13.1647042Z sample including the uneven inputs have equal weight in terms of 2024-08-06T21:24:13.1647334Z how much they contribute to the global gradient. This is 2024-08-06T21:24:13.1647645Z achieved by always dividing the gradient by the initial 2024-08-06T21:24:13.1647966Z ``world_size`` even when we encounter uneven inputs. If you set 2024-08-06T21:24:13.1648337Z this to ``False``, we divide the gradient by the remaining 2024-08-06T21:24:13.1648691Z number of nodes. This ensures parity with training on a smaller 2024-08-06T21:24:13.1649005Z ``world_size`` although it also means the uneven inputs would 2024-08-06T21:24:13.1649344Z contribute more towards the global gradient. Typically, you 2024-08-06T21:24:13.1649679Z would want to set this to ``True`` for cases where the last few 2024-08-06T21:24:13.1650020Z inputs of your training job are uneven. In extreme cases, where 2024-08-06T21:24:13.1650357Z there is a large discrepancy in the number of inputs, setting 2024-08-06T21:24:13.1650599Z this to ``False`` might provide better results. 2024-08-06T21:24:13.1650968Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-08-06T21:24:13.1651277Z in ``enable=False`` to disable in cases where you know that 2024-08-06T21:24:13.1651604Z inputs are even across participating processes. Default is 2024-08-06T21:24:13.1651747Z ``True``. 2024-08-06T21:24:13.1652077Z throw_on_early_termination (bool): Whether to throw an error 2024-08-06T21:24:13.1652377Z or continue training when at least one rank has exhausted 2024-08-06T21:24:13.1652696Z inputs. If ``True``, will throw upon the first rank reaching end 2024-08-06T21:24:13.1652994Z of data. If ``False``, will continue training with a smaller 2024-08-06T21:24:13.1653335Z effective world size until all ranks are joined. Note that if 2024-08-06T21:24:13.1653533Z this flag is specified, then the flag 2024-08-06T21:24:13.1653846Z ``divide_by_initial_world_size`` would be ignored. Default 2024-08-06T21:24:13.1654000Z is ``False``. 2024-08-06T21:24:13.1654152Z 2024-08-06T21:24:13.1654336Z 2024-08-06T21:24:13.1654478Z Example:: 2024-08-06T21:24:13.1654622Z 2024-08-06T21:24:13.1654812Z >>> # xdoctest: +SKIP("Distributed") 2024-08-06T21:24:13.1654966Z >>> import torch 2024-08-06T21:24:13.1655170Z >>> import torch.distributed as dist 2024-08-06T21:24:13.1655316Z >>> import os 2024-08-06T21:24:13.1655521Z >>> import torch.multiprocessing as mp 2024-08-06T21:24:13.1655698Z >>> import torch.nn as nn 2024-08-06T21:24:13.1655864Z >>> # On each spawned worker 2024-08-06T21:24:13.1656019Z >>> def worker(rank): 2024-08-06T21:24:13.1656338Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-08-06T21:24:13.1656525Z >>> torch.cuda.set_device(rank) 2024-08-06T21:24:13.1656745Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-08-06T21:24:13.1657081Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-08-06T21:24:13.1657313Z >>> model, device_ids=[rank], output_device=rank 2024-08-06T21:24:13.1657455Z >>> ) 2024-08-06T21:24:13.1657687Z >>> # Rank 1 gets one more input than rank 0. 2024-08-06T21:24:13.1658003Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-08-06T21:24:13.1658168Z >>> with model.join(): 2024-08-06T21:24:13.1658340Z >>> for _ in range(5): 2024-08-06T21:24:13.1658511Z >>> for inp in inputs: 2024-08-06T21:24:13.1658694Z >>> loss = model(inp).sum() 2024-08-06T21:24:13.1658880Z >>> loss.backward() 2024-08-06T21:24:13.1659209Z >>> # Without the join() API, the below synchronization will hang 2024-08-06T21:24:13.1659453Z >>> # blocking for rank 1's allreduce to complete. 2024-08-06T21:24:13.1659664Z >>> torch.cuda.synchronize(device=rank) 2024-08-06T21:24:13.1659798Z 2024-08-06T21:24:13.1660259Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1660396Z 2024-08-06T21:24:13.1660553Z warnings.warn(msg) 2024-08-06T21:24:13.1660704Z 2024-08-06T21:24:13.1661130Z --- Parse Warning: 63 / 100 --- 2024-08-06T21:24:13.1663164Z /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=2036. 2024-08-06T21:24:13.1663648Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1663786Z 2024-08-06T21:24:13.1664335Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-08-06T21:24:13.1664484Z 2024-08-06T21:24:13.1664849Z Registers an optimizer with DDP such that the optimization for a 2024-08-06T21:24:13.1665210Z parameter will run immediately when that parameter's gradient is 2024-08-06T21:24:13.1665580Z finished with reduction, instead of waiting for all parameters' 2024-08-06T21:24:13.1665962Z gradients to finish reduction. This can result in a training speedup 2024-08-06T21:24:13.1666361Z depending on your workload since the optimizer can run while gradient 2024-08-06T21:24:13.1666846Z reduction for other parameters are still ongoing. In addition, this has 2024-08-06T21:24:13.1667244Z the potential to reduce peak memory consumption during training, as it 2024-08-06T21:24:13.1667604Z only needs to load the per-parameter optimizer states of a single 2024-08-06T21:24:13.1667973Z parameter at a time, instead of loading all per-parameter optimizer 2024-08-06T21:24:13.1668126Z states at once. 2024-08-06T21:24:13.1668281Z 2024-08-06T21:24:13.1668422Z Args: 2024-08-06T21:24:13.1668762Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-08-06T21:24:13.1668940Z as a fused optimizer. 2024-08-06T21:24:13.1669261Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-08-06T21:24:13.1669634Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-08-06T21:24:13.1670046Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-08-06T21:24:13.1670399Z Optimizers. If this is omitted, all DDP model parameters will be 2024-08-06T21:24:13.1670562Z optimized. 2024-08-06T21:24:13.1670902Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-08-06T21:24:13.1671038Z 2024-08-06T21:24:13.1671202Z .. warning :: 2024-08-06T21:24:13.1671577Z _register_fused_optim should only be called once on a DDP instance, 2024-08-06T21:24:13.1671941Z and registering multiple fused optimizers for the same DDP model 2024-08-06T21:24:13.1672207Z is not currently supported. Please ping 2024-08-06T21:24:13.1672615Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-06T21:24:13.1672778Z for your use case. 2024-08-06T21:24:13.1672920Z 2024-08-06T21:24:13.1673068Z .. warning :: 2024-08-06T21:24:13.1673412Z _register_fused_optim and register_comm_hook currently do not 2024-08-06T21:24:13.1673799Z compose together, meaning that custom DDP communication hooks are 2024-08-06T21:24:13.1674088Z not supported with overlapped optimizers. Please ping 2024-08-06T21:24:13.1674484Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-06T21:24:13.1674651Z for your use case. 2024-08-06T21:24:13.1674788Z 2024-08-06T21:24:13.1674934Z .. warning :: 2024-08-06T21:24:13.1675338Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-08-06T21:24:13.1675545Z with overlapped optimizer. Please ping 2024-08-06T21:24:13.1675958Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-06T21:24:13.1676119Z for your use case. 2024-08-06T21:24:13.1676251Z 2024-08-06T21:24:13.1676410Z Example:: 2024-08-06T21:24:13.1676545Z 2024-08-06T21:24:13.1676823Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-08-06T21:24:13.1677373Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-08-06T21:24:13.1677708Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-08-06T21:24:13.1677853Z >>> lr = 1e-2 2024-08-06T21:24:13.1678023Z >>> betas = (0.9, 0.99) 2024-08-06T21:24:13.1678164Z >>> eps = 1e-6 2024-08-06T21:24:13.1678559Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-08-06T21:24:13.1678766Z >>> # Example with subset of parameters 2024-08-06T21:24:13.1678990Z >>> params_to_opt = [list(net.parameters())[0]] 2024-08-06T21:24:13.1679170Z >>> net._register_fused_optim( 2024-08-06T21:24:13.1679579Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-08-06T21:24:13.1679719Z ... ) 2024-08-06T21:24:13.1679850Z 2024-08-06T21:24:13.1680320Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1680451Z 2024-08-06T21:24:13.1680609Z warnings.warn(msg) 2024-08-06T21:24:13.1680760Z 2024-08-06T21:24:13.1681085Z --- Parse Warning: 64 / 100 --- 2024-08-06T21:24:13.1682960Z /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. 2024-08-06T21:24:13.1683431Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1683805Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-08-06T21:24:13.1683958Z 2024-08-06T21:24:13.1684449Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-06T21:24:13.1684971Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-06T21:24:13.1685439Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-06T21:24:13.1685991Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-06T21:24:13.1686130Z 2024-08-06T21:24:13.1686289Z .. note:: 2024-08-06T21:24:13.1686704Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-06T21:24:13.1687096Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-08-06T21:24:13.1687481Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-06T21:24:13.1687875Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-06T21:24:13.1688330Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-06T21:24:13.1688721Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-08-06T21:24:13.1689081Z even in cases where we have to apply permutation to input tensors. 2024-08-06T21:24:13.1689232Z 2024-08-06T21:24:13.1689631Z Hence our strategy here is to convert only the weight of convolution to 2024-08-06T21:24:13.1689821Z channels_last. This ensures that; 2024-08-06T21:24:13.1690219Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-06T21:24:13.1690625Z outweigh overhead of permutation (if input is not in the same format) 2024-08-06T21:24:13.1691039Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-06T21:24:13.1691241Z from memory_format conversion. 2024-08-06T21:24:13.1691384Z 2024-08-06T21:24:13.1691797Z The optimal case is that, layers between convolution layers are channels 2024-08-06T21:24:13.1692218Z last compatible. Input tensor would be permuted to channels last when it 2024-08-06T21:24:13.1692690Z encounters the first convolution layer and stay in that memory format. 2024-08-06T21:24:13.1693134Z Hence following convolutions will not need to permute its input tensor. 2024-08-06T21:24:13.1693277Z 2024-08-06T21:24:13.1693671Z In case where a channels last incompatible layer is between convolution 2024-08-06T21:24:13.1694057Z layers, we need to permute the input tensor back to contiguous format 2024-08-06T21:24:13.1694448Z for that layer. The input tensor will go through the remaining layers in 2024-08-06T21:24:13.1694848Z contiguous format and be permuted to channels last when it encounters 2024-08-06T21:24:13.1695233Z another convolution layer. There's no point in propagating that 2024-08-06T21:24:13.1695619Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-06T21:24:13.1695785Z ``memory_format``. 2024-08-06T21:24:13.1695940Z 2024-08-06T21:24:13.1696358Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-06T21:24:13.1696768Z there might have been a better spot to fuse the permutation other than 2024-08-06T21:24:13.1696969Z immediately before a convolution. 2024-08-06T21:24:13.1697107Z 2024-08-06T21:24:13.1697261Z Args: 2024-08-06T21:24:13.1697628Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-08-06T21:24:13.1697800Z ``nn.Module`` 2024-08-06T21:24:13.1698070Z memory_format: user specified ``memory_format``, 2024-08-06T21:24:13.1698379Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-06T21:24:13.1698520Z 2024-08-06T21:24:13.1698684Z Returns: 2024-08-06T21:24:13.1698929Z The original module with updated ``nn.Conv2d`` 2024-08-06T21:24:13.1699106Z 2024-08-06T21:24:13.1699267Z Example: 2024-08-06T21:24:13.1699504Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:13.1699772Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-06T21:24:13.1700188Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-08-06T21:24:13.1700362Z >>> model = nn.Sequential( 2024-08-06T21:24:13.1700551Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-08-06T21:24:13.1700745Z >>> # This is identical to: 2024-08-06T21:24:13.1701178Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-06T21:24:13.1701664Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-06T21:24:13.1701867Z >>> out = model(input) 2024-08-06T21:24:13.1702004Z 2024-08-06T21:24:13.1702476Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1702617Z 2024-08-06T21:24:13.1702783Z warnings.warn(msg) 2024-08-06T21:24:13.1702936Z 2024-08-06T21:24:13.1703265Z --- Parse Warning: 65 / 100 --- 2024-08-06T21:24:13.1705131Z /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. 2024-08-06T21:24:13.1705609Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1705985Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-08-06T21:24:13.1706483Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-06T21:24:13.1707054Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-06T21:24:13.1707512Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-06T21:24:13.1708157Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-06T21:24:13.1708299Z 2024-08-06T21:24:13.1708449Z .. note:: 2024-08-06T21:24:13.1708885Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-06T21:24:13.1709280Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-08-06T21:24:13.1709640Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-06T21:24:13.1710051Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-06T21:24:13.1710444Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-06T21:24:13.1710832Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-08-06T21:24:13.1711200Z even in cases where we have to apply permutation to input tensors. 2024-08-06T21:24:13.1711342Z 2024-08-06T21:24:13.1711757Z Hence our strategy here is to convert only the weight of convolution to 2024-08-06T21:24:13.1711949Z channels_last. This ensures that; 2024-08-06T21:24:13.1712334Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-06T21:24:13.1712752Z outweigh overhead of permutation (if input is not in the same format) 2024-08-06T21:24:13.1713172Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-06T21:24:13.1713360Z from memory_format conversion. 2024-08-06T21:24:13.1713511Z 2024-08-06T21:24:13.1713912Z The optimal case is that, layers between convolution layers are channels 2024-08-06T21:24:13.1714333Z last compatible. Input tensor would be permuted to channels last when it 2024-08-06T21:24:13.1714755Z encounters the first convolution layer and stay in that memory format. 2024-08-06T21:24:13.1715220Z Hence following convolutions will not need to permute its input tensor. 2024-08-06T21:24:13.1715365Z 2024-08-06T21:24:13.1715767Z In case where a channels last incompatible layer is between convolution 2024-08-06T21:24:13.1716137Z layers, we need to permute the input tensor back to contiguous format 2024-08-06T21:24:13.1716551Z for that layer. The input tensor will go through the remaining layers in 2024-08-06T21:24:13.1716945Z contiguous format and be permuted to channels last when it encounters 2024-08-06T21:24:13.1717311Z another convolution layer. There's no point in propagating that 2024-08-06T21:24:13.1717708Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-06T21:24:13.1717907Z ``memory_format``. 2024-08-06T21:24:13.1718045Z 2024-08-06T21:24:13.1718474Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-06T21:24:13.1718869Z there might have been a better spot to fuse the permutation other than 2024-08-06T21:24:13.1719072Z immediately before a convolution. 2024-08-06T21:24:13.1719225Z 2024-08-06T21:24:13.1719367Z Args: 2024-08-06T21:24:13.1719736Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-08-06T21:24:13.1719920Z ``nn.Module`` 2024-08-06T21:24:13.1720173Z memory_format: user specified ``memory_format``, 2024-08-06T21:24:13.1720477Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-06T21:24:13.1720628Z 2024-08-06T21:24:13.1720772Z Returns: 2024-08-06T21:24:13.1721026Z The original module with updated ``nn.Conv3d`` 2024-08-06T21:24:13.1721167Z 2024-08-06T21:24:13.1721313Z Example: 2024-08-06T21:24:13.1721557Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:13.1721823Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-06T21:24:13.1722289Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-08-06T21:24:13.1722477Z >>> model = nn.Sequential( 2024-08-06T21:24:13.1722669Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-08-06T21:24:13.1722841Z >>> # This is identical to: 2024-08-06T21:24:13.1723285Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-08-06T21:24:13.1723770Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-06T21:24:13.1723935Z >>> out = model(input) 2024-08-06T21:24:13.1724085Z 2024-08-06T21:24:13.1724544Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1724682Z 2024-08-06T21:24:13.1724855Z warnings.warn(msg) 2024-08-06T21:24:13.1724996Z 2024-08-06T21:24:13.1725329Z --- Parse Warning: 66 / 100 --- 2024-08-06T21:24:13.1727020Z /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=937. 2024-08-06T21:24:13.1727493Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1727913Z Prune tensor by removing random channels along the specified dimension. 2024-08-06T21:24:13.1728052Z 2024-08-06T21:24:13.1728462Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-06T21:24:13.1728856Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-06T21:24:13.1729097Z along the specified ``dim`` selected at random. 2024-08-06T21:24:13.1729442Z Modifies module in place (and also return the modified module) 2024-08-06T21:24:13.1729599Z by: 2024-08-06T21:24:13.1729774Z 2024-08-06T21:24:13.1730138Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-06T21:24:13.1730542Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-06T21:24:13.1730908Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-06T21:24:13.1731272Z original (unpruned) parameter is stored in a new parameter named 2024-08-06T21:24:13.1731441Z ``name+'_orig'``. 2024-08-06T21:24:13.1731578Z 2024-08-06T21:24:13.1731724Z Args: 2024-08-06T21:24:13.1732051Z module (nn.Module): module containing the tensor to prune 2024-08-06T21:24:13.1732371Z name (str): parameter name within ``module`` on which pruning 2024-08-06T21:24:13.1732537Z will act. 2024-08-06T21:24:13.1732864Z amount (int or float): quantity of parameters to prune. 2024-08-06T21:24:13.1733163Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-06T21:24:13.1733527Z fraction of parameters to prune. If ``int``, it represents the 2024-08-06T21:24:13.1733756Z absolute number of parameters to prune. 2024-08-06T21:24:13.1734105Z dim (int): index of the dim along which we define channels to prune. 2024-08-06T21:24:13.1734262Z 2024-08-06T21:24:13.1734405Z Returns: 2024-08-06T21:24:13.1734790Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-06T21:24:13.1734938Z 2024-08-06T21:24:13.1735084Z Examples: 2024-08-06T21:24:13.1735251Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1735462Z >>> m = prune.random_structured( 2024-08-06T21:24:13.1735688Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-08-06T21:24:13.1735834Z ... ) 2024-08-06T21:24:13.1736159Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-08-06T21:24:13.1736341Z >>> print(columns_pruned) 2024-08-06T21:24:13.1736487Z 3 2024-08-06T21:24:13.1736632Z 2024-08-06T21:24:13.1737146Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1737294Z 2024-08-06T21:24:13.1737455Z warnings.warn(msg) 2024-08-06T21:24:13.1737592Z 2024-08-06T21:24:13.1737915Z --- Parse Warning: 67 / 100 --- 2024-08-06T21:24:13.1739545Z /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=978. 2024-08-06T21:24:13.1740012Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1740582Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-08-06T21:24:13.1740723Z 2024-08-06T21:24:13.1741135Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-06T21:24:13.1741531Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-06T21:24:13.1741842Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-08-06T21:24:13.1742184Z Modifies module in place (and also return the modified module) 2024-08-06T21:24:13.1742339Z by: 2024-08-06T21:24:13.1742623Z 2024-08-06T21:24:13.1742987Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-06T21:24:13.1743385Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-06T21:24:13.1743754Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-06T21:24:13.1744130Z original (unpruned) parameter is stored in a new parameter named 2024-08-06T21:24:13.1744291Z ``name+'_orig'``. 2024-08-06T21:24:13.1744430Z 2024-08-06T21:24:13.1744581Z Args: 2024-08-06T21:24:13.1744887Z module (nn.Module): module containing the tensor to prune 2024-08-06T21:24:13.1745301Z name (str): parameter name within ``module`` on which pruning 2024-08-06T21:24:13.1745471Z will act. 2024-08-06T21:24:13.1745763Z amount (int or float): quantity of parameters to prune. 2024-08-06T21:24:13.1746064Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-06T21:24:13.1746430Z fraction of parameters to prune. If ``int``, it represents the 2024-08-06T21:24:13.1746714Z absolute number of parameters to prune. 2024-08-06T21:24:13.1747046Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-08-06T21:24:13.1747323Z entries for argument ``p`` in :func:`torch.norm`. 2024-08-06T21:24:13.1747668Z dim (int): index of the dim along which we define channels to prune. 2024-08-06T21:24:13.1748151Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-08-06T21:24:13.1748487Z shape as module parameter) used to compute mask for pruning. 2024-08-06T21:24:13.1748884Z The values in this tensor indicate the importance of the corresponding 2024-08-06T21:24:13.1749112Z elements in the parameter being pruned. 2024-08-06T21:24:13.1749513Z If unspecified or None, the module parameter will be used in its place. 2024-08-06T21:24:13.1749656Z 2024-08-06T21:24:13.1749812Z Returns: 2024-08-06T21:24:13.1750195Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-06T21:24:13.1750331Z 2024-08-06T21:24:13.1750493Z Examples: 2024-08-06T21:24:13.1750700Z >>> from torch.nn.utils import prune 2024-08-06T21:24:13.1750878Z >>> m = prune.ln_structured( 2024-08-06T21:24:13.1751203Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-08-06T21:24:13.1751348Z ... ) 2024-08-06T21:24:13.1751491Z 2024-08-06T21:24:13.1751976Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1752113Z 2024-08-06T21:24:13.1752364Z warnings.warn(msg) 2024-08-06T21:24:13.1752517Z 2024-08-06T21:24:13.1752851Z --- Parse Warning: 68 / 100 --- 2024-08-06T21:24:13.1754559Z /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=1025. 2024-08-06T21:24:13.1755032Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1755173Z 2024-08-06T21:24:13.1755986Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-08-06T21:24:13.1757174Z 2024-08-06T21:24:13.1757558Z Modifies modules in place by: 2024-08-06T21:24:13.1758066Z 2024-08-06T21:24:13.1758613Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-06T21:24:13.1759590Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-06T21:24:13.1760567Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-06T21:24:13.1761517Z original (unpruned) parameter is stored in a new parameter named 2024-08-06T21:24:13.1762274Z ``name+'_orig'``. 2024-08-06T21:24:13.1762703Z 2024-08-06T21:24:13.1763029Z Args: 2024-08-06T21:24:13.1763585Z parameters (Iterable of (module, name) tuples): parameters of 2024-08-06T21:24:13.1764502Z the model to prune in a global fashion, i.e. by aggregating all 2024-08-06T21:24:13.1765414Z weights prior to deciding which ones to prune. module must be of 2024-08-06T21:24:13.1766276Z type :class:`nn.Module`, and name must be a string. 2024-08-06T21:24:13.1767155Z pruning_method (function): a valid pruning function from this module, 2024-08-06T21:24:13.1768123Z or a custom one implemented by the user that satisfies the 2024-08-06T21:24:13.1769074Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-08-06T21:24:13.1770196Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-08-06T21:24:13.1771237Z the corresponding parameter's importance scores tensor. The tensor 2024-08-06T21:24:13.1772225Z should be the same shape as the parameter, and is used for computing 2024-08-06T21:24:13.1772990Z mask for pruning. 2024-08-06T21:24:13.1773680Z If unspecified or None, the parameter will be used in place of its 2024-08-06T21:24:13.1774435Z importance scores. 2024-08-06T21:24:13.1774966Z kwargs: other keyword arguments such as: 2024-08-06T21:24:13.1775778Z amount (int or float): quantity of parameters to prune across the 2024-08-06T21:24:13.1776512Z specified parameters. 2024-08-06T21:24:13.1777164Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-06T21:24:13.1778045Z fraction of parameters to prune. If ``int``, it represents the 2024-08-06T21:24:13.1778830Z absolute number of parameters to prune. 2024-08-06T21:24:13.1779411Z 2024-08-06T21:24:13.1779745Z Raises: 2024-08-06T21:24:13.1780203Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-08-06T21:24:13.1780811Z 2024-08-06T21:24:13.1781138Z Note: 2024-08-06T21:24:13.1781714Z Since global structured pruning doesn't make much sense unless the 2024-08-06T21:24:13.1782684Z norm is normalized by the size of the parameter, we now limit the 2024-08-06T21:24:13.1783515Z scope of global pruning to unstructured methods. 2024-08-06T21:24:13.1784114Z 2024-08-06T21:24:13.1784462Z Examples: 2024-08-06T21:24:13.1784892Z >>> from torch.nn.utils import prune 2024-08-06T21:24:13.1785488Z >>> from collections import OrderedDict 2024-08-06T21:24:13.1786103Z >>> net = nn.Sequential(OrderedDict([ 2024-08-06T21:24:13.1786843Z ... ('first', nn.Linear(10, 4)), 2024-08-06T21:24:13.1787396Z ... ('second', nn.Linear(4, 1)), 2024-08-06T21:24:13.1787921Z ... ])) 2024-08-06T21:24:13.1788333Z >>> parameters_to_prune = ( 2024-08-06T21:24:13.1788856Z ... (net.first, 'weight'), 2024-08-06T21:24:13.1789392Z ... (net.second, 'weight'), 2024-08-06T21:24:13.1789904Z ... ) 2024-08-06T21:24:13.1790287Z >>> prune.global_unstructured( 2024-08-06T21:24:13.1790842Z ... parameters_to_prune, 2024-08-06T21:24:13.1791428Z ... pruning_method=prune.L1Unstructured, 2024-08-06T21:24:13.1792007Z ... amount=10, 2024-08-06T21:24:13.1792431Z ... ) 2024-08-06T21:24:13.1793037Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-08-06T21:24:13.1793777Z tensor(10) 2024-08-06T21:24:13.1794156Z 2024-08-06T21:24:13.1794484Z 2024-08-06T21:24:13.1795113Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1795940Z 2024-08-06T21:24:13.1796274Z warnings.warn(msg) 2024-08-06T21:24:13.1796705Z 2024-08-06T21:24:13.1797236Z --- Parse Warning: 69 / 100 --- 2024-08-06T21:24:13.1799280Z /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=1144. 2024-08-06T21:24:13.1801619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1803030Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-08-06T21:24:13.1804106Z 2024-08-06T21:24:13.1804662Z Modifies module in place (and also return the modified module) by: 2024-08-06T21:24:13.1805454Z 2024-08-06T21:24:13.1806014Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-06T21:24:13.1806990Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-06T21:24:13.1807974Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-06T21:24:13.1808939Z original (unpruned) parameter is stored in a new parameter named 2024-08-06T21:24:13.1809679Z ``name+'_orig'``. 2024-08-06T21:24:13.1810127Z 2024-08-06T21:24:13.1810461Z Args: 2024-08-06T21:24:13.1810981Z module (nn.Module): module containing the tensor to prune 2024-08-06T21:24:13.1811831Z name (str): parameter name within ``module`` on which pruning 2024-08-06T21:24:13.1812535Z will act. 2024-08-06T21:24:13.1813159Z mask (Tensor): binary mask to be applied to the parameter. 2024-08-06T21:24:13.1813830Z 2024-08-06T21:24:13.1814167Z Returns: 2024-08-06T21:24:13.1814783Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-06T21:24:13.1815542Z 2024-08-06T21:24:13.1815877Z Examples: 2024-08-06T21:24:13.1816305Z >>> from torch.nn.utils import prune 2024-08-06T21:24:13.1816912Z >>> m = prune.custom_from_mask( 2024-08-06T21:24:13.1817601Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-08-06T21:24:13.1818250Z ... ) 2024-08-06T21:24:13.1818656Z >>> print(m.bias_mask) 2024-08-06T21:24:13.1819167Z tensor([0., 1., 0.]) 2024-08-06T21:24:13.1819618Z 2024-08-06T21:24:13.1819955Z 2024-08-06T21:24:13.1820536Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1821159Z 2024-08-06T21:24:13.1821406Z warnings.warn(msg) 2024-08-06T21:24:13.1821686Z 2024-08-06T21:24:13.1822058Z --- Parse Warning: 70 / 100 --- 2024-08-06T21:24:13.1823468Z /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=106. 2024-08-06T21:24:13.1824953Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1825816Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-08-06T21:24:13.1826478Z 2024-08-06T21:24:13.1827014Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-08-06T21:24:13.1827777Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-08-06T21:24:13.1828344Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-08-06T21:24:13.1828775Z (UAI 2018). 2024-08-06T21:24:13.1828993Z 2024-08-06T21:24:13.1829331Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-08-06T21:24:13.1829919Z but using exponential weights instead of equal weights across iterations. 2024-08-06T21:24:13.1830356Z 2024-08-06T21:24:13.1830720Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-08-06T21:24:13.1831316Z on the device :attr:`device` and allows to compute running averages of the 2024-08-06T21:24:13.1831770Z parameters of the :attr:`model`. 2024-08-06T21:24:13.1832084Z 2024-08-06T21:24:13.1832291Z Args: 2024-08-06T21:24:13.1832568Z model (torch.nn.Module): model to use with SWA/EMA 2024-08-06T21:24:13.1833088Z device (torch.device, optional): if provided, the averaged model will be 2024-08-06T21:24:13.1833567Z stored on the :attr:`device` 2024-08-06T21:24:13.1833996Z avg_fn (function, optional): the averaging function used to update 2024-08-06T21:24:13.1834544Z parameters; the function must take in the current value of the 2024-08-06T21:24:13.1835141Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-08-06T21:24:13.1835685Z parameter, and the number of models already averaged; if None, 2024-08-06T21:24:13.1836183Z an equally weighted average is used (default: None) 2024-08-06T21:24:13.1836700Z multi_avg_fn (function, optional): the averaging function used to update 2024-08-06T21:24:13.1837286Z parameters inplace; the function must take in the current values of the 2024-08-06T21:24:13.1837930Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-08-06T21:24:13.1838560Z parameters as a list, and the number of models already averaged; if None, 2024-08-06T21:24:13.1839092Z an equally weighted average is used (default: None) 2024-08-06T21:24:13.1839608Z use_buffers (bool): if ``True``, it will compute running averages for 2024-08-06T21:24:13.1840171Z both the parameters and the buffers of the model. (default: ``False``) 2024-08-06T21:24:13.1840611Z 2024-08-06T21:24:13.1840813Z Example: 2024-08-06T21:24:13.1841095Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.1841479Z >>> loader, optimizer, model, loss_fn = ... 2024-08-06T21:24:13.1841892Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-08-06T21:24:13.1842641Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-08-06T21:24:13.1843126Z >>> T_max=300) 2024-08-06T21:24:13.1843449Z >>> swa_start = 160 2024-08-06T21:24:13.1843793Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-08-06T21:24:13.1844162Z >>> for i in range(300): 2024-08-06T21:24:13.1844482Z >>> for input, target in loader: 2024-08-06T21:24:13.1844832Z >>> optimizer.zero_grad() 2024-08-06T21:24:13.1845204Z >>> loss_fn(model(input), target).backward() 2024-08-06T21:24:13.1845559Z >>> optimizer.step() 2024-08-06T21:24:13.1846038Z >>> if i > swa_start: 2024-08-06T21:24:13.1846386Z >>> swa_model.update_parameters(model) 2024-08-06T21:24:13.1846743Z >>> swa_scheduler.step() 2024-08-06T21:24:13.1847063Z >>> else: 2024-08-06T21:24:13.1847345Z >>> scheduler.step() 2024-08-06T21:24:13.1847637Z >>> 2024-08-06T21:24:13.1847937Z >>> # Update bn statistics for the swa_model at the end 2024-08-06T21:24:13.1848377Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-08-06T21:24:13.1848730Z 2024-08-06T21:24:13.1849150Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-08-06T21:24:13.1849790Z If no averaging function is provided, the default is to compute 2024-08-06T21:24:13.1850251Z equally-weighted average of the weights (SWA). 2024-08-06T21:24:13.1850603Z 2024-08-06T21:24:13.1850808Z Example: 2024-08-06T21:24:13.1851079Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.1851547Z >>> # Compute exponential moving averages of the weights and buffers 2024-08-06T21:24:13.1852073Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-08-06T21:24:13.1852594Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-08-06T21:24:13.1853019Z 2024-08-06T21:24:13.1853240Z .. note:: 2024-08-06T21:24:13.1853621Z When using SWA/EMA with models containing Batch Normalization you may 2024-08-06T21:24:13.1854171Z need to update the activation statistics for Batch Normalization. 2024-08-06T21:24:13.1854750Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-08-06T21:24:13.1855400Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-08-06T21:24:13.1855997Z statistics in a post-training step by passing data through the model. The 2024-08-06T21:24:13.1856615Z second does it during the parameter update phase by averaging all buffers. 2024-08-06T21:24:13.1857236Z Empirical evidence has shown that updating the statistics in normalization 2024-08-06T21:24:13.1857833Z layers increases accuracy, but you may wish to empirically test which 2024-08-06T21:24:13.1858357Z approach yields the best results in your problem. 2024-08-06T21:24:13.1858728Z 2024-08-06T21:24:13.1858925Z .. note:: 2024-08-06T21:24:13.1859334Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-08-06T21:24:13.1859843Z 2024-08-06T21:24:13.1860035Z .. note:: 2024-08-06T21:24:13.1860386Z When :meth:`update_parameters` is called for the first time (i.e. 2024-08-06T21:24:13.1860908Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-08-06T21:24:13.1861436Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-08-06T21:24:13.1861949Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-08-06T21:24:13.1862368Z to update the parameters. 2024-08-06T21:24:13.1862665Z 2024-08-06T21:24:13.1862993Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-06T21:24:13.1863469Z https://arxiv.org/abs/1803.05407 2024-08-06T21:24:13.1863945Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-08-06T21:24:13.1864383Z Average: 2024-08-06T21:24:13.1864653Z https://arxiv.org/abs/1806.05594 2024-08-06T21:24:13.1865092Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-08-06T21:24:13.1865524Z https://arxiv.org/abs/1904.11943 2024-08-06T21:24:13.1865984Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-08-06T21:24:13.1866491Z Generalizes Well: 2024-08-06T21:24:13.1866863Z https://arxiv.org/abs/2001.02312 2024-08-06T21:24:13.1867198Z .. _Polyak averaging: 2024-08-06T21:24:13.1867561Z https://paperswithcode.com/method/polyak-averaging 2024-08-06T21:24:13.1867927Z 2024-08-06T21:24:13.1868310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1868771Z 2024-08-06T21:24:13.1868975Z warnings.warn(msg) 2024-08-06T21:24:13.1869230Z 2024-08-06T21:24:13.1869567Z --- Parse Warning: 71 / 100 --- 2024-08-06T21:24:13.1870670Z /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=357. 2024-08-06T21:24:13.1871905Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1872520Z Anneals the learning rate in each parameter group to a fixed value. 2024-08-06T21:24:13.1872932Z 2024-08-06T21:24:13.1873283Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-08-06T21:24:13.1873852Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-08-06T21:24:13.1874262Z 2024-08-06T21:24:13.1874467Z Args: 2024-08-06T21:24:13.1874780Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-08-06T21:24:13.1875274Z swa_lrs (float or list): the learning rate value for all param groups 2024-08-06T21:24:13.1875739Z together or separately for each group. 2024-08-06T21:24:13.1876195Z annealing_epochs (int): number of epochs in the annealing phase 2024-08-06T21:24:13.1876615Z (default: 10) 2024-08-06T21:24:13.1877016Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-08-06T21:24:13.1877611Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-08-06T21:24:13.1878039Z (default: "cos") 2024-08-06T21:24:13.1878414Z last_epoch (int): the index of the last epoch (default: -1) 2024-08-06T21:24:13.1878806Z 2024-08-06T21:24:13.1879093Z The :class:`SWALR` scheduler can be used together with other 2024-08-06T21:24:13.1879621Z schedulers to switch to a constant learning rate late in the training 2024-08-06T21:24:13.1880067Z as in the example below. 2024-08-06T21:24:13.1880333Z 2024-08-06T21:24:13.1880536Z Example: 2024-08-06T21:24:13.1880807Z >>> # xdoctest: +SKIP("Undefined variables") 2024-08-06T21:24:13.1881162Z >>> loader, optimizer, model = ... 2024-08-06T21:24:13.1881538Z >>> lr_lambda = lambda epoch: 0.9 2024-08-06T21:24:13.1881991Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-08-06T21:24:13.1882430Z >>> lr_lambda=lr_lambda) 2024-08-06T21:24:13.1882827Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-08-06T21:24:13.1883306Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-08-06T21:24:13.1883693Z >>> swa_start = 160 2024-08-06T21:24:13.1883976Z >>> for i in range(300): 2024-08-06T21:24:13.1884298Z >>> for input, target in loader: 2024-08-06T21:24:13.1884633Z >>> optimizer.zero_grad() 2024-08-06T21:24:13.1884999Z >>> loss_fn(model(input), target).backward() 2024-08-06T21:24:13.1885364Z >>> optimizer.step() 2024-08-06T21:24:13.1885673Z >>> if i > swa_start: 2024-08-06T21:24:13.1885994Z >>> swa_scheduler.step() 2024-08-06T21:24:13.1886316Z >>> else: 2024-08-06T21:24:13.1886581Z >>> scheduler.step() 2024-08-06T21:24:13.1886884Z 2024-08-06T21:24:13.1887229Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-06T21:24:13.1887742Z https://arxiv.org/abs/1803.05407 2024-08-06T21:24:13.1888061Z 2024-08-06T21:24:13.1888436Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1888882Z 2024-08-06T21:24:13.1889096Z warnings.warn(msg) 2024-08-06T21:24:13.1889350Z 2024-08-06T21:24:13.1889654Z --- Parse Warning: 72 / 100 --- 2024-08-06T21:24:13.1890828Z /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=111. 2024-08-06T21:24:13.1892147Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1892670Z Register a container-like type as pytree node. 2024-08-06T21:24:13.1893018Z 2024-08-06T21:24:13.1893228Z Args: 2024-08-06T21:24:13.1893549Z cls (type): A Python type to treat as an internal pytree node. 2024-08-06T21:24:13.1894136Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-08-06T21:24:13.1894789Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-08-06T21:24:13.1895449Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-08-06T21:24:13.1895985Z passed to the ``unflatten_fn``. 2024-08-06T21:24:13.1896504Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-08-06T21:24:13.1897176Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-08-06T21:24:13.1897714Z The function should return an instance of ``cls``. 2024-08-06T21:24:13.1898295Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-08-06T21:24:13.1898861Z qualified name used when serializing the tree spec. 2024-08-06T21:24:13.1899454Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-08-06T21:24:13.1900180Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-08-06T21:24:13.1900861Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-08-06T21:24:13.1901557Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-08-06T21:24:13.1902258Z how to convert the custom json dumpable representation of the context back to the 2024-08-06T21:24:13.1902942Z original context. This is used for json deserialization, which is being used in 2024-08-06T21:24:13.1903434Z :mod:`torch.export` right now. 2024-08-06T21:24:13.1903760Z 2024-08-06T21:24:13.1903976Z Example:: 2024-08-06T21:24:13.1904192Z 2024-08-06T21:24:13.1904424Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1904778Z >>> # Registry a Python type with lambda functions 2024-08-06T21:24:13.1905136Z >>> register_pytree_node( 2024-08-06T21:24:13.1905434Z ... set, 2024-08-06T21:24:13.1905718Z ... lambda s: (sorted(s), None, None), 2024-08-06T21:24:13.1906079Z ... lambda children, _: set(children), 2024-08-06T21:24:13.1906406Z ... ) 2024-08-06T21:24:13.1906728Z 2024-08-06T21:24:13.1907097Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1907556Z 2024-08-06T21:24:13.1907773Z warnings.warn(msg) 2024-08-06T21:24:13.1908021Z 2024-08-06T21:24:13.1908342Z --- Parse Warning: 73 / 100 --- 2024-08-06T21:24:13.1909630Z /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=1199. 2024-08-06T21:24:13.1910981Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1911453Z 2024-08-06T21:24:13.1911794Z Context passed to policy function during selective checkpointing. 2024-08-06T21:24:13.1912200Z 2024-08-06T21:24:13.1912549Z This class is used to pass relevant metadata to the policy function during 2024-08-06T21:24:13.1913171Z selective checkpointing. The metadata includes whether the current invocation 2024-08-06T21:24:13.1913714Z of the policy function is during recomputation or not. 2024-08-06T21:24:13.1914074Z 2024-08-06T21:24:13.1914278Z Example: 2024-08-06T21:24:13.1914501Z >>> # xdoctest: +SKIP(stub) 2024-08-06T21:24:13.1914790Z >>> 2024-08-06T21:24:13.1915051Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-06T21:24:13.1915391Z >>> print(ctx.is_recompute) 2024-08-06T21:24:13.1915690Z >>> 2024-08-06T21:24:13.1916097Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-06T21:24:13.1916566Z >>> 2024-08-06T21:24:13.1916838Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-06T21:24:13.1917188Z >>> fn, x, y, 2024-08-06T21:24:13.1917448Z >>> use_reentrant=False, 2024-08-06T21:24:13.1917761Z >>> context_fn=context_fn, 2024-08-06T21:24:13.1918060Z >>> ) 2024-08-06T21:24:13.1918263Z 2024-08-06T21:24:13.1918638Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1919095Z 2024-08-06T21:24:13.1919294Z warnings.warn(msg) 2024-08-06T21:24:13.1919552Z 2024-08-06T21:24:13.1919860Z --- Parse Warning: 74 / 100 --- 2024-08-06T21:24:13.1921093Z /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=1333. 2024-08-06T21:24:13.1922504Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1922977Z 2024-08-06T21:24:13.1923325Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-08-06T21:24:13.1923768Z 2024-08-06T21:24:13.1924104Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-08-06T21:24:13.1924588Z operations are recomputed during the backward pass. 2024-08-06T21:24:13.1924955Z 2024-08-06T21:24:13.1925156Z Args: 2024-08-06T21:24:13.1925389Z policy_fn_or_list (Callable or List): 2024-08-06T21:24:13.1925828Z - If a policy function is provided, it should accept a 2024-08-06T21:24:13.1926355Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-08-06T21:24:13.1926923Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-08-06T21:24:13.1927498Z indicating whether the execution of the op should be recomputed or not. 2024-08-06T21:24:13.1928070Z - If a list of operations is provided, it is equivalent to a policy 2024-08-06T21:24:13.1928570Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-08-06T21:24:13.1929094Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-08-06T21:24:13.1929531Z operations. 2024-08-06T21:24:13.1929899Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-08-06T21:24:13.1930455Z raised if any tensors cached by selective activation checkpoint are 2024-08-06T21:24:13.1931011Z mutated in order to ensure correctness. If set to `True`, this check 2024-08-06T21:24:13.1931427Z is disabled. 2024-08-06T21:24:13.1931686Z Returns: 2024-08-06T21:24:13.1931939Z A tuple of two context managers. 2024-08-06T21:24:13.1932235Z 2024-08-06T21:24:13.1932507Z Example: 2024-08-06T21:24:13.1932752Z >>> # xdoctest: +REQUIRES(LINUX) 2024-08-06T21:24:13.1933058Z >>> import functools 2024-08-06T21:24:13.1933327Z >>> 2024-08-06T21:24:13.1933583Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-08-06T21:24:13.1933945Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-08-06T21:24:13.1934269Z >>> 2024-08-06T21:24:13.1934491Z >>> ops_to_save = [ 2024-08-06T21:24:13.1934771Z >>> torch.ops.aten.mm.default, 2024-08-06T21:24:13.1935091Z >>> ] 2024-08-06T21:24:13.1935310Z >>> 2024-08-06T21:24:13.1935552Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-06T21:24:13.1935906Z >>> if op in ops_to_save: 2024-08-06T21:24:13.1936251Z >>> return CheckpointPolicy.MUST_SAVE 2024-08-06T21:24:13.1936572Z >>> else: 2024-08-06T21:24:13.1936875Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-08-06T21:24:13.1937226Z >>> 2024-08-06T21:24:13.1937625Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-06T21:24:13.1938116Z >>> 2024-08-06T21:24:13.1938346Z >>> # or equivalently 2024-08-06T21:24:13.1938801Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-08-06T21:24:13.1939293Z >>> 2024-08-06T21:24:13.1939517Z >>> def fn(x, y): 2024-08-06T21:24:13.1939887Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-08-06T21:24:13.1940301Z >>> 2024-08-06T21:24:13.1940574Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-06T21:24:13.1940912Z >>> fn, x, y, 2024-08-06T21:24:13.1941190Z >>> use_reentrant=False, 2024-08-06T21:24:13.1941495Z >>> context_fn=context_fn, 2024-08-06T21:24:13.1941824Z >>> ) 2024-08-06T21:24:13.1942046Z 2024-08-06T21:24:13.1942607Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1943074Z 2024-08-06T21:24:13.1943288Z warnings.warn(msg) 2024-08-06T21:24:13.1943530Z 2024-08-06T21:24:13.1943866Z --- Parse Warning: 75 / 100 --- 2024-08-06T21:24:13.1945029Z /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=925. 2024-08-06T21:24:13.1946317Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1946855Z 2024-08-06T21:24:13.1947119Z Create a :class:`setuptools.Extension` for C++. 2024-08-06T21:24:13.1947584Z 2024-08-06T21:24:13.1947946Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-06T21:24:13.1948542Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-08-06T21:24:13.1948962Z 2024-08-06T21:24:13.1949296Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-06T21:24:13.1949780Z constructor. Full list arguments can be found at 2024-08-06T21:24:13.1950368Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-06T21:24:13.1950902Z 2024-08-06T21:24:13.1951107Z Example: 2024-08-06T21:24:13.1951332Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1951667Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-06T21:24:13.1952048Z >>> from setuptools import setup 2024-08-06T21:24:13.1952486Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-08-06T21:24:13.1952920Z >>> setup( 2024-08-06T21:24:13.1953169Z ... name='extension', 2024-08-06T21:24:13.1953448Z ... ext_modules=[ 2024-08-06T21:24:13.1953729Z ... CppExtension( 2024-08-06T21:24:13.1954030Z ... name='extension', 2024-08-06T21:24:13.1954439Z ... sources=['extension.cpp'], 2024-08-06T21:24:13.1954804Z ... extra_compile_args=['-g'], 2024-08-06T21:24:13.1955209Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-08-06T21:24:13.1955566Z ... ], 2024-08-06T21:24:13.1955807Z ... cmdclass={ 2024-08-06T21:24:13.1956103Z ... 'build_ext': BuildExtension 2024-08-06T21:24:13.1956412Z ... }) 2024-08-06T21:24:13.1956641Z 2024-08-06T21:24:13.1957016Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.1957460Z 2024-08-06T21:24:13.1957673Z warnings.warn(msg) 2024-08-06T21:24:13.1957926Z 2024-08-06T21:24:13.1958235Z --- Parse Warning: 76 / 100 --- 2024-08-06T21:24:13.1959392Z /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=976. 2024-08-06T21:24:13.1960698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.1961158Z 2024-08-06T21:24:13.1961437Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-08-06T21:24:13.1961794Z 2024-08-06T21:24:13.1962145Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-06T21:24:13.1962714Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-08-06T21:24:13.1963271Z extension. This includes the CUDA include path, library path and runtime 2024-08-06T21:24:13.1963704Z library. 2024-08-06T21:24:13.1963916Z 2024-08-06T21:24:13.1964240Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-06T21:24:13.1964710Z constructor. Full list arguments can be found at 2024-08-06T21:24:13.1965351Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-06T21:24:13.1965885Z 2024-08-06T21:24:13.1966079Z Example: 2024-08-06T21:24:13.1966312Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.1966652Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-06T21:24:13.1967021Z >>> from setuptools import setup 2024-08-06T21:24:13.1967470Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-08-06T21:24:13.1967915Z >>> setup( 2024-08-06T21:24:13.1968153Z ... name='cuda_extension', 2024-08-06T21:24:13.1968472Z ... ext_modules=[ 2024-08-06T21:24:13.1968758Z ... CUDAExtension( 2024-08-06T21:24:13.1969065Z ... name='cuda_extension', 2024-08-06T21:24:13.1969469Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-06T21:24:13.1969946Z ... extra_compile_args={'cxx': ['-g'], 2024-08-06T21:24:13.1970307Z ... 'nvcc': ['-O2']}, 2024-08-06T21:24:13.1970714Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-08-06T21:24:13.1971086Z ... ], 2024-08-06T21:24:13.1971313Z ... cmdclass={ 2024-08-06T21:24:13.1971606Z ... 'build_ext': BuildExtension 2024-08-06T21:24:13.1971926Z ... }) 2024-08-06T21:24:13.1980689Z 2024-08-06T21:24:13.1980946Z Compute capabilities: 2024-08-06T21:24:13.1981234Z 2024-08-06T21:24:13.1981650Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-08-06T21:24:13.1982379Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-08-06T21:24:13.1983095Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-08-06T21:24:13.1983833Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-08-06T21:24:13.1984542Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-08-06T21:24:13.1985217Z support (see below for details on PTX). 2024-08-06T21:24:13.1985543Z 2024-08-06T21:24:13.1985969Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-08-06T21:24:13.1986530Z CCs you want the extension to support: 2024-08-06T21:24:13.1986953Z 2024-08-06T21:24:13.1987252Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-08-06T21:24:13.1987803Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-08-06T21:24:13.1988252Z 2024-08-06T21:24:13.1988683Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-08-06T21:24:13.1989452Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-08-06T21:24:13.1990206Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-08-06T21:24:13.1990926Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-08-06T21:24:13.1991683Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-08-06T21:24:13.1992409Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-08-06T21:24:13.1993123Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-08-06T21:24:13.1993892Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-08-06T21:24:13.1994433Z "8.0 8.6" would be better. 2024-08-06T21:24:13.1994705Z 2024-08-06T21:24:13.1995118Z Note that while it's possible to include all supported archs, the more archs get included the 2024-08-06T21:24:13.1995837Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-08-06T21:24:13.1996378Z 2024-08-06T21:24:13.1996824Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-08-06T21:24:13.1997499Z To workaround the issue, move python binding logic to pure C++ file. 2024-08-06T21:24:13.1997920Z 2024-08-06T21:24:13.1998119Z Example use: 2024-08-06T21:24:13.1998373Z #include 2024-08-06T21:24:13.1998725Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-08-06T21:24:13.1999061Z 2024-08-06T21:24:13.1999275Z Instead of: 2024-08-06T21:24:13.1999515Z #include 2024-08-06T21:24:13.1999896Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-08-06T21:24:13.2000254Z 2024-08-06T21:24:13.2000640Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-08-06T21:24:13.2001594Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-08-06T21:24:13.2002316Z 2024-08-06T21:24:13.2002555Z Relocatable device code linking: 2024-08-06T21:24:13.2002846Z 2024-08-06T21:24:13.2003252Z If you want to reference device symbols across compilation units (across object files), 2024-08-06T21:24:13.2003930Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-08-06T21:24:13.2004667Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-08-06T21:24:13.2005485Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-08-06T21:24:13.2006261Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-08-06T21:24:13.2006875Z help reduce the protentional perf degradation of `-rdc`. 2024-08-06T21:24:13.2007342Z Note that it needs to be used at both steps to be useful. 2024-08-06T21:24:13.2007716Z 2024-08-06T21:24:13.2008253Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-08-06T21:24:13.2008920Z There is also a case where `-dlink` is used without `-rdc`: 2024-08-06T21:24:13.2009472Z when an extension is linked against a static lib containing rdc-compiled objects 2024-08-06T21:24:13.2009687Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-08-06T21:24:13.2009770Z 2024-08-06T21:24:13.2009986Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-08-06T21:24:13.2010069Z 2024-08-06T21:24:13.2010157Z Example: 2024-08-06T21:24:13.2010271Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.2010421Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-06T21:24:13.2010524Z >>> CUDAExtension( 2024-08-06T21:24:13.2010647Z ... name='cuda_extension', 2024-08-06T21:24:13.2010814Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-06T21:24:13.2010928Z ... dlink=True, 2024-08-06T21:24:13.2011052Z ... dlink_libraries=["dlink_lib"], 2024-08-06T21:24:13.2011179Z ... extra_compile_args={'cxx': ['-g'], 2024-08-06T21:24:13.2011320Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-08-06T21:24:13.2011403Z 2024-08-06T21:24:13.2011659Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2011754Z 2024-08-06T21:24:13.2011852Z warnings.warn(msg) 2024-08-06T21:24:13.2011936Z 2024-08-06T21:24:13.2012210Z --- Parse Warning: 77 / 100 --- 2024-08-06T21:24:13.2013087Z /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=1234. 2024-08-06T21:24:13.2013360Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2013487Z 2024-08-06T21:24:13.2013640Z Load a PyTorch C++ extension just-in-time (JIT). 2024-08-06T21:24:13.2013723Z 2024-08-06T21:24:13.2013945Z To load an extension, a Ninja build file is emitted, which is used to 2024-08-06T21:24:13.2014151Z compile the given sources into a dynamic library. This library is 2024-08-06T21:24:13.2014385Z subsequently loaded into the current Python process as a module and 2024-08-06T21:24:13.2014515Z returned from this function, ready for use. 2024-08-06T21:24:13.2014597Z 2024-08-06T21:24:13.2014821Z By default, the directory to which the build file is emitted and the 2024-08-06T21:24:13.2015057Z resulting library compiled to is ``/torch_extensions/``, where 2024-08-06T21:24:13.2015305Z ```` is the temporary folder on the current platform and ```` 2024-08-06T21:24:13.2015535Z the name of the extension. This location can be overridden in two ways. 2024-08-06T21:24:13.2015746Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-08-06T21:24:13.2015970Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-08-06T21:24:13.2016203Z into subfolders of this directory. Second, if the ``build_directory`` 2024-08-06T21:24:13.2016440Z argument to this function is supplied, it overrides the entire path, i.e. 2024-08-06T21:24:13.2016605Z the library will be compiled into that folder directly. 2024-08-06T21:24:13.2016702Z 2024-08-06T21:24:13.2016914Z To compile the sources, the default system compiler (``c++``) is used, 2024-08-06T21:24:13.2017168Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-08-06T21:24:13.2017395Z additional arguments to the compilation process, ``extra_cflags`` or 2024-08-06T21:24:13.2017622Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-08-06T21:24:13.2017852Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-08-06T21:24:13.2018011Z ``extra_cflags`` to pass further include directories. 2024-08-06T21:24:13.2018152Z 2024-08-06T21:24:13.2018402Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-08-06T21:24:13.2018587Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-08-06T21:24:13.2018832Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-08-06T21:24:13.2019062Z passing the CUDA lib64 directory as a library directory, and linking 2024-08-06T21:24:13.2019215Z ``cudart``. You can pass additional flags to nvcc via 2024-08-06T21:24:13.2019422Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-08-06T21:24:13.2019672Z heuristics for finding the CUDA install directory are used, which usually 2024-08-06T21:24:13.2019889Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-08-06T21:24:13.2020002Z safest option. 2024-08-06T21:24:13.2020086Z 2024-08-06T21:24:13.2020172Z Args: 2024-08-06T21:24:13.2020402Z name: The name of the extension to build. This MUST be the same as the 2024-08-06T21:24:13.2020513Z name of the pybind11 module! 2024-08-06T21:24:13.2020718Z sources: A list of relative or absolute paths to C++ source files. 2024-08-06T21:24:13.2020959Z extra_cflags: optional list of compiler flags to forward to the build. 2024-08-06T21:24:13.2021181Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-08-06T21:24:13.2021288Z when building CUDA sources. 2024-08-06T21:24:13.2021528Z extra_ldflags: optional list of linker flags to forward to the build. 2024-08-06T21:24:13.2021750Z extra_include_paths: optional list of include directories to forward 2024-08-06T21:24:13.2021851Z to the build. 2024-08-06T21:24:13.2022044Z build_directory: optional path to use as build workspace. 2024-08-06T21:24:13.2022252Z verbose: If ``True``, turns on verbose logging of load steps. 2024-08-06T21:24:13.2022503Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-06T21:24:13.2022663Z the build. If set to ``None`` (default), this value is 2024-08-06T21:24:13.2022870Z automatically determined based on the existence of ``.cu`` or 2024-08-06T21:24:13.2023054Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-08-06T21:24:13.2023166Z and libraries to be included. 2024-08-06T21:24:13.2023371Z is_python_module: If ``True`` (default), imports the produced shared 2024-08-06T21:24:13.2023573Z library as a Python module. If ``False``, behavior depends on 2024-08-06T21:24:13.2023672Z ``is_standalone``. 2024-08-06T21:24:13.2023905Z is_standalone: If ``False`` (default) loads the constructed extension 2024-08-06T21:24:13.2024114Z into the process as a plain dynamic library. If ``True``, build a 2024-08-06T21:24:13.2024222Z standalone executable. 2024-08-06T21:24:13.2024308Z 2024-08-06T21:24:13.2024411Z Returns: 2024-08-06T21:24:13.2024524Z If ``is_python_module`` is ``True``: 2024-08-06T21:24:13.2024704Z Returns the loaded PyTorch extension as a Python module. 2024-08-06T21:24:13.2024800Z 2024-08-06T21:24:13.2025002Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-08-06T21:24:13.2025228Z Returns nothing. (The shared library is loaded into the process as 2024-08-06T21:24:13.2025326Z a side effect.) 2024-08-06T21:24:13.2025407Z 2024-08-06T21:24:13.2025533Z If ``is_standalone`` is ``True``. 2024-08-06T21:24:13.2025733Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-08-06T21:24:13.2025909Z added to the PATH environment variable as a side effect.) 2024-08-06T21:24:13.2026008Z 2024-08-06T21:24:13.2026093Z Example: 2024-08-06T21:24:13.2026192Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.2026390Z >>> from torch.utils.cpp_extension import load 2024-08-06T21:24:13.2026485Z >>> module = load( 2024-08-06T21:24:13.2026659Z ... name='extension', 2024-08-06T21:24:13.2026840Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-06T21:24:13.2026941Z ... extra_cflags=['-O2'], 2024-08-06T21:24:13.2027034Z ... verbose=True) 2024-08-06T21:24:13.2027129Z 2024-08-06T21:24:13.2027385Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2027465Z 2024-08-06T21:24:13.2027577Z warnings.warn(msg) 2024-08-06T21:24:13.2027657Z 2024-08-06T21:24:13.2027876Z --- Parse Warning: 78 / 100 --- 2024-08-06T21:24:13.2028803Z /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=1523. 2024-08-06T21:24:13.2029074Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2029171Z 2024-08-06T21:24:13.2029382Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-08-06T21:24:13.2029464Z 2024-08-06T21:24:13.2029717Z This function behaves exactly like :func:`load`, but takes its sources as 2024-08-06T21:24:13.2029947Z strings rather than filenames. These strings are stored to files in the 2024-08-06T21:24:13.2030160Z build directory, after which the behavior of :func:`load_inline` is 2024-08-06T21:24:13.2030280Z identical to :func:`load`. 2024-08-06T21:24:13.2030360Z 2024-08-06T21:24:13.2030447Z See `the 2024-08-06T21:24:13.2030792Z tests `_ 2024-08-06T21:24:13.2030918Z for good examples of using this function. 2024-08-06T21:24:13.2031034Z 2024-08-06T21:24:13.2031281Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-08-06T21:24:13.2031529Z the necessary header includes, as well as the (pybind11) binding code. More 2024-08-06T21:24:13.2031774Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-08-06T21:24:13.2031974Z single ``.cpp`` file. This file is then prepended with ``#include 2024-08-06T21:24:13.2032075Z ``. 2024-08-06T21:24:13.2032170Z 2024-08-06T21:24:13.2032396Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-08-06T21:24:13.2032634Z automatically generated for each function specified. ``functions`` can 2024-08-06T21:24:13.2032867Z either be a list of function names, or a dictionary mapping from function 2024-08-06T21:24:13.2033121Z names to docstrings. If a list is given, the name of each function is used 2024-08-06T21:24:13.2033214Z as its docstring. 2024-08-06T21:24:13.2033310Z 2024-08-06T21:24:13.2033527Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-08-06T21:24:13.2033706Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-08-06T21:24:13.2033927Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-08-06T21:24:13.2034152Z separately, but ultimately linked into a single library. Note that no 2024-08-06T21:24:13.2034388Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-08-06T21:24:13.2034616Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-08-06T21:24:13.2034831Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-08-06T21:24:13.2034943Z include its name in ``functions``). 2024-08-06T21:24:13.2035040Z 2024-08-06T21:24:13.2035223Z See :func:`load` for a description of arguments omitted below. 2024-08-06T21:24:13.2035320Z 2024-08-06T21:24:13.2035406Z Args: 2024-08-06T21:24:13.2035622Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-08-06T21:24:13.2035908Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-08-06T21:24:13.2036113Z functions: A list of function names for which to generate function 2024-08-06T21:24:13.2036325Z bindings. If a dictionary is given, it should map function names to 2024-08-06T21:24:13.2036521Z docstrings (which are otherwise just the function names). 2024-08-06T21:24:13.2036742Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-06T21:24:13.2036901Z the build. If set to ``None`` (default), this value is 2024-08-06T21:24:13.2037118Z automatically determined based on whether ``cuda_sources`` is 2024-08-06T21:24:13.2037273Z provided. Set it to ``True`` to force CUDA headers 2024-08-06T21:24:13.2037382Z and libraries to be included. 2024-08-06T21:24:13.2037608Z with_pytorch_error_handling: Determines whether pytorch error and 2024-08-06T21:24:13.2037810Z warning macros are handled by pytorch instead of pybind. To do 2024-08-06T21:24:13.2038039Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-08-06T21:24:13.2038240Z function. This redirection might cause issues in obscure cases 2024-08-06T21:24:13.2038423Z of cpp. This flag should be set to ``False`` when this redirect 2024-08-06T21:24:13.2038534Z causes issues. 2024-08-06T21:24:13.2038616Z 2024-08-06T21:24:13.2038702Z Example: 2024-08-06T21:24:13.2038862Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-06T21:24:13.2039018Z >>> from torch.utils.cpp_extension import load_inline 2024-08-06T21:24:13.2039113Z >>> source = """ 2024-08-06T21:24:13.2039271Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-08-06T21:24:13.2039370Z return x.sin() + y.sin(); 2024-08-06T21:24:13.2039481Z } 2024-08-06T21:24:13.2039576Z """ 2024-08-06T21:24:13.2039718Z >>> module = load_inline(name='inline_extension', 2024-08-06T21:24:13.2039838Z ... cpp_sources=[source], 2024-08-06T21:24:13.2039966Z ... functions=['sin_add']) 2024-08-06T21:24:13.2040048Z 2024-08-06T21:24:13.2040136Z .. note:: 2024-08-06T21:24:13.2040344Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-08-06T21:24:13.2040551Z extension. This may use up too many resources on some systems. One 2024-08-06T21:24:13.2040786Z can control the number of workers by setting the `MAX_JOBS` environment 2024-08-06T21:24:13.2040901Z variable to a non-negative number. 2024-08-06T21:24:13.2040984Z 2024-08-06T21:24:13.2041275Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2041356Z 2024-08-06T21:24:13.2041455Z warnings.warn(msg) 2024-08-06T21:24:13.2041547Z 2024-08-06T21:24:13.2041749Z --- Parse Warning: 79 / 100 --- 2024-08-06T21:24:13.2042909Z /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. 2024-08-06T21:24:13.2043191Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2043272Z 2024-08-06T21:24:13.2043568Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-08-06T21:24:13.2043663Z 2024-08-06T21:24:13.2043959Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-08-06T21:24:13.2044225Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-08-06T21:24:13.2044456Z server like load. It can emulate multiple calling threads to a single module 2024-08-06T21:24:13.2044699Z provided. In the future we plan to enhance this component to support inter and 2024-08-06T21:24:13.2045062Z intra-op parallelism as well as multiple models running in a single process. 2024-08-06T21:24:13.2045146Z 2024-08-06T21:24:13.2045400Z Please note that even though nn.Module is supported, it might incur an overhead 2024-08-06T21:24:13.2045636Z from the need to hold GIL every time we execute Python code or pass around 2024-08-06T21:24:13.2045878Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-08-06T21:24:13.2046115Z model for inference deployment it is better to switch to using it in this 2024-08-06T21:24:13.2046219Z benchmark. 2024-08-06T21:24:13.2046301Z 2024-08-06T21:24:13.2046393Z Example:: 2024-08-06T21:24:13.2046495Z 2024-08-06T21:24:13.2046615Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.2046761Z >>> from torch.utils import ThroughputBenchmark 2024-08-06T21:24:13.2046902Z >>> bench = ThroughputBenchmark(my_module) 2024-08-06T21:24:13.2047065Z >>> # Pre-populate benchmark's data set with the inputs 2024-08-06T21:24:13.2047168Z >>> for input in inputs: 2024-08-06T21:24:13.2047398Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-08-06T21:24:13.2047526Z ... bench.add_input(input[0], x2=input[1]) 2024-08-06T21:24:13.2047734Z >>> # Inputs supplied above are randomly used during the execution 2024-08-06T21:24:13.2047839Z >>> stats = bench.benchmark( 2024-08-06T21:24:13.2047944Z ... num_calling_threads=4, 2024-08-06T21:24:13.2048060Z ... num_warmup_iters = 100, 2024-08-06T21:24:13.2048157Z ... num_iters = 1000, 2024-08-06T21:24:13.2048243Z ... ) 2024-08-06T21:24:13.2048436Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-08-06T21:24:13.2048610Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-08-06T21:24:13.2048744Z 2024-08-06T21:24:13.2049014Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2049095Z 2024-08-06T21:24:13.2049193Z warnings.warn(msg) 2024-08-06T21:24:13.2049292Z 2024-08-06T21:24:13.2049494Z --- Parse Warning: 80 / 100 --- 2024-08-06T21:24:13.2050442Z /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. 2024-08-06T21:24:13.2050717Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2050918Z Sampler that restricts data loading to a subset of the dataset. 2024-08-06T21:24:13.2051052Z 2024-08-06T21:24:13.2051185Z It is especially useful in conjunction with 2024-08-06T21:24:13.2051433Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-08-06T21:24:13.2051714Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-08-06T21:24:13.2051944Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-08-06T21:24:13.2052073Z original dataset that is exclusive to it. 2024-08-06T21:24:13.2052168Z 2024-08-06T21:24:13.2052258Z .. note:: 2024-08-06T21:24:13.2052494Z Dataset is assumed to be of constant size and that any instance of it always 2024-08-06T21:24:13.2052648Z returns the same elements in the same order. 2024-08-06T21:24:13.2052734Z 2024-08-06T21:24:13.2052823Z Args: 2024-08-06T21:24:13.2052958Z dataset: Dataset used for sampling. 2024-08-06T21:24:13.2053176Z num_replicas (int, optional): Number of processes participating in 2024-08-06T21:24:13.2053431Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-08-06T21:24:13.2053561Z current distributed group. 2024-08-06T21:24:13.2053849Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-08-06T21:24:13.2054064Z By default, :attr:`rank` is retrieved from the current distributed 2024-08-06T21:24:13.2054151Z group. 2024-08-06T21:24:13.2054375Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-08-06T21:24:13.2054478Z indices. 2024-08-06T21:24:13.2054669Z seed (int, optional): random seed used to shuffle the sampler if 2024-08-06T21:24:13.2054867Z :attr:`shuffle=True`. This number should be identical across all 2024-08-06T21:24:13.2055043Z processes in the distributed group. Default: ``0``. 2024-08-06T21:24:13.2055259Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-08-06T21:24:13.2055453Z tail of the data to make it evenly divisible across the number of 2024-08-06T21:24:13.2055662Z replicas. If ``False``, the sampler will add extra indices to make 2024-08-06T21:24:13.2055885Z the data evenly divisible across the replicas. Default: ``False``. 2024-08-06T21:24:13.2055963Z 2024-08-06T21:24:13.2056052Z .. warning:: 2024-08-06T21:24:13.2056253Z In distributed mode, calling the :meth:`set_epoch` method at 2024-08-06T21:24:13.2056503Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-08-06T21:24:13.2056773Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-08-06T21:24:13.2056895Z the same ordering will be always used. 2024-08-06T21:24:13.2056971Z 2024-08-06T21:24:13.2057071Z Example:: 2024-08-06T21:24:13.2057155Z 2024-08-06T21:24:13.2057251Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.2057480Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-08-06T21:24:13.2057679Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-08-06T21:24:13.2057794Z ... sampler=sampler) 2024-08-06T21:24:13.2057941Z >>> for epoch in range(start_epoch, n_epochs): 2024-08-06T21:24:13.2058043Z ... if is_distributed: 2024-08-06T21:24:13.2058154Z ... sampler.set_epoch(epoch) 2024-08-06T21:24:13.2058260Z ... train(loader) 2024-08-06T21:24:13.2058343Z 2024-08-06T21:24:13.2058595Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2058688Z 2024-08-06T21:24:13.2058786Z warnings.warn(msg) 2024-08-06T21:24:13.2058865Z 2024-08-06T21:24:13.2059072Z --- Parse Warning: 81 / 100 --- 2024-08-06T21:24:13.2059944Z /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. 2024-08-06T21:24:13.2060218Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2060300Z 2024-08-06T21:24:13.2060516Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-08-06T21:24:13.2060719Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-08-06T21:24:13.2060936Z pushes the map into PyTorch operations called by ``func``, effectively 2024-08-06T21:24:13.2061042Z vectorizing those operations. 2024-08-06T21:24:13.2061129Z 2024-08-06T21:24:13.2061340Z vmap is useful for handling batch dimensions: one can write a function 2024-08-06T21:24:13.2061535Z ``func`` that runs on examples and then lift it to a function that can 2024-08-06T21:24:13.2061752Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-08-06T21:24:13.2061922Z compute batched gradients when composed with autograd. 2024-08-06T21:24:13.2062009Z 2024-08-06T21:24:13.2062101Z .. note:: 2024-08-06T21:24:13.2062335Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-08-06T21:24:13.2062468Z convenience. Use whichever one you'd like. 2024-08-06T21:24:13.2062553Z 2024-08-06T21:24:13.2062634Z Args: 2024-08-06T21:24:13.2062839Z func (function): A Python function that takes one or more arguments. 2024-08-06T21:24:13.2062958Z Must return one or more Tensors. 2024-08-06T21:24:13.2063159Z in_dims (int or nested structure): Specifies which dimension of the 2024-08-06T21:24:13.2063340Z inputs should be mapped over. ``in_dims`` should have a 2024-08-06T21:24:13.2063528Z structure like the inputs. If the ``in_dim`` for a particular 2024-08-06T21:24:13.2063713Z input is None, then that indicates there is no map dimension. 2024-08-06T21:24:13.2063811Z Default: 0. 2024-08-06T21:24:13.2064001Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-08-06T21:24:13.2064191Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-08-06T21:24:13.2064352Z it should have one element per output. Default: 0. 2024-08-06T21:24:13.2064532Z randomness (str): Specifies whether the randomness in this 2024-08-06T21:24:13.2064737Z vmap should be the same or different across batches. If 'different', 2024-08-06T21:24:13.2064942Z the randomness for each batch will be different. If 'same', the 2024-08-06T21:24:13.2065154Z randomness will be the same across batches. If 'error', any calls to 2024-08-06T21:24:13.2065374Z random functions will error. Default: 'error'. WARNING: this flag 2024-08-06T21:24:13.2065571Z only applies to random PyTorch operations and does not apply to 2024-08-06T21:24:13.2065704Z Python's random module or numpy randomness. 2024-08-06T21:24:13.2065967Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-08-06T21:24:13.2066185Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-08-06T21:24:13.2066440Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-08-06T21:24:13.2066799Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-08-06T21:24:13.2066880Z 2024-08-06T21:24:13.2066962Z Returns: 2024-08-06T21:24:13.2067160Z Returns a new "batched" function. It takes the same inputs as 2024-08-06T21:24:13.2067341Z ``func``, except each input has an extra dimension at the index 2024-08-06T21:24:13.2067526Z specified by ``in_dims``. It takes returns the same outputs as 2024-08-06T21:24:13.2067722Z ``func``, except each output has an extra dimension at the index 2024-08-06T21:24:13.2067850Z specified by ``out_dims``. 2024-08-06T21:24:13.2067937Z 2024-08-06T21:24:13.2068024Z .. warning: 2024-08-06T21:24:13.2068220Z :func:`vmap` works best with functional-style code. Please do not 2024-08-06T21:24:13.2068414Z perform any side-effects in ``func``, with the exception of 2024-08-06T21:24:13.2068650Z in-place PyTorch operations. Examples of side-effects include mutating 2024-08-06T21:24:13.2068870Z Python data structures and assigning values to variables not captured 2024-08-06T21:24:13.2068962Z in ``func``. 2024-08-06T21:24:13.2069043Z 2024-08-06T21:24:13.2069273Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-08-06T21:24:13.2069497Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-08-06T21:24:13.2069716Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-08-06T21:24:13.2069796Z 2024-08-06T21:24:13.2069947Z >>> torch.dot # [D], [D] -> [] 2024-08-06T21:24:13.2070149Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-08-06T21:24:13.2070277Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-06T21:24:13.2070431Z >>> batched_dot(x, y) 2024-08-06T21:24:13.2070514Z 2024-08-06T21:24:13.2070736Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-08-06T21:24:13.2070850Z model authoring experience. 2024-08-06T21:24:13.2070928Z 2024-08-06T21:24:13.2071035Z >>> batch_size, feature_size = 3, 5 2024-08-06T21:24:13.2071211Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-08-06T21:24:13.2071298Z >>> 2024-08-06T21:24:13.2071406Z >>> def model(feature_vec): 2024-08-06T21:24:13.2071540Z >>> # Very simple linear model with activation 2024-08-06T21:24:13.2071665Z >>> return feature_vec.dot(weights).relu() 2024-08-06T21:24:13.2071755Z >>> 2024-08-06T21:24:13.2071901Z >>> examples = torch.randn(batch_size, feature_size) 2024-08-06T21:24:13.2072023Z >>> result = torch.vmap(model)(examples) 2024-08-06T21:24:13.2072110Z 2024-08-06T21:24:13.2072359Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-08-06T21:24:13.2072591Z or impossible to batch. One example is higher-order gradient computation. 2024-08-06T21:24:13.2072825Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-08-06T21:24:13.2073050Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-08-06T21:24:13.2073294Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-08-06T21:24:13.2073536Z we can vectorize the whole computation, computing the Jacobian in a single 2024-08-06T21:24:13.2073636Z call to ``autograd.grad``. 2024-08-06T21:24:13.2073718Z 2024-08-06T21:24:13.2073808Z >>> # Setup 2024-08-06T21:24:13.2073892Z >>> N = 5 2024-08-06T21:24:13.2073987Z >>> f = lambda x: x ** 2 2024-08-06T21:24:13.2074158Z >>> x = torch.randn(N, requires_grad=True) 2024-08-06T21:24:13.2074241Z >>> y = f(x) 2024-08-06T21:24:13.2074348Z >>> I_N = torch.eye(N) 2024-08-06T21:24:13.2074426Z >>> 2024-08-06T21:24:13.2074523Z >>> # Sequential approach 2024-08-06T21:24:13.2074746Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-08-06T21:24:13.2074851Z >>> for v in I_N.unbind()] 2024-08-06T21:24:13.2074966Z >>> jacobian = torch.stack(jacobian_rows) 2024-08-06T21:24:13.2075058Z >>> 2024-08-06T21:24:13.2075170Z >>> # vectorized gradient computation 2024-08-06T21:24:13.2075263Z >>> def get_vjp(v): 2024-08-06T21:24:13.2075393Z >>> return torch.autograd.grad(y, x, v) 2024-08-06T21:24:13.2075506Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-08-06T21:24:13.2075611Z 2024-08-06T21:24:13.2075879Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-08-06T21:24:13.2075963Z 2024-08-06T21:24:13.2076107Z >>> torch.dot # [D], [D] -> [] 2024-08-06T21:24:13.2076391Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-08-06T21:24:13.2076523Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-08-06T21:24:13.2076647Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-08-06T21:24:13.2076735Z 2024-08-06T21:24:13.2076975Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-08-06T21:24:13.2077125Z the dimension that each inputs are batched along as 2024-08-06T21:24:13.2077218Z 2024-08-06T21:24:13.2077359Z >>> torch.dot # [N], [N] -> [] 2024-08-06T21:24:13.2077588Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-08-06T21:24:13.2077712Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-06T21:24:13.2077952Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-08-06T21:24:13.2078046Z 2024-08-06T21:24:13.2078369Z If there are multiple inputs each of which is batched along different dimensions, 2024-08-06T21:24:13.2078562Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-08-06T21:24:13.2078653Z 2024-08-06T21:24:13.2078788Z >>> torch.dot # [D], [D] -> [] 2024-08-06T21:24:13.2079013Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-08-06T21:24:13.2079142Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-06T21:24:13.2079379Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-08-06T21:24:13.2079457Z 2024-08-06T21:24:13.2079705Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-08-06T21:24:13.2079809Z matching the shape of the input: 2024-08-06T21:24:13.2079889Z 2024-08-06T21:24:13.2080034Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-08-06T21:24:13.2080154Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-06T21:24:13.2080259Z >>> input = {'x': x, 'y': y} 2024-08-06T21:24:13.2080435Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-08-06T21:24:13.2080532Z >>> batched_dot(input) 2024-08-06T21:24:13.2080617Z 2024-08-06T21:24:13.2080891Z By default, the output is batched along the first dimension. However, it can be batched 2024-08-06T21:24:13.2081012Z along any dimension by using ``out_dims`` 2024-08-06T21:24:13.2081101Z 2024-08-06T21:24:13.2081196Z >>> f = lambda x: x ** 2 2024-08-06T21:24:13.2081292Z >>> x = torch.randn(2, 5) 2024-08-06T21:24:13.2081420Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-08-06T21:24:13.2081519Z >>> batched_pow(x) # [5, 2] 2024-08-06T21:24:13.2081599Z 2024-08-06T21:24:13.2081921Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-08-06T21:24:13.2082008Z accept kwargs 2024-08-06T21:24:13.2082092Z 2024-08-06T21:24:13.2082196Z >>> x = torch.randn([2, 5]) 2024-08-06T21:24:13.2082289Z >>> def fn(x, scale=4.): 2024-08-06T21:24:13.2082382Z >>> return x * scale 2024-08-06T21:24:13.2082474Z >>> 2024-08-06T21:24:13.2082579Z >>> batched_pow = torch.vmap(fn) 2024-08-06T21:24:13.2082714Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-08-06T21:24:13.2082940Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-08-06T21:24:13.2083019Z 2024-08-06T21:24:13.2083109Z .. note:: 2024-08-06T21:24:13.2083333Z vmap does not provide general autobatching or handle variable-length 2024-08-06T21:24:13.2083458Z sequences out of the box. 2024-08-06T21:24:13.2083538Z 2024-08-06T21:24:13.2083795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2083876Z 2024-08-06T21:24:13.2083972Z warnings.warn(msg) 2024-08-06T21:24:13.2084065Z 2024-08-06T21:24:13.2084281Z --- Parse Warning: 82 / 100 --- 2024-08-06T21:24:13.2085162Z /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=14. 2024-08-06T21:24:13.2085428Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2085684Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-08-06T21:24:13.2085783Z 2024-08-06T21:24:13.2086024Z Use this instead of :func:`torch.library.custom_op` when the implementation 2024-08-06T21:24:13.2086248Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-08-06T21:24:13.2086437Z custom operators as opaque (:func:`torch.compile` and 2024-08-06T21:24:13.2086726Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-08-06T21:24:13.2086953Z makes the implementation visible to these subsystems, allowing them 2024-08-06T21:24:13.2087081Z to optimize the triton kernel(s). 2024-08-06T21:24:13.2087163Z 2024-08-06T21:24:13.2087368Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-08-06T21:24:13.2087598Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-08-06T21:24:13.2087806Z must be wrapped in a call to :func:`torch._library.capture_triton``. 2024-08-06T21:24:13.2087904Z 2024-08-06T21:24:13.2087990Z Args: 2024-08-06T21:24:13.2088216Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-08-06T21:24:13.2088447Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-08-06T21:24:13.2088619Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-08-06T21:24:13.2088862Z To avoid name collisions, please use your project name as the namespace; 2024-08-06T21:24:13.2089085Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-08-06T21:24:13.2089362Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-08-06T21:24:13.2089618Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-08-06T21:24:13.2089884Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-08-06T21:24:13.2090071Z schema (None | str): A schema string for the operator. If None 2024-08-06T21:24:13.2090296Z (recommended) we'll infer a schema for the operator from its type 2024-08-06T21:24:13.2090506Z annotations. We recommend letting us infer a schema unless you 2024-08-06T21:24:13.2090650Z have a specific reason not to. 2024-08-06T21:24:13.2090815Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-08-06T21:24:13.2090904Z 2024-08-06T21:24:13.2090997Z Example:: 2024-08-06T21:24:13.2091096Z 2024-08-06T21:24:13.2091235Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-06T21:24:13.2091330Z >>> import torch 2024-08-06T21:24:13.2091514Z >>> from torch._library import triton_op, capture_triton 2024-08-06T21:24:13.2091598Z >>> 2024-08-06T21:24:13.2091695Z >>> import triton 2024-08-06T21:24:13.2091832Z >>> from triton import language as tl 2024-08-06T21:24:13.2091917Z >>> 2024-08-06T21:24:13.2092011Z >>> @triton.jit 2024-08-06T21:24:13.2092119Z >>> def add_kernel( 2024-08-06T21:24:13.2092236Z >>> in_ptr0, 2024-08-06T21:24:13.2092326Z >>> in_ptr1, 2024-08-06T21:24:13.2092429Z >>> out_ptr, 2024-08-06T21:24:13.2092527Z >>> n_elements, 2024-08-06T21:24:13.2092659Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-06T21:24:13.2092745Z >>> ): 2024-08-06T21:24:13.2092858Z >>> pid = tl.program_id(axis=0) 2024-08-06T21:24:13.2092982Z >>> block_start = pid * BLOCK_SIZE 2024-08-06T21:24:13.2093133Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-06T21:24:13.2093242Z >>> mask = offsets < n_elements 2024-08-06T21:24:13.2093385Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-06T21:24:13.2093510Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-06T21:24:13.2093608Z >>> output = x + y 2024-08-06T21:24:13.2093762Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-06T21:24:13.2093849Z >>> 2024-08-06T21:24:13.2093980Z >>> @triton_op("mylib::add", mutates_args={}) 2024-08-06T21:24:13.2094174Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-08-06T21:24:13.2094288Z >>> output = torch.empty_like(x) 2024-08-06T21:24:13.2094449Z >>> n_elements = output.numel() 2024-08-06T21:24:13.2094545Z >>> 2024-08-06T21:24:13.2094643Z >>> def grid(meta): 2024-08-06T21:24:13.2094813Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-06T21:24:13.2094909Z >>> 2024-08-06T21:24:13.2095101Z >>> # NB: we need to wrap the triton kernel in a call to capture_triton 2024-08-06T21:24:13.2095314Z >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-08-06T21:24:13.2095410Z >>> return output 2024-08-06T21:24:13.2095493Z >>> 2024-08-06T21:24:13.2095606Z >>> @torch.compile 2024-08-06T21:24:13.2095702Z >>> def f(x, y): 2024-08-06T21:24:13.2095800Z >>> return add(x, y) 2024-08-06T21:24:13.2095901Z >>> 2024-08-06T21:24:13.2096016Z >>> x = torch.randn(3, device="cuda") 2024-08-06T21:24:13.2096136Z >>> y = torch.randn(3, device="cuda") 2024-08-06T21:24:13.2096234Z >>> 2024-08-06T21:24:13.2096326Z >>> z = f(x, y) 2024-08-06T21:24:13.2096442Z >>> assert torch.allclose(z, x + y) 2024-08-06T21:24:13.2096538Z 2024-08-06T21:24:13.2096621Z 2024-08-06T21:24:13.2096876Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2096969Z 2024-08-06T21:24:13.2097065Z warnings.warn(msg) 2024-08-06T21:24:13.2097147Z 2024-08-06T21:24:13.2097360Z --- Parse Warning: 83 / 100 --- 2024-08-06T21:24:13.2098256Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=capture_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=124. 2024-08-06T21:24:13.2098553Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2098762Z Allows capture of a triton kernel into a graph via make_fx or 2024-08-06T21:24:13.2098875Z non-strict export (coming soon). 2024-08-06T21:24:13.2098970Z 2024-08-06T21:24:13.2099156Z These technologies perform Dispatcher-based tracing (via 2024-08-06T21:24:13.2099356Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-08-06T21:24:13.2099571Z The ``capture_triton`` API returns a new callable that can actually 2024-08-06T21:24:13.2099673Z be traced into a graph. 2024-08-06T21:24:13.2099753Z 2024-08-06T21:24:13.2099855Z Examples: 2024-08-06T21:24:13.2099936Z 2024-08-06T21:24:13.2100035Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.2100168Z >>> import torch 2024-08-06T21:24:13.2100261Z >>> import triton 2024-08-06T21:24:13.2100382Z >>> from triton import language as tl 2024-08-06T21:24:13.2100580Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-06T21:24:13.2100814Z >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton 2024-08-06T21:24:13.2100899Z >>> 2024-08-06T21:24:13.2101007Z >>> @triton.jit 2024-08-06T21:24:13.2101101Z >>> def add_kernel( 2024-08-06T21:24:13.2101192Z >>> in_ptr0, 2024-08-06T21:24:13.2101295Z >>> in_ptr1, 2024-08-06T21:24:13.2101384Z >>> out_ptr, 2024-08-06T21:24:13.2101477Z >>> n_elements, 2024-08-06T21:24:13.2101605Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-06T21:24:13.2101688Z >>> ): 2024-08-06T21:24:13.2101801Z >>> pid = tl.program_id(axis=0) 2024-08-06T21:24:13.2101930Z >>> block_start = pid * BLOCK_SIZE 2024-08-06T21:24:13.2102080Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-06T21:24:13.2102192Z >>> mask = offsets < n_elements 2024-08-06T21:24:13.2102335Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-06T21:24:13.2102510Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-06T21:24:13.2102622Z >>> output = x + y 2024-08-06T21:24:13.2102767Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-06T21:24:13.2102851Z >>> 2024-08-06T21:24:13.2102958Z >>> def add(x, y): 2024-08-06T21:24:13.2103072Z >>> output = torch.empty_like(x) 2024-08-06T21:24:13.2103185Z >>> n_elements = output.numel() 2024-08-06T21:24:13.2103279Z >>> 2024-08-06T21:24:13.2103379Z >>> def grid_fn(meta): 2024-08-06T21:24:13.2103549Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-06T21:24:13.2103647Z >>> 2024-08-06T21:24:13.2103857Z >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-08-06T21:24:13.2103957Z >>> return output 2024-08-06T21:24:13.2104052Z >>> 2024-08-06T21:24:13.2104170Z >>> x = torch.randn(3, device="cuda") 2024-08-06T21:24:13.2104284Z >>> y = torch.randn(3, device="cuda") 2024-08-06T21:24:13.2104397Z >>> gm = make_fx(add)(x, y) 2024-08-06T21:24:13.2104496Z >>> print(gm.code) 2024-08-06T21:24:13.2104607Z >>> # def forward(self, x_1, y_1): 2024-08-06T21:24:13.2104856Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-08-06T21:24:13.2105101Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-08-06T21:24:13.2105242Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-08-06T21:24:13.2105357Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-08-06T21:24:13.2105511Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-08-06T21:24:13.2105671Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-08-06T21:24:13.2105761Z >>> # }) 2024-08-06T21:24:13.2105867Z >>> # return empty_like 2024-08-06T21:24:13.2105958Z 2024-08-06T21:24:13.2106041Z 2024-08-06T21:24:13.2106296Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2106389Z 2024-08-06T21:24:13.2106485Z warnings.warn(msg) 2024-08-06T21:24:13.2106566Z 2024-08-06T21:24:13.2106867Z --- Parse Warning: 84 / 100 --- 2024-08-06T21:24:13.2107807Z /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. 2024-08-06T21:24:13.2108107Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2108205Z 2024-08-06T21:24:13.2108411Z Raises an AssertionError if two items are not equal up to desired 2024-08-06T21:24:13.2108514Z precision. 2024-08-06T21:24:13.2108599Z 2024-08-06T21:24:13.2108775Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-06T21:24:13.2108967Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-06T21:24:13.2109154Z instead of this function for more consistent floating point 2024-08-06T21:24:13.2109250Z comparisons. 2024-08-06T21:24:13.2109342Z 2024-08-06T21:24:13.2109559Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-08-06T21:24:13.2109640Z 2024-08-06T21:24:13.2109811Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-08-06T21:24:13.2109892Z 2024-08-06T21:24:13.2110124Z That is a looser test than originally documented, but agrees with what the 2024-08-06T21:24:13.2110370Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-08-06T21:24:13.2110609Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-08-06T21:24:13.2110795Z delegates to assert_array_almost_equal 2024-08-06T21:24:13.2110891Z 2024-08-06T21:24:13.2110982Z Parameters 2024-08-06T21:24:13.2111073Z ---------- 2024-08-06T21:24:13.2111183Z actual : array_like 2024-08-06T21:24:13.2111281Z The object to check. 2024-08-06T21:24:13.2111377Z desired : array_like 2024-08-06T21:24:13.2111493Z The expected object. 2024-08-06T21:24:13.2111591Z decimal : int, optional 2024-08-06T21:24:13.2111705Z Desired precision, default is 7. 2024-08-06T21:24:13.2111814Z err_msg : str, optional 2024-08-06T21:24:13.2111968Z The error message to be printed in case of failure. 2024-08-06T21:24:13.2112069Z verbose : bool, optional 2024-08-06T21:24:13.2112291Z If True, the conflicting values are appended to the error message. 2024-08-06T21:24:13.2112373Z 2024-08-06T21:24:13.2112470Z Raises 2024-08-06T21:24:13.2112556Z ------ 2024-08-06T21:24:13.2112648Z AssertionError 2024-08-06T21:24:13.2112859Z If actual and desired are not equal up to specified precision. 2024-08-06T21:24:13.2112940Z 2024-08-06T21:24:13.2113025Z See Also 2024-08-06T21:24:13.2113125Z -------- 2024-08-06T21:24:13.2113361Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-06T21:24:13.2113488Z relative and/or absolute precision. 2024-08-06T21:24:13.2113712Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-06T21:24:13.2113794Z 2024-08-06T21:24:13.2113876Z Examples 2024-08-06T21:24:13.2113974Z -------- 2024-08-06T21:24:13.2114138Z >>> from torch._numpy.testing import assert_almost_equal 2024-08-06T21:24:13.2114272Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-08-06T21:24:13.2114457Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-08-06T21:24:13.2114596Z Traceback (most recent call last): 2024-08-06T21:24:13.2114677Z ... 2024-08-06T21:24:13.2114787Z AssertionError: 2024-08-06T21:24:13.2114914Z Arrays are not almost equal to 10 decimals 2024-08-06T21:24:13.2115004Z ACTUAL: 2.3333333333333 2024-08-06T21:24:13.2115110Z DESIRED: 2.33333334 2024-08-06T21:24:13.2115196Z 2024-08-06T21:24:13.2115343Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-08-06T21:24:13.2115482Z ... np.array([1.0,2.33333334]), decimal=9) 2024-08-06T21:24:13.2115591Z Traceback (most recent call last): 2024-08-06T21:24:13.2115675Z ... 2024-08-06T21:24:13.2115785Z AssertionError: 2024-08-06T21:24:13.2115906Z Arrays are not almost equal to 9 decimals 2024-08-06T21:24:13.2115993Z 2024-08-06T21:24:13.2116134Z Mismatched elements: 1 / 2 (50%) 2024-08-06T21:24:13.2116261Z Max absolute difference: 6.666699636781459e-09 2024-08-06T21:24:13.2116390Z Max relative difference: 2.8571569790287484e-09 2024-08-06T21:24:13.2116544Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-06T21:24:13.2116681Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-06T21:24:13.2116776Z 2024-08-06T21:24:13.2116859Z 2024-08-06T21:24:13.2117113Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2117206Z 2024-08-06T21:24:13.2117306Z warnings.warn(msg) 2024-08-06T21:24:13.2117387Z 2024-08-06T21:24:13.2117591Z --- Parse Warning: 85 / 100 --- 2024-08-06T21:24:13.2118522Z /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. 2024-08-06T21:24:13.2118786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2118881Z 2024-08-06T21:24:13.2119104Z Raises an AssertionError if two items are not equal up to significant 2024-08-06T21:24:13.2119187Z digits. 2024-08-06T21:24:13.2119279Z 2024-08-06T21:24:13.2119502Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-06T21:24:13.2119681Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-06T21:24:13.2119877Z instead of this function for more consistent floating point 2024-08-06T21:24:13.2119972Z comparisons. 2024-08-06T21:24:13.2120050Z 2024-08-06T21:24:13.2120246Z Given two numbers, check that they are approximately equal. 2024-08-06T21:24:13.2120463Z Approximately equal is defined as the number of significant digits 2024-08-06T21:24:13.2120557Z that agree. 2024-08-06T21:24:13.2120636Z 2024-08-06T21:24:13.2120723Z Parameters 2024-08-06T21:24:13.2120819Z ---------- 2024-08-06T21:24:13.2120907Z actual : scalar 2024-08-06T21:24:13.2121004Z The object to check. 2024-08-06T21:24:13.2121105Z desired : scalar 2024-08-06T21:24:13.2121202Z The expected object. 2024-08-06T21:24:13.2121304Z significant : int, optional 2024-08-06T21:24:13.2121425Z Desired precision, default is 7. 2024-08-06T21:24:13.2121521Z err_msg : str, optional 2024-08-06T21:24:13.2121678Z The error message to be printed in case of failure. 2024-08-06T21:24:13.2121784Z verbose : bool, optional 2024-08-06T21:24:13.2121987Z If True, the conflicting values are appended to the error message. 2024-08-06T21:24:13.2122067Z 2024-08-06T21:24:13.2122158Z Raises 2024-08-06T21:24:13.2122240Z ------ 2024-08-06T21:24:13.2122332Z AssertionError 2024-08-06T21:24:13.2122528Z If actual and desired are not equal up to specified precision. 2024-08-06T21:24:13.2122609Z 2024-08-06T21:24:13.2122699Z See Also 2024-08-06T21:24:13.2122789Z -------- 2024-08-06T21:24:13.2123026Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-06T21:24:13.2123224Z relative and/or absolute precision. 2024-08-06T21:24:13.2123444Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-06T21:24:13.2123525Z 2024-08-06T21:24:13.2123614Z Examples 2024-08-06T21:24:13.2123706Z -------- 2024-08-06T21:24:13.2123980Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-08-06T21:24:13.2124236Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-08-06T21:24:13.2124362Z ... significant=8) 2024-08-06T21:24:13.2124611Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-08-06T21:24:13.2124736Z ... significant=8) 2024-08-06T21:24:13.2124872Z Traceback (most recent call last): 2024-08-06T21:24:13.2124956Z ... 2024-08-06T21:24:13.2125059Z AssertionError: 2024-08-06T21:24:13.2125187Z Items are not equal to 8 significant digits: 2024-08-06T21:24:13.2125283Z ACTUAL: 1.234567e-21 2024-08-06T21:24:13.2125385Z DESIRED: 1.2345672e-21 2024-08-06T21:24:13.2125470Z 2024-08-06T21:24:13.2125630Z the evaluated condition that raises the exception is 2024-08-06T21:24:13.2125720Z 2024-08-06T21:24:13.2125893Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-08-06T21:24:13.2125976Z True 2024-08-06T21:24:13.2126068Z 2024-08-06T21:24:13.2126149Z 2024-08-06T21:24:13.2126403Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2126496Z 2024-08-06T21:24:13.2126591Z warnings.warn(msg) 2024-08-06T21:24:13.2126672Z 2024-08-06T21:24:13.2126873Z --- Parse Warning: 86 / 100 --- 2024-08-06T21:24:13.2127796Z /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. 2024-08-06T21:24:13.2128148Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2128242Z 2024-08-06T21:24:13.2128447Z Raises an AssertionError if two array_like objects are not equal. 2024-08-06T21:24:13.2128528Z 2024-08-06T21:24:13.2128741Z Given two array_like objects, check that the shape is equal and all 2024-08-06T21:24:13.2128966Z elements of these objects are equal (but see the Notes for the special 2024-08-06T21:24:13.2129180Z handling of a scalar). An exception is raised at shape mismatch or 2024-08-06T21:24:13.2129400Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-08-06T21:24:13.2129620Z are compared like numbers, no assertion is raised if both objects have 2024-08-06T21:24:13.2129740Z NaNs in the same positions. 2024-08-06T21:24:13.2129820Z 2024-08-06T21:24:13.2130047Z The usual caution for verifying equality with floating point numbers is 2024-08-06T21:24:13.2130148Z advised. 2024-08-06T21:24:13.2130224Z 2024-08-06T21:24:13.2130313Z Parameters 2024-08-06T21:24:13.2130414Z ---------- 2024-08-06T21:24:13.2130501Z x : array_like 2024-08-06T21:24:13.2130603Z The actual object to check. 2024-08-06T21:24:13.2130699Z y : array_like 2024-08-06T21:24:13.2130805Z The desired, expected object. 2024-08-06T21:24:13.2130900Z err_msg : str, optional 2024-08-06T21:24:13.2131063Z The error message to be printed in case of failure. 2024-08-06T21:24:13.2131161Z verbose : bool, optional 2024-08-06T21:24:13.2131364Z If True, the conflicting values are appended to the error message. 2024-08-06T21:24:13.2131473Z strict : bool, optional 2024-08-06T21:24:13.2131669Z If True, raise an AssertionError when either the shape or the data 2024-08-06T21:24:13.2131846Z type of the array_like objects does not match. The special 2024-08-06T21:24:13.2132057Z handling for scalars mentioned in the Notes section is disabled. 2024-08-06T21:24:13.2132165Z 2024-08-06T21:24:13.2132248Z Raises 2024-08-06T21:24:13.2132346Z ------ 2024-08-06T21:24:13.2132436Z AssertionError 2024-08-06T21:24:13.2132569Z If actual and desired objects are not equal. 2024-08-06T21:24:13.2132660Z 2024-08-06T21:24:13.2132743Z See Also 2024-08-06T21:24:13.2132829Z -------- 2024-08-06T21:24:13.2133077Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-06T21:24:13.2133202Z relative and/or absolute precision. 2024-08-06T21:24:13.2133422Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-06T21:24:13.2133502Z 2024-08-06T21:24:13.2133580Z Notes 2024-08-06T21:24:13.2133673Z ----- 2024-08-06T21:24:13.2133884Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-08-06T21:24:13.2134106Z function checks that each element of the array_like object is equal to 2024-08-06T21:24:13.2134343Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-08-06T21:24:13.2134423Z 2024-08-06T21:24:13.2134509Z Examples 2024-08-06T21:24:13.2134605Z -------- 2024-08-06T21:24:13.2134737Z The first assert does not raise an exception: 2024-08-06T21:24:13.2134813Z 2024-08-06T21:24:13.2134978Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-08-06T21:24:13.2135102Z ... [np.exp(0),2.33333, np.nan]) 2024-08-06T21:24:13.2135179Z 2024-08-06T21:24:13.2135418Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-08-06T21:24:13.2135529Z functions for these cases instead: 2024-08-06T21:24:13.2135608Z 2024-08-06T21:24:13.2135762Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-08-06T21:24:13.2135889Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-08-06T21:24:13.2136000Z ... rtol=1e-10, atol=0) 2024-08-06T21:24:13.2136095Z 2024-08-06T21:24:13.2136300Z As mentioned in the Notes section, `assert_array_equal` has special 2024-08-06T21:24:13.2136577Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-08-06T21:24:13.2136672Z 2024-08-06T21:24:13.2136780Z >>> x = np.full((2, 5), fill_value=3) 2024-08-06T21:24:13.2136905Z >>> np.testing.assert_array_equal(x, 3) 2024-08-06T21:24:13.2136987Z 2024-08-06T21:24:13.2137202Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-08-06T21:24:13.2137297Z array: 2024-08-06T21:24:13.2137377Z 2024-08-06T21:24:13.2137523Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-08-06T21:24:13.2137642Z Traceback (most recent call last): 2024-08-06T21:24:13.2137727Z ... 2024-08-06T21:24:13.2137825Z AssertionError: 2024-08-06T21:24:13.2137933Z Arrays are not equal 2024-08-06T21:24:13.2138020Z 2024-08-06T21:24:13.2138122Z (shapes (2, 5), () mismatch) 2024-08-06T21:24:13.2138232Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-08-06T21:24:13.2138321Z [3, 3, 3, 3, 3]]) 2024-08-06T21:24:13.2138420Z y: torch.ndarray(3) 2024-08-06T21:24:13.2138516Z 2024-08-06T21:24:13.2138728Z The `strict` parameter also ensures that the array data types match: 2024-08-06T21:24:13.2138808Z 2024-08-06T21:24:13.2138913Z >>> x = np.array([2, 2, 2]) 2024-08-06T21:24:13.2139040Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-08-06T21:24:13.2139188Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-08-06T21:24:13.2139309Z Traceback (most recent call last): 2024-08-06T21:24:13.2139391Z ... 2024-08-06T21:24:13.2139481Z AssertionError: 2024-08-06T21:24:13.2139586Z Arrays are not equal 2024-08-06T21:24:13.2139673Z 2024-08-06T21:24:13.2139817Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-08-06T21:24:13.2139926Z x: torch.ndarray([2, 2, 2]) 2024-08-06T21:24:13.2140055Z y: torch.ndarray([2., 2., 2.]) 2024-08-06T21:24:13.2140136Z 2024-08-06T21:24:13.2140406Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2140489Z 2024-08-06T21:24:13.2140583Z warnings.warn(msg) 2024-08-06T21:24:13.2140676Z 2024-08-06T21:24:13.2140874Z --- Parse Warning: 87 / 100 --- 2024-08-06T21:24:13.2141841Z /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. 2024-08-06T21:24:13.2142120Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2142203Z 2024-08-06T21:24:13.2142574Z Raises an AssertionError if two objects are not equal up to desired 2024-08-06T21:24:13.2142756Z precision. 2024-08-06T21:24:13.2142840Z 2024-08-06T21:24:13.2143027Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-06T21:24:13.2143213Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-06T21:24:13.2143402Z instead of this function for more consistent floating point 2024-08-06T21:24:13.2143509Z comparisons. 2024-08-06T21:24:13.2143592Z 2024-08-06T21:24:13.2143833Z The test verifies identical shapes and that the elements of ``actual`` and 2024-08-06T21:24:13.2143942Z ``desired`` satisfy. 2024-08-06T21:24:13.2144021Z 2024-08-06T21:24:13.2144153Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-08-06T21:24:13.2144247Z 2024-08-06T21:24:13.2144478Z That is a looser test than originally documented, but agrees with what the 2024-08-06T21:24:13.2144721Z actual implementation did up to rounding vagaries. An exception is raised 2024-08-06T21:24:13.2144970Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-08-06T21:24:13.2145193Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-08-06T21:24:13.2145326Z objects have NaNs in the same positions. 2024-08-06T21:24:13.2145482Z 2024-08-06T21:24:13.2145576Z Parameters 2024-08-06T21:24:13.2145675Z ---------- 2024-08-06T21:24:13.2145767Z x : array_like 2024-08-06T21:24:13.2145873Z The actual object to check. 2024-08-06T21:24:13.2145967Z y : array_like 2024-08-06T21:24:13.2146078Z The desired, expected object. 2024-08-06T21:24:13.2146181Z decimal : int, optional 2024-08-06T21:24:13.2146305Z Desired precision, default is 6. 2024-08-06T21:24:13.2146400Z err_msg : str, optional 2024-08-06T21:24:13.2146557Z The error message to be printed in case of failure. 2024-08-06T21:24:13.2146736Z verbose : bool, optional 2024-08-06T21:24:13.2146947Z If True, the conflicting values are appended to the error message. 2024-08-06T21:24:13.2147030Z 2024-08-06T21:24:13.2147126Z Raises 2024-08-06T21:24:13.2147213Z ------ 2024-08-06T21:24:13.2147305Z AssertionError 2024-08-06T21:24:13.2147513Z If actual and desired are not equal up to specified precision. 2024-08-06T21:24:13.2147594Z 2024-08-06T21:24:13.2147678Z See Also 2024-08-06T21:24:13.2147775Z -------- 2024-08-06T21:24:13.2148011Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-06T21:24:13.2148136Z relative and/or absolute precision. 2024-08-06T21:24:13.2148352Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-06T21:24:13.2148433Z 2024-08-06T21:24:13.2148517Z Examples 2024-08-06T21:24:13.2148608Z -------- 2024-08-06T21:24:13.2148737Z the first assert does not raise an exception 2024-08-06T21:24:13.2148817Z 2024-08-06T21:24:13.2149001Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-08-06T21:24:13.2149119Z ... [1.0,2.333,np.nan]) 2024-08-06T21:24:13.2149249Z 2024-08-06T21:24:13.2149432Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-06T21:24:13.2149561Z ... [1.0,2.33339,np.nan], decimal=5) 2024-08-06T21:24:13.2152570Z Traceback (most recent call last): 2024-08-06T21:24:13.2152700Z ... 2024-08-06T21:24:13.2152796Z AssertionError: 2024-08-06T21:24:13.2152915Z Arrays are not almost equal to 5 decimals 2024-08-06T21:24:13.2153020Z 2024-08-06T21:24:13.2153124Z Mismatched elements: 1 / 3 (33.3%) 2024-08-06T21:24:13.2153252Z Max absolute difference: 5.999999999994898e-05 2024-08-06T21:24:13.2153394Z Max relative difference: 2.5713661239633743e-05 2024-08-06T21:24:13.2153558Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-06T21:24:13.2153718Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-08-06T21:24:13.2154355Z 2024-08-06T21:24:13.2154536Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-06T21:24:13.2154658Z ... [1.0,2.33333, 5], decimal=5) 2024-08-06T21:24:13.2154786Z Traceback (most recent call last): 2024-08-06T21:24:13.2154869Z ... 2024-08-06T21:24:13.2154960Z AssertionError: 2024-08-06T21:24:13.2155134Z Arrays are not almost equal to 5 decimals 2024-08-06T21:24:13.2155220Z 2024-08-06T21:24:13.2155326Z x and y nan location mismatch: 2024-08-06T21:24:13.2155496Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-06T21:24:13.2155651Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-08-06T21:24:13.2155731Z 2024-08-06T21:24:13.2155823Z 2024-08-06T21:24:13.2156079Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2156159Z 2024-08-06T21:24:13.2156273Z warnings.warn(msg) 2024-08-06T21:24:13.2156353Z 2024-08-06T21:24:13.2156597Z --- Parse Warning: 88 / 100 --- 2024-08-06T21:24:13.2157618Z /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. 2024-08-06T21:24:13.2157887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2158112Z Context manager that resets warning registry for catching warnings 2024-08-06T21:24:13.2158195Z 2024-08-06T21:24:13.2158438Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-08-06T21:24:13.2158678Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-08-06T21:24:13.2158917Z it impossible to retrigger the warning in this module, whatever you put in 2024-08-06T21:24:13.2159162Z the warnings filters. This context manager accepts a sequence of `modules` 2024-08-06T21:24:13.2159312Z as a keyword argument to its constructor and: 2024-08-06T21:24:13.2159399Z 2024-08-06T21:24:13.2159632Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-08-06T21:24:13.2159736Z on entry; 2024-08-06T21:24:13.2159927Z * resets ``__warningregistry__`` to its previous state on exit. 2024-08-06T21:24:13.2160021Z 2024-08-06T21:24:13.2160248Z This makes it possible to trigger any warning afresh inside the context 2024-08-06T21:24:13.2160432Z manager without disturbing the state of warnings outside. 2024-08-06T21:24:13.2160522Z 2024-08-06T21:24:13.2160757Z For compatibility with Python 3.0, please consider all arguments to be 2024-08-06T21:24:13.2160851Z keyword-only. 2024-08-06T21:24:13.2160942Z 2024-08-06T21:24:13.2161031Z Parameters 2024-08-06T21:24:13.2161123Z ---------- 2024-08-06T21:24:13.2161233Z record : bool, optional 2024-08-06T21:24:13.2161419Z Specifies whether warnings should be captured by a custom 2024-08-06T21:24:13.2161691Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-08-06T21:24:13.2161913Z returned by the context manager. Otherwise None is returned by the 2024-08-06T21:24:13.2162211Z context manager. The objects appended to the list are arguments whose 2024-08-06T21:24:13.2162383Z attributes mirror the arguments to ``showwarning()``. 2024-08-06T21:24:13.2162495Z modules : sequence, optional 2024-08-06T21:24:13.2162711Z Sequence of modules for which to reset warnings registry on entry and 2024-08-06T21:24:13.2162900Z restore on exit. To work correctly, all 'ignore' filters should 2024-08-06T21:24:13.2163020Z filter by one of these modules. 2024-08-06T21:24:13.2163103Z 2024-08-06T21:24:13.2163218Z Examples 2024-08-06T21:24:13.2163318Z -------- 2024-08-06T21:24:13.2163416Z >>> import warnings 2024-08-06T21:24:13.2163612Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-08-06T21:24:13.2163739Z ... modules=[np.core.fromnumeric]): 2024-08-06T21:24:13.2163864Z ... warnings.simplefilter('always') 2024-08-06T21:24:13.2164104Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-08-06T21:24:13.2164274Z ... # do something that raises a warning but ignore those in 2024-08-06T21:24:13.2164379Z ... # np.core.fromnumeric 2024-08-06T21:24:13.2164473Z 2024-08-06T21:24:13.2164726Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2164807Z 2024-08-06T21:24:13.2164916Z warnings.warn(msg) 2024-08-06T21:24:13.2164996Z 2024-08-06T21:24:13.2165191Z --- Parse Warning: 89 / 100 --- 2024-08-06T21:24:13.2166125Z /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=355. 2024-08-06T21:24:13.2166419Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2166634Z Applies a 1D convolution over a quantized input signal composed of 2024-08-06T21:24:13.2166759Z several quantized input planes. 2024-08-06T21:24:13.2166840Z 2024-08-06T21:24:13.2167064Z For details on input arguments, parameters, and implementation see 2024-08-06T21:24:13.2167167Z :class:`~torch.nn.Conv1d`. 2024-08-06T21:24:13.2167246Z 2024-08-06T21:24:13.2167352Z .. note:: 2024-08-06T21:24:13.2167549Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-08-06T21:24:13.2167630Z 2024-08-06T21:24:13.2167726Z .. note:: 2024-08-06T21:24:13.2167905Z Only `torch.quint8` is supported for the input data type. 2024-08-06T21:24:13.2167987Z 2024-08-06T21:24:13.2168078Z 2024-08-06T21:24:13.2168168Z Attributes: 2024-08-06T21:24:13.2168379Z weight (Tensor): packed tensor derived from the learnable weight 2024-08-06T21:24:13.2168494Z parameter. 2024-08-06T21:24:13.2168638Z scale (Tensor): scalar for the output scale 2024-08-06T21:24:13.2168803Z zero_point (Tensor): scalar for the output zero point 2024-08-06T21:24:13.2168896Z 2024-08-06T21:24:13.2169047Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-08-06T21:24:13.2169127Z 2024-08-06T21:24:13.2169228Z Examples:: 2024-08-06T21:24:13.2169311Z 2024-08-06T21:24:13.2169459Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-08-06T21:24:13.2169603Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-08-06T21:24:13.2169714Z >>> input = torch.randn(20, 16, 100) 2024-08-06T21:24:13.2169824Z >>> # quantize input to quint8 2024-08-06T21:24:13.2169936Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.2170177Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-08-06T21:24:13.2170319Z ... dtype=torch.quint8) 2024-08-06T21:24:13.2170419Z >>> output = m(q_input) 2024-08-06T21:24:13.2170542Z 2024-08-06T21:24:13.2170638Z 2024-08-06T21:24:13.2170893Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2170973Z 2024-08-06T21:24:13.2171084Z warnings.warn(msg) 2024-08-06T21:24:13.2171164Z 2024-08-06T21:24:13.2171355Z --- Parse Warning: 90 / 100 --- 2024-08-06T21:24:13.2172272Z /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. 2024-08-06T21:24:13.2172565Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2172694Z A quantized long short-term memory (LSTM). 2024-08-06T21:24:13.2172788Z 2024-08-06T21:24:13.2173070Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-08-06T21:24:13.2173151Z 2024-08-06T21:24:13.2173254Z Attributes: 2024-08-06T21:24:13.2173376Z layers : instances of the `_LSTMLayer` 2024-08-06T21:24:13.2173457Z 2024-08-06T21:24:13.2173570Z .. note:: 2024-08-06T21:24:13.2173784Z To access the weights and biases, you need to access them per layer. 2024-08-06T21:24:13.2173959Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-08-06T21:24:13.2174054Z 2024-08-06T21:24:13.2174149Z Examples:: 2024-08-06T21:24:13.2174264Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.2174374Z >>> custom_module_config = { 2024-08-06T21:24:13.2174513Z ... 'float_to_observed_custom_module_class': { 2024-08-06T21:24:13.2174656Z ... nn.LSTM: nn.quantizable.LSTM, 2024-08-06T21:24:13.2174742Z ... }, 2024-08-06T21:24:13.2174922Z ... 'observed_to_quantized_custom_module_class': { 2024-08-06T21:24:13.2175084Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-08-06T21:24:13.2175172Z ... } 2024-08-06T21:24:13.2175258Z ... } 2024-08-06T21:24:13.2175496Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-08-06T21:24:13.2175710Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-08-06T21:24:13.2175797Z 2024-08-06T21:24:13.2176066Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2176151Z 2024-08-06T21:24:13.2176250Z warnings.warn(msg) 2024-08-06T21:24:13.2176352Z 2024-08-06T21:24:13.2176543Z --- Parse Warning: 91 / 100 --- 2024-08-06T21:24:13.2177621Z /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=229. 2024-08-06T21:24:13.2177904Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2178079Z Squashes the sparse masks into the appropriate tensors. 2024-08-06T21:24:13.2178176Z 2024-08-06T21:24:13.2178385Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-08-06T21:24:13.2178572Z the module will have a `sparse_params` dict attached to it. 2024-08-06T21:24:13.2178670Z 2024-08-06T21:24:13.2178756Z Args: 2024-08-06T21:24:13.2178945Z params_to_keep: List of keys to save in the module or a dict 2024-08-06T21:24:13.2179122Z representing the modules and keys that will have 2024-08-06T21:24:13.2179249Z sparsity parameters saved 2024-08-06T21:24:13.2179511Z params_to_keep_per_layer: Dict to specify the params that should be 2024-08-06T21:24:13.2179683Z saved for specific layers. The keys in the dict 2024-08-06T21:24:13.2179870Z should be the module fqn, while the values should 2024-08-06T21:24:13.2180034Z be a list of strings with the names of the variables 2024-08-06T21:24:13.2180176Z to save in the `sparse_params` 2024-08-06T21:24:13.2180259Z 2024-08-06T21:24:13.2180359Z Examples: 2024-08-06T21:24:13.2180491Z >>> # xdoctest: +SKIP("locals are undefined") 2024-08-06T21:24:13.2180606Z >>> # Don't save any sparse params 2024-08-06T21:24:13.2180764Z >>> sparsifier.squash_mask() 2024-08-06T21:24:13.2180904Z >>> hasattr(model.submodule1, 'sparse_params') 2024-08-06T21:24:13.2180994Z False 2024-08-06T21:24:13.2181087Z 2024-08-06T21:24:13.2181202Z >>> # Keep sparse params per layer 2024-08-06T21:24:13.2181315Z >>> sparsifier.squash_mask( 2024-08-06T21:24:13.2181440Z ... params_to_keep_per_layer={ 2024-08-06T21:24:13.2181571Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-08-06T21:24:13.2181696Z ... 'submodule2.linear42': ('baz',) 2024-08-06T21:24:13.2181794Z ... }) 2024-08-06T21:24:13.2181949Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-06T21:24:13.2182048Z {'foo': 42, 'bar': 24} 2024-08-06T21:24:13.2182219Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-06T21:24:13.2182311Z {'baz': 0.1} 2024-08-06T21:24:13.2182396Z 2024-08-06T21:24:13.2182532Z >>> # Keep sparse params for all layers 2024-08-06T21:24:13.2182705Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-08-06T21:24:13.2182875Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-06T21:24:13.2182997Z {'foo': 42, 'bar': 24} 2024-08-06T21:24:13.2183159Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-06T21:24:13.2183265Z {'foo': 42, 'bar': 24} 2024-08-06T21:24:13.2183348Z 2024-08-06T21:24:13.2183543Z >>> # Keep some sparse params for all layers, and specific ones for 2024-08-06T21:24:13.2183657Z >>> # some other layers 2024-08-06T21:24:13.2183767Z >>> sparsifier.squash_mask( 2024-08-06T21:24:13.2183890Z ... params_to_keep=('foo', 'bar'), 2024-08-06T21:24:13.2184020Z ... params_to_keep_per_layer={ 2024-08-06T21:24:13.2184149Z ... 'submodule2.linear42': ('baz',) 2024-08-06T21:24:13.2184240Z ... }) 2024-08-06T21:24:13.2184407Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-06T21:24:13.2184507Z {'foo': 42, 'bar': 24} 2024-08-06T21:24:13.2184664Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-06T21:24:13.2184784Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-08-06T21:24:13.2184870Z 2024-08-06T21:24:13.2185124Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2185216Z 2024-08-06T21:24:13.2185310Z warnings.warn(msg) 2024-08-06T21:24:13.2185391Z 2024-08-06T21:24:13.2185591Z --- Parse Warning: 92 / 100 --- 2024-08-06T21:24:13.2186735Z /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. 2024-08-06T21:24:13.2187021Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2187140Z 2024-08-06T21:24:13.2187393Z Config object that specifies the supported data types passed as arguments to 2024-08-06T21:24:13.2187648Z quantize ops in the reference model spec, for input and output activations, 2024-08-06T21:24:13.2187773Z weights, and biases. 2024-08-06T21:24:13.2187857Z 2024-08-06T21:24:13.2188028Z For example, consider the following reference model: 2024-08-06T21:24:13.2188108Z 2024-08-06T21:24:13.2188266Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-08-06T21:24:13.2188359Z 2024-08-06T21:24:13.2188570Z The pattern in the square brackets refers to the reference pattern of 2024-08-06T21:24:13.2188809Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-08-06T21:24:13.2189050Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-08-06T21:24:13.2189306Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-08-06T21:24:13.2189542Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-08-06T21:24:13.2189654Z the second quantize op (quant2). 2024-08-06T21:24:13.2189736Z 2024-08-06T21:24:13.2189968Z Note that the dtype here does not refer to the interface dtypes of the 2024-08-06T21:24:13.2190179Z op. For example, the "input dtype" here is not the dtype of the input 2024-08-06T21:24:13.2190394Z tensor passed to the quantized linear op. Though it can still be the 2024-08-06T21:24:13.2190604Z same as the interface dtype, this is not always the case, e.g. the 2024-08-06T21:24:13.2190823Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-08-06T21:24:13.2191036Z specified in the DTypeConfig would still be quint8. The semantics of 2024-08-06T21:24:13.2191260Z dtypes here are the same as the semantics of the dtypes specified in 2024-08-06T21:24:13.2191354Z the observers. 2024-08-06T21:24:13.2191438Z 2024-08-06T21:24:13.2191663Z These dtypes are matched against the ones specified in the user's 2024-08-06T21:24:13.2191906Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-08-06T21:24:13.2192129Z specified in the DTypeConfig (if any), then we will quantize the given 2024-08-06T21:24:13.2192365Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-08-06T21:24:13.2192476Z the pattern will not be quantized. 2024-08-06T21:24:13.2192571Z 2024-08-06T21:24:13.2192669Z Example usage:: 2024-08-06T21:24:13.2192750Z 2024-08-06T21:24:13.2192870Z >>> # xdoctest: +SKIP(failing) 2024-08-06T21:24:13.2192979Z >>> dtype_config1 = DTypeConfig( 2024-08-06T21:24:13.2193086Z ... input_dtype=torch.quint8, 2024-08-06T21:24:13.2193208Z ... output_dtype=torch.quint8, 2024-08-06T21:24:13.2193316Z ... weight_dtype=torch.qint8, 2024-08-06T21:24:13.2193420Z ... bias_dtype=torch.float) 2024-08-06T21:24:13.2193517Z 2024-08-06T21:24:13.2193624Z >>> dtype_config2 = DTypeConfig( 2024-08-06T21:24:13.2193752Z ... input_dtype=DTypeWithConstraints( 2024-08-06T21:24:13.2193869Z ... dtype=torch.quint8, 2024-08-06T21:24:13.2193977Z ... quant_min_lower_bound=0, 2024-08-06T21:24:13.2194091Z ... quant_max_upper_bound=255, 2024-08-06T21:24:13.2194187Z ... ), 2024-08-06T21:24:13.2194315Z ... output_dtype=DTypeWithConstraints( 2024-08-06T21:24:13.2194416Z ... dtype=torch.quint8, 2024-08-06T21:24:13.2194535Z ... quant_min_lower_bound=0, 2024-08-06T21:24:13.2194646Z ... quant_max_upper_bound=255, 2024-08-06T21:24:13.2194730Z ... ), 2024-08-06T21:24:13.2194868Z ... weight_dtype=DTypeWithConstraints( 2024-08-06T21:24:13.2194970Z ... dtype=torch.qint8, 2024-08-06T21:24:13.2195084Z ... quant_min_lower_bound=-128, 2024-08-06T21:24:13.2195236Z ... quant_max_upper_bound=127, 2024-08-06T21:24:13.2195320Z ... ), 2024-08-06T21:24:13.2195422Z ... bias_dtype=torch.float) 2024-08-06T21:24:13.2195518Z 2024-08-06T21:24:13.2195625Z >>> dtype_config1.input_dtype 2024-08-06T21:24:13.2195743Z torch.quint8 2024-08-06T21:24:13.2195836Z 2024-08-06T21:24:13.2195942Z >>> dtype_config2.input_dtype 2024-08-06T21:24:13.2196031Z torch.quint8 2024-08-06T21:24:13.2196123Z 2024-08-06T21:24:13.2196257Z >>> dtype_config2.input_dtype_with_constraints 2024-08-06T21:24:13.2196815Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-08-06T21:24:13.2196897Z 2024-08-06T21:24:13.2197150Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2197269Z 2024-08-06T21:24:13.2197367Z warnings.warn(msg) 2024-08-06T21:24:13.2197451Z 2024-08-06T21:24:13.2197675Z --- Parse Warning: 93 / 100 --- 2024-08-06T21:24:13.2198938Z /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. 2024-08-06T21:24:13.2199201Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2199296Z 2024-08-06T21:24:13.2199566Z Takes in optional filter values and generates two tables with desired information. 2024-08-06T21:24:13.2199648Z 2024-08-06T21:24:13.2199871Z The generated tables are presented in both a list-of-lists format 2024-08-06T21:24:13.2199953Z 2024-08-06T21:24:13.2200177Z The reason for the two tables are that they handle different things: 2024-08-06T21:24:13.2200339Z 1.) the first table handles all tensor level information 2024-08-06T21:24:13.2200560Z 2.) the second table handles and displays all channel based information 2024-08-06T21:24:13.2200656Z 2024-08-06T21:24:13.2201009Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-06T21:24:13.2201343Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-06T21:24:13.2201716Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-06T21:24:13.2201799Z 2024-08-06T21:24:13.2201898Z Tensor table columns: 2024-08-06T21:24:13.2202106Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-06T21:24:13.2202265Z ---- --------- --------- --------- --------- --------- 2024-08-06T21:24:13.2202348Z 2024-08-06T21:24:13.2202470Z Per-Channel table columns: 2024-08-06T21:24:13.2202690Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-06T21:24:13.2202861Z ---- --------- ------- --------- --------- --------- --------- 2024-08-06T21:24:13.2202958Z 2024-08-06T21:24:13.2203043Z Args: 2024-08-06T21:24:13.2203323Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-06T21:24:13.2203435Z contain this filter substring 2024-08-06T21:24:13.2203599Z Default = "", results in all the features being printed 2024-08-06T21:24:13.2203872Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-06T21:24:13.2204120Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-06T21:24:13.2204201Z 2024-08-06T21:24:13.2204325Z Returns a dictionary with two keys: 2024-08-06T21:24:13.2204501Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-08-06T21:24:13.2204623Z "tensor_level_info", "channel_level_info" 2024-08-06T21:24:13.2204777Z Each key maps to a tuple with: 2024-08-06T21:24:13.2204897Z A list of the headers of each table 2024-08-06T21:24:13.2205082Z A list of lists containing the table information row by row 2024-08-06T21:24:13.2205294Z The 0th index row will contain the headers of the columns 2024-08-06T21:24:13.2205419Z The rest of the rows will contain data 2024-08-06T21:24:13.2205501Z 2024-08-06T21:24:13.2205607Z Example Use: 2024-08-06T21:24:13.2205735Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.2205885Z >>> mod_report_visualizer.generate_filtered_tables( 2024-08-06T21:24:13.2206022Z ... feature_filter = "per_channel_min", 2024-08-06T21:24:13.2206133Z ... module_fqn_filter = "block1" 2024-08-06T21:24:13.2206450Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-08-06T21:24:13.2206536Z 2024-08-06T21:24:13.2206792Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2206891Z 2024-08-06T21:24:13.2206993Z warnings.warn(msg) 2024-08-06T21:24:13.2207077Z 2024-08-06T21:24:13.2207288Z --- Parse Warning: 94 / 100 --- 2024-08-06T21:24:13.2208580Z /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. 2024-08-06T21:24:13.2208844Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2208943Z 2024-08-06T21:24:13.2209218Z Takes in optional filter values and prints out formatted tables of the information. 2024-08-06T21:24:13.2209304Z 2024-08-06T21:24:13.2209662Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-08-06T21:24:13.2209829Z 1.) the first table handles all tensor level information 2024-08-06T21:24:13.2210104Z 2.) the second table handles and displays all channel based information 2024-08-06T21:24:13.2210190Z 2024-08-06T21:24:13.2210509Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-06T21:24:13.2210855Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-06T21:24:13.2211212Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-06T21:24:13.2211296Z 2024-08-06T21:24:13.2211413Z Tensor table columns: 2024-08-06T21:24:13.2211605Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-06T21:24:13.2211761Z ---- --------- --------- --------- --------- --------- 2024-08-06T21:24:13.2211862Z 2024-08-06T21:24:13.2211970Z Per-Channel table columns: 2024-08-06T21:24:13.2212053Z 2024-08-06T21:24:13.2212292Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-06T21:24:13.2212458Z ---- --------- ------- --------- --------- --------- --------- 2024-08-06T21:24:13.2212542Z 2024-08-06T21:24:13.2212639Z Args: 2024-08-06T21:24:13.2212905Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-06T21:24:13.2213033Z contain this filter substring 2024-08-06T21:24:13.2213193Z Default = "", results in all the features being printed 2024-08-06T21:24:13.2213455Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-06T21:24:13.2213715Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-06T21:24:13.2213802Z 2024-08-06T21:24:13.2213892Z Example Use: 2024-08-06T21:24:13.2214068Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.2214233Z >>> mod_report_visualizer.generate_table_visualization( 2024-08-06T21:24:13.2214357Z ... feature_filter = "per_channel_min", 2024-08-06T21:24:13.2214502Z ... module_fqn_filter = "block1" 2024-08-06T21:24:13.2214587Z ... ) 2024-08-06T21:24:13.2214778Z >>> # prints out neatly formatted table with per_channel_min info 2024-08-06T21:24:13.2214914Z >>> # for all modules in block 1 of the model 2024-08-06T21:24:13.2214996Z 2024-08-06T21:24:13.2215250Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2215344Z 2024-08-06T21:24:13.2215442Z warnings.warn(msg) 2024-08-06T21:24:13.2215524Z 2024-08-06T21:24:13.2215724Z --- Parse Warning: 95 / 100 --- 2024-08-06T21:24:13.2217025Z /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=565. 2024-08-06T21:24:13.2217304Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2217386Z 2024-08-06T21:24:13.2217623Z Takes in a feature and optional module_filter and plots of the desired data. 2024-08-06T21:24:13.2217720Z 2024-08-06T21:24:13.2217995Z For per channel features, it averages the value across the channels and plots a point 2024-08-06T21:24:13.2218252Z per module. The reason for this is that for models with hundreds of channels, it can 2024-08-06T21:24:13.2218542Z be hard to differentiate one channel line from another, and so the point of generating 2024-08-06T21:24:13.2218816Z a single average point per module is to give a sense of general trends that encourage 2024-08-06T21:24:13.2218916Z further deep dives. 2024-08-06T21:24:13.2219015Z 2024-08-06T21:24:13.2219099Z Note: 2024-08-06T21:24:13.2219393Z Only features in the report that have tensor value data are plottable by this class 2024-08-06T21:24:13.2219573Z When the tensor information is plotted, it will plot: 2024-08-06T21:24:13.2219711Z idx as the x val, feature value as the y_val 2024-08-06T21:24:13.2219890Z When the channel information is plotted, it will plot: 2024-08-06T21:24:13.2220158Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-08-06T21:24:13.2220384Z The reason for this is that we want to be able to compare values across the 2024-08-06T21:24:13.2220632Z channels for same layer, and it will be hard if values are staggered by idx 2024-08-06T21:24:13.2220802Z This means each module is represented by only 1 x value 2024-08-06T21:24:13.2220889Z Args: 2024-08-06T21:24:13.2221125Z feature_filter (str): Filters the features presented to only those that 2024-08-06T21:24:13.2221239Z contain this filter substring 2024-08-06T21:24:13.2221501Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-06T21:24:13.2221765Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-06T21:24:13.2221849Z 2024-08-06T21:24:13.2221939Z Example Use: 2024-08-06T21:24:13.2222080Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-06T21:24:13.2222242Z >>> mod_report_visualizer.generate_plot_visualization( 2024-08-06T21:24:13.2222376Z ... feature_filter = "per_channel_min", 2024-08-06T21:24:13.2222486Z ... module_fqn_filter = "block1" 2024-08-06T21:24:13.2222571Z ... ) 2024-08-06T21:24:13.2222766Z >>> # outputs line plot of per_channel_min information for all 2024-08-06T21:24:13.2222952Z >>> # modules in block1 of model each channel gets it's own line, 2024-08-06T21:24:13.2223158Z >>> # and it's plotted across the in-order modules on the x-axis 2024-08-06T21:24:13.2223255Z 2024-08-06T21:24:13.2223510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2223592Z 2024-08-06T21:24:13.2223732Z warnings.warn(msg) 2024-08-06T21:24:13.2223817Z 2024-08-06T21:24:13.2224005Z --- Parse Warning: 96 / 100 --- 2024-08-06T21:24:13.2225325Z /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=645. 2024-08-06T21:24:13.2225587Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2225710Z 2024-08-06T21:24:13.2225990Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-08-06T21:24:13.2226075Z 2024-08-06T21:24:13.2226173Z Note: 2024-08-06T21:24:13.2226441Z Only features in the report that have tensor value data can be viewed as a histogram 2024-08-06T21:24:13.2226814Z If you want to plot a histogram from all the channel values of a specific feature for 2024-08-06T21:24:13.2227083Z a specific model, make sure to specify both the model and the feature properly 2024-08-06T21:24:13.2227328Z in the filters and you should be able to see a distribution of the channel data 2024-08-06T21:24:13.2227414Z 2024-08-06T21:24:13.2227512Z Args: 2024-08-06T21:24:13.2227780Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-06T21:24:13.2227892Z contain this filter substring 2024-08-06T21:24:13.2228069Z Default = "", results in all the features being printed 2024-08-06T21:24:13.2228331Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-06T21:24:13.2228594Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-06T21:24:13.2228852Z num_bins (int, optional): The number of bins to create the histogram with 2024-08-06T21:24:13.2229040Z Default = 10, the values will be split into 10 equal sized bins 2024-08-06T21:24:13.2229134Z 2024-08-06T21:24:13.2229224Z Example Use: 2024-08-06T21:24:13.2229323Z >>> # xdoctest: +SKIP 2024-08-06T21:24:13.2229634Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-08-06T21:24:13.2229757Z ... feature_filter = "per_channel_min", 2024-08-06T21:24:13.2229867Z ... module_fqn_filter = "block1" 2024-08-06T21:24:13.2229962Z ... ) 2024-08-06T21:24:13.2230238Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-08-06T21:24:13.2230500Z information is gathered across all channels for all modules in block 1 for the 2024-08-06T21:24:13.2230732Z per_channel_min and is displayed in a histogram of equally sized bins 2024-08-06T21:24:13.2230817Z 2024-08-06T21:24:13.2231074Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2231167Z 2024-08-06T21:24:13.2231268Z warnings.warn(msg) 2024-08-06T21:24:13.2231348Z 2024-08-06T21:24:13.2231559Z --- Parse Warning: 97 / 100 --- 2024-08-06T21:24:13.2232565Z /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. 2024-08-06T21:24:13.2232837Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2232920Z 2024-08-06T21:24:13.2233146Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-08-06T21:24:13.2233438Z distribution where all component are from different parameterizations of 2024-08-06T21:24:13.2233653Z the same distribution type. It is parameterized by a `Categorical` 2024-08-06T21:24:13.2233882Z "selecting distribution" (over `k` component) and a component 2024-08-06T21:24:13.2234098Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-08-06T21:24:13.2234257Z (equal to `[k]`) which indexes each (batch of) component. 2024-08-06T21:24:13.2234339Z 2024-08-06T21:24:13.2234447Z Examples:: 2024-08-06T21:24:13.2234527Z 2024-08-06T21:24:13.2234648Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-06T21:24:13.2234862Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-08-06T21:24:13.2234976Z >>> # weighted normal distributions 2024-08-06T21:24:13.2235138Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-06T21:24:13.2235282Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-08-06T21:24:13.2235407Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-06T21:24:13.2235503Z 2024-08-06T21:24:13.2235704Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-08-06T21:24:13.2235839Z >>> # weighted bivariate normal distributions 2024-08-06T21:24:13.2235969Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-06T21:24:13.2236082Z >>> comp = D.Independent(D.Normal( 2024-08-06T21:24:13.2236210Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-08-06T21:24:13.2236342Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-06T21:24:13.2236427Z 2024-08-06T21:24:13.2236608Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-08-06T21:24:13.2236824Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-08-06T21:24:13.2236946Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-08-06T21:24:13.2237062Z >>> comp = D.Independent(D.Normal( 2024-08-06T21:24:13.2237212Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-08-06T21:24:13.2237332Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-06T21:24:13.2237444Z 2024-08-06T21:24:13.2237543Z Args: 2024-08-06T21:24:13.2237750Z mixture_distribution: `torch.distributions.Categorical`-like 2024-08-06T21:24:13.2237936Z instance. Manages the probability of selecting component. 2024-08-06T21:24:13.2238116Z The number of categories must match the rightmost batch 2024-08-06T21:24:13.2238300Z dimension of the `component_distribution`. Must have either 2024-08-06T21:24:13.2238451Z scalar `batch_shape` or `batch_shape` matching 2024-08-06T21:24:13.2238587Z `component_distribution.batch_shape[:-1]` 2024-08-06T21:24:13.2238806Z component_distribution: `torch.distributions.Distribution`-like 2024-08-06T21:24:13.2238999Z instance. Right-most batch dimension indexes component. 2024-08-06T21:24:13.2239081Z 2024-08-06T21:24:13.2239336Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2239431Z 2024-08-06T21:24:13.2239530Z warnings.warn(msg) 2024-08-06T21:24:13.2239611Z 2024-08-06T21:24:13.2239816Z --- Parse Warning: 98 / 100 --- 2024-08-06T21:24:13.2240814Z /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=110. 2024-08-06T21:24:13.2241075Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2241169Z 2024-08-06T21:24:13.2241351Z Creates a RelaxedBernoulli distribution, parametrized by 2024-08-06T21:24:13.2241570Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-08-06T21:24:13.2241788Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-08-06T21:24:13.2241999Z so the values are in (0, 1), and has reparametrizable samples. 2024-08-06T21:24:13.2242093Z 2024-08-06T21:24:13.2242186Z Example:: 2024-08-06T21:24:13.2242269Z 2024-08-06T21:24:13.2242694Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-06T21:24:13.2242832Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-08-06T21:24:13.2242959Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-08-06T21:24:13.2243066Z >>> m.sample() 2024-08-06T21:24:13.2243184Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-08-06T21:24:13.2243269Z 2024-08-06T21:24:13.2243369Z Args: 2024-08-06T21:24:13.2243512Z temperature (Tensor): relaxation temperature 2024-08-06T21:24:13.2243684Z probs (Number, Tensor): the probability of sampling `1` 2024-08-06T21:24:13.2243925Z logits (Number, Tensor): the log-odds of sampling `1` 2024-08-06T21:24:13.2244011Z 2024-08-06T21:24:13.2244268Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2244372Z 2024-08-06T21:24:13.2244474Z warnings.warn(msg) 2024-08-06T21:24:13.2244561Z 2024-08-06T21:24:13.2244774Z --- Parse Warning: 99 / 100 --- 2024-08-06T21:24:13.2245826Z /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=98. 2024-08-06T21:24:13.2246105Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2246189Z 2024-08-06T21:24:13.2246407Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-08-06T21:24:13.2246618Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-08-06T21:24:13.2246862Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-08-06T21:24:13.2247029Z its samples are on simplex, and are reparametrizable. 2024-08-06T21:24:13.2247127Z 2024-08-06T21:24:13.2247217Z Example:: 2024-08-06T21:24:13.2247340Z 2024-08-06T21:24:13.2247494Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-06T21:24:13.2247652Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-08-06T21:24:13.2247780Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-08-06T21:24:13.2247888Z >>> m.sample() 2024-08-06T21:24:13.2248006Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-08-06T21:24:13.2248091Z 2024-08-06T21:24:13.2248192Z Args: 2024-08-06T21:24:13.2248334Z temperature (Tensor): relaxation temperature 2024-08-06T21:24:13.2248454Z probs (Tensor): event probabilities 2024-08-06T21:24:13.2248661Z logits (Tensor): unnormalized log probability for each event 2024-08-06T21:24:13.2248747Z 2024-08-06T21:24:13.2249018Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2249106Z 2024-08-06T21:24:13.2249205Z warnings.warn(msg) 2024-08-06T21:24:13.2249302Z 2024-08-06T21:24:13.2249491Z --- Parse Warning: 100 / 100 --- 2024-08-06T21:24:13.2250399Z /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. 2024-08-06T21:24:13.2250677Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-06T21:24:13.2250824Z Asserts that ``actual`` and ``expected`` are close. 2024-08-06T21:24:13.2250905Z 2024-08-06T21:24:13.2251284Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-08-06T21:24:13.2251369Z 2024-08-06T21:24:13.2251457Z .. math:: 2024-08-06T21:24:13.2251552Z 2024-08-06T21:24:13.2251958Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-08-06T21:24:13.2252039Z 2024-08-06T21:24:13.2252428Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-08-06T21:24:13.2252637Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-08-06T21:24:13.2252728Z 2024-08-06T21:24:13.2252927Z In addition, they are only considered close if they have the same 2024-08-06T21:24:13.2253007Z 2024-08-06T21:24:13.2253212Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-08-06T21:24:13.2253344Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-08-06T21:24:13.2253485Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-08-06T21:24:13.2253661Z - stride (if ``check_stride`` is ``True``). 2024-08-06T21:24:13.2253751Z 2024-08-06T21:24:13.2254051Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-08-06T21:24:13.2254154Z 2024-08-06T21:24:13.2254517Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-08-06T21:24:13.2254896Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-08-06T21:24:13.2255140Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-08-06T21:24:13.2255530Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-08-06T21:24:13.2255629Z 2024-08-06T21:24:13.2255911Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-08-06T21:24:13.2256268Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-08-06T21:24:13.2256383Z definition above. 2024-08-06T21:24:13.2256466Z 2024-08-06T21:24:13.2256798Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-08-06T21:24:13.2257191Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-08-06T21:24:13.2257555Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-08-06T21:24:13.2257933Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-08-06T21:24:13.2258175Z their elements are considered close according to the above definition. 2024-08-06T21:24:13.2258258Z 2024-08-06T21:24:13.2258358Z .. note:: 2024-08-06T21:24:13.2258441Z 2024-08-06T21:24:13.2258774Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-08-06T21:24:13.2259112Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-08-06T21:24:13.2259394Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-08-06T21:24:13.2259478Z 2024-08-06T21:24:13.2259575Z Args: 2024-08-06T21:24:13.2259680Z actual (Any): Actual input. 2024-08-06T21:24:13.2259795Z expected (Any): Expected input. 2024-08-06T21:24:13.2260170Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-08-06T21:24:13.2260329Z are allowed. Otherwise type equality is required. 2024-08-06T21:24:13.2260696Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-08-06T21:24:13.2260976Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-06T21:24:13.2261365Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-08-06T21:24:13.2261639Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-06T21:24:13.2261919Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-08-06T21:24:13.2262207Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-08-06T21:24:13.2262471Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-08-06T21:24:13.2262705Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-08-06T21:24:13.2263057Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-08-06T21:24:13.2263439Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-08-06T21:24:13.2263601Z :func:`torch.promote_types`) before being compared. 2024-08-06T21:24:13.2263975Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-08-06T21:24:13.2264312Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-08-06T21:24:13.2264406Z compared. 2024-08-06T21:24:13.2264780Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-08-06T21:24:13.2265134Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-08-06T21:24:13.2265491Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-08-06T21:24:13.2265623Z should return the new message. 2024-08-06T21:24:13.2265707Z 2024-08-06T21:24:13.2265805Z Raises: 2024-08-06T21:24:13.2266094Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-08-06T21:24:13.2266263Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-08-06T21:24:13.2266694Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-08-06T21:24:13.2267067Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-08-06T21:24:13.2267169Z different types. 2024-08-06T21:24:13.2267549Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-08-06T21:24:13.2267913Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-08-06T21:24:13.2268234Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-08-06T21:24:13.2268551Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-08-06T21:24:13.2268668Z :attr:`~torch.Tensor.layout`. 2024-08-06T21:24:13.2268906Z AssertionError: If only one of corresponding tensors is quantized. 2024-08-06T21:24:13.2269293Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-08-06T21:24:13.2269587Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-08-06T21:24:13.2269717Z :attr:`~torch.Tensor.device`. 2024-08-06T21:24:13.2270051Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-08-06T21:24:13.2270411Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-08-06T21:24:13.2270821Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-08-06T21:24:13.2270907Z 2024-08-06T21:24:13.2271308Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-08-06T21:24:13.2271457Z ``dtype``'s, the maximum of both tolerances is used. 2024-08-06T21:24:13.2271540Z 2024-08-06T21:24:13.2271679Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2271804Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-08-06T21:24:13.2271911Z +===========================+============+==========+ 2024-08-06T21:24:13.2272058Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-08-06T21:24:13.2272209Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2272344Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-08-06T21:24:13.2272479Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2272612Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:13.2272733Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2272880Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-08-06T21:24:13.2273002Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2273148Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-08-06T21:24:13.2273269Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2273403Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:13.2273536Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2273671Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-08-06T21:24:13.2273793Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2273940Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:13.2274061Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2274220Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:13.2274356Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2274490Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:13.2274610Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2274755Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:13.2274876Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2275021Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-08-06T21:24:13.2275142Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2275265Z | other | ``0.0`` | ``0.0`` | 2024-08-06T21:24:13.2275403Z +---------------------------+------------+----------+ 2024-08-06T21:24:13.2275489Z 2024-08-06T21:24:13.2275582Z .. note:: 2024-08-06T21:24:13.2275675Z 2024-08-06T21:24:13.2276064Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-08-06T21:24:13.2276418Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-08-06T21:24:13.2276693Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-08-06T21:24:13.2276775Z 2024-08-06T21:24:13.2276876Z >>> import functools 2024-08-06T21:24:13.2277144Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-08-06T21:24:13.2277252Z >>> assert_equal(1e-9, 1e-10) 2024-08-06T21:24:13.2277377Z Traceback (most recent call last): 2024-08-06T21:24:13.2277472Z ... 2024-08-06T21:24:13.2277627Z AssertionError: Scalars are not equal! 2024-08-06T21:24:13.2277719Z 2024-08-06T21:24:13.2277841Z Expected 1e-10 but got 1e-09. 2024-08-06T21:24:13.2277967Z Absolute difference: 9.000000000000001e-10 2024-08-06T21:24:13.2278126Z Relative difference: 9.0 2024-08-06T21:24:13.2278208Z 2024-08-06T21:24:13.2278295Z Examples: 2024-08-06T21:24:13.2278421Z >>> # tensor to tensor comparison 2024-08-06T21:24:13.2278553Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-08-06T21:24:13.2278685Z >>> actual = torch.acos(torch.cos(expected)) 2024-08-06T21:24:13.2286380Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:13.2286525Z 2024-08-06T21:24:13.2286669Z >>> # scalar to scalar comparison 2024-08-06T21:24:13.2286879Z >>> import math 2024-08-06T21:24:13.2286991Z >>> expected = math.sqrt(2.0) 2024-08-06T21:24:13.2287115Z >>> actual = 2.0 / math.sqrt(2.0) 2024-08-06T21:24:13.2287275Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:13.2287358Z 2024-08-06T21:24:13.2287505Z >>> # numpy array to numpy array comparison 2024-08-06T21:24:13.2287611Z >>> import numpy as np 2024-08-06T21:24:13.2287735Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-08-06T21:24:13.2287875Z >>> actual = np.arccos(np.cos(expected)) 2024-08-06T21:24:13.2288020Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:13.2288104Z 2024-08-06T21:24:13.2288240Z >>> # sequence to sequence comparison 2024-08-06T21:24:13.2288342Z >>> import numpy as np 2024-08-06T21:24:13.2288603Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-08-06T21:24:13.2288749Z >>> # length and their elements have to match. 2024-08-06T21:24:13.2288906Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-08-06T21:24:13.2289020Z >>> actual = tuple(expected) 2024-08-06T21:24:13.2289220Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:13.2289303Z 2024-08-06T21:24:13.2289427Z >>> # mapping to mapping comparison 2024-08-06T21:24:13.2289564Z >>> from collections import OrderedDict 2024-08-06T21:24:13.2289667Z >>> import numpy as np 2024-08-06T21:24:13.2289784Z >>> foo = torch.tensor(1.0) 2024-08-06T21:24:13.2289877Z >>> bar = 2.0 2024-08-06T21:24:13.2289976Z >>> baz = np.array(3.0) 2024-08-06T21:24:13.2290250Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-08-06T21:24:13.2290455Z >>> # have to have the same set of keys and their elements have to match. 2024-08-06T21:24:13.2290667Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-08-06T21:24:13.2290816Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-08-06T21:24:13.2290965Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:13.2291050Z 2024-08-06T21:24:13.2291190Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-06T21:24:13.2291304Z >>> actual = expected.clone() 2024-08-06T21:24:13.2291472Z >>> # By default, directly related instances can be compared 2024-08-06T21:24:13.2291706Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-08-06T21:24:13.2291901Z >>> # This check can be made more strict with allow_subclasses=False 2024-08-06T21:24:13.2292015Z >>> torch.testing.assert_close( 2024-08-06T21:24:13.2292230Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-08-06T21:24:13.2292319Z ... ) 2024-08-06T21:24:13.2292448Z Traceback (most recent call last): 2024-08-06T21:24:13.2292535Z ... 2024-08-06T21:24:13.2292780Z TypeError: No comparison pair was able to handle inputs of type 2024-08-06T21:24:13.2293011Z and . 2024-08-06T21:24:13.2293278Z >>> # If the inputs are not directly related, they are never considered close 2024-08-06T21:24:13.2293456Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-08-06T21:24:13.2293589Z Traceback (most recent call last): 2024-08-06T21:24:13.2293674Z ... 2024-08-06T21:24:13.2293969Z TypeError: No comparison pair was able to handle inputs of type 2024-08-06T21:24:13.2294090Z and . 2024-08-06T21:24:13.2294357Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-08-06T21:24:13.2294506Z >>> # their type if check_dtype=False. 2024-08-06T21:24:13.2294692Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-08-06T21:24:13.2294778Z 2024-08-06T21:24:13.2294883Z >>> # NaN != NaN by default. 2024-08-06T21:24:13.2295025Z >>> expected = torch.tensor(float("Nan")) 2024-08-06T21:24:13.2295135Z >>> actual = expected.clone() 2024-08-06T21:24:13.2295298Z >>> torch.testing.assert_close(actual, expected) 2024-08-06T21:24:13.2295418Z Traceback (most recent call last): 2024-08-06T21:24:13.2295501Z ... 2024-08-06T21:24:13.2295639Z AssertionError: Scalars are not close! 2024-08-06T21:24:13.2295731Z 2024-08-06T21:24:13.2295837Z Expected nan but got nan. 2024-08-06T21:24:13.2295993Z Absolute difference: nan (up to 1e-05 allowed) 2024-08-06T21:24:13.2296142Z Relative difference: nan (up to 1.3e-06 allowed) 2024-08-06T21:24:13.2296345Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-08-06T21:24:13.2296441Z 2024-08-06T21:24:13.2296568Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-06T21:24:13.2296686Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-08-06T21:24:13.2296867Z >>> # The default error message can be overwritten. 2024-08-06T21:24:13.2297163Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-08-06T21:24:13.2297281Z Traceback (most recent call last): 2024-08-06T21:24:13.2297380Z ... 2024-08-06T21:24:13.2297531Z AssertionError: Argh, the tensors are not close! 2024-08-06T21:24:13.2297756Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-08-06T21:24:13.2297866Z >>> # extra information 2024-08-06T21:24:13.2297981Z >>> torch.testing.assert_close( 2024-08-06T21:24:13.2298199Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-08-06T21:24:13.2298285Z ... ) 2024-08-06T21:24:13.2298404Z Traceback (most recent call last): 2024-08-06T21:24:13.2298505Z ... 2024-08-06T21:24:13.2298610Z AssertionError: Header 2024-08-06T21:24:13.2298706Z 2024-08-06T21:24:13.2298826Z Tensor-likes are not close! 2024-08-06T21:24:13.2298917Z 2024-08-06T21:24:13.2299031Z Mismatched elements: 2 / 3 (66.7%) 2024-08-06T21:24:13.2299276Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-08-06T21:24:13.2299508Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-08-06T21:24:13.2299600Z 2024-08-06T21:24:13.2299698Z Footer 2024-08-06T21:24:13.2299781Z 2024-08-06T21:24:13.2300036Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-06T21:24:13.2300135Z 2024-08-06T21:24:13.2300233Z warnings.warn(msg) 2024-08-06T21:24:13.2300316Z 2024-08-06T21:24:13.2300542Z  2024-08-06T21:24:13.2300724Z === Found 9 run-time warnings === 2024-08-06T21:24:13.2300911Z --- Runtime Warning: 1 / 9 --- 2024-08-06T21:24:13.2301194Z example = 2024-08-06T21:24:13.2302587Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py:1250: 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:1931.) 2024-08-06T21:24:13.2302717Z return super().refine_names(names) 2024-08-06T21:24:13.2302801Z 2024-08-06T21:24:13.2302984Z --- Runtime Warning: 2 / 9 --- 2024-08-06T21:24:13.2303336Z example = 2024-08-06T21:24:13.2303966Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:250: UserWarning: Warning only once for all operators, other operators may also be overridden. 2024-08-06T21:24:13.2304287Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-08-06T21:24:13.2304513Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-08-06T21:24:13.2304813Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-08-06T21:24:13.2304924Z dispatch key: CPU 2024-08-06T21:24:13.2305356Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-08-06T21:24:13.2305913Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:162.) 2024-08-06T21:24:13.2306087Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-08-06T21:24:13.2306170Z 2024-08-06T21:24:13.2306351Z --- Runtime Warning: 3 / 9 --- 2024-08-06T21:24:13.2306718Z example = 2024-08-06T21:24:13.2307846Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py:106: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:180.) 2024-08-06T21:24:13.2308110Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-08-06T21:24:13.2308195Z 2024-08-06T21:24:13.2308379Z --- Runtime Warning: 4 / 9 --- 2024-08-06T21:24:13.2308645Z example = 2024-08-06T21:24:13.2310275Z :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.) 2024-08-06T21:24:13.2310375Z 2024-08-06T21:24:13.2310557Z --- Runtime Warning: 5 / 9 --- 2024-08-06T21:24:13.2310850Z example = 2024-08-06T21:24:13.2311957Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: 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) 2024-08-06T21:24:13.2312056Z warnings.warn( 2024-08-06T21:24:13.2312141Z 2024-08-06T21:24:13.2312329Z --- Runtime Warning: 6 / 9 --- 2024-08-06T21:24:13.2312660Z example = 2024-08-06T21:24:13.2313839Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: 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) 2024-08-06T21:24:13.2313936Z warnings.warn( 2024-08-06T21:24:13.2314017Z 2024-08-06T21:24:13.2314211Z --- Runtime Warning: 7 / 9 --- 2024-08-06T21:24:13.2314494Z example = 2024-08-06T21:24:13.2315307Z /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`. 2024-08-06T21:24:13.2315443Z WeightNorm.apply(module, name, dim) 2024-08-06T21:24:13.2315553Z 2024-08-06T21:24:13.2315730Z --- Runtime Warning: 8 / 9 --- 2024-08-06T21:24:13.2316051Z example = 2024-08-06T21:24:13.2316867Z /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`. 2024-08-06T21:24:13.2317001Z WeightNorm.apply(module, name, dim) 2024-08-06T21:24:13.2317083Z 2024-08-06T21:24:13.2317261Z --- Runtime Warning: 9 / 9 --- 2024-08-06T21:24:13.2317581Z example = 2024-08-06T21:24:13.2319101Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/const_fold.py:252: 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 2024-08-06T21:24:13.2319272Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-08-06T21:24:13.2319369Z 2024-08-06T21:24:13.2319702Z === 334 passed, 360 skipped, 109 warnings in 12.04 seconds === 2024-08-06T21:24:13.2319955Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2024-08-06 21:24:13.021780] 2024-08-06T21:24:15.3858283Z running install 2024-08-06T21:24:15.3881451Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-08-06T21:24:15.3882845Z !! 2024-08-06T21:24:15.3883043Z 2024-08-06T21:24:15.3883275Z ******************************************************************************** 2024-08-06T21:24:15.3883970Z Please avoid running ``setup.py`` directly. 2024-08-06T21:24:15.3884735Z Instead, use pypa/build, pypa/installer or other 2024-08-06T21:24:15.3885415Z standards-based tools. 2024-08-06T21:24:15.3885757Z 2024-08-06T21:24:15.3886309Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-08-06T21:24:15.3887299Z ******************************************************************************** 2024-08-06T21:24:15.3887774Z 2024-08-06T21:24:15.3887927Z !! 2024-08-06T21:24:15.3888311Z self.initialize_options() 2024-08-06T21:24:15.4004138Z running build 2024-08-06T21:24:15.4004556Z running build_py 2024-08-06T21:24:15.4075815Z creating build 2024-08-06T21:24:15.4076663Z creating build/lib.linux-x86_64-cpython-312 2024-08-06T21:24:15.4077318Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-08-06T21:24:15.4078536Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-08-06T21:24:15.4088779Z running build_ext 2024-08-06T21:24:15.4105212Z building 'torch_test_cpp_extension.cpp' extension 2024-08-06T21:24:15.4106419Z creating build/temp.linux-x86_64-cpython-312 2024-08-06T21:24:15.4111811Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:16.7711556Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-08-06T21:24:16.7712601Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-08-06T21:24:16.7713479Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2024-08-06T21:24:16.7714025Z from extension.cpp:1: 2024-08-06T21:24:16.7715572Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-08-06T21:24:16.7716474Z extension.cpp:45:53: required from here 2024-08-06T21:24:16.7718331Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1588:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-08-06T21:24:16.7719804Z 1588 | class class_ : public detail::generic_type { 2024-08-06T21:24:16.7720159Z | ^~~~~~ 2024-08-06T21:24:16.7722019Z /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 = {}]’: 2024-08-06T21:24:16.7723400Z extension.cpp:45:53: required from here 2024-08-06T21:24:16.7726652Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1648:28: 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] 2024-08-06T21:24:16.7729336Z 1648 | with_internals([&](internals &internals) { 2024-08-06T21:24:16.7729718Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:16.7730244Z 1649 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-08-06T21:24:16.7730807Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:16.7731254Z 1650 | : internals.registered_types_cpp; 2024-08-06T21:24:16.7731663Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:16.7732071Z 1651 | instances[std::type_index(typeid(type_alias))] 2024-08-06T21:24:16.7732480Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:16.7732874Z 1652 | = instances[std::type_index(typeid(type))]; 2024-08-06T21:24:16.7733338Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:16.7733658Z 1653 | }); 2024-08-06T21:24:16.7733910Z | ~ 2024-08-06T21:24:16.7736330Z g++ -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 2024-08-06T21:24:17.2087112Z building 'torch_test_cpp_extension.maia' extension 2024-08-06T21:24:17.2091802Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:18.8517827Z g++ -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 2024-08-06T21:24:19.2504134Z building 'torch_test_cpp_extension.rng' extension 2024-08-06T21:24:19.2508543Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:20.7383828Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:20.7384740Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:20.7385597Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:20.7386960Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:20.7388007Z from rng_extension.cpp:6: 2024-08-06T21:24:20.7389281Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1106: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-08-06T21:24:20.7390935Z 1106 | # pragma unroll 2024-08-06T21:24:20.7391348Z | 2024-08-06T21:24:20.7392371Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1141, 2024-08-06T21:24:20.7393830Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:20.7394653Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:20.7395608Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:20.7396527Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:20.7397300Z from rng_extension.cpp:6: 2024-08-06T21:24:20.7398090Z /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] 2024-08-06T21:24:20.7398878Z 59 | #pragma unroll 2024-08-06T21:24:20.7399128Z | 2024-08-06T21:24:20.7399800Z /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] 2024-08-06T21:24:20.7400570Z 72 | #pragma unroll 2024-08-06T21:24:20.7400820Z | 2024-08-06T21:24:20.7401349Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1142, 2024-08-06T21:24:20.7402255Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:20.7403065Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:20.7403861Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:20.7404824Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:20.7405502Z from rng_extension.cpp:6: 2024-08-06T21:24:20.7406317Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:131: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-08-06T21:24:20.7407100Z 131 | #pragma unroll 2024-08-06T21:24:20.7407348Z | 2024-08-06T21:24:20.7409662Z g++ -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 2024-08-06T21:24:21.1760939Z running install_lib 2024-08-06T21:24:21.1842767Z creating install 2024-08-06T21:24:21.1843155Z creating install/opt 2024-08-06T21:24:21.1843525Z creating install/opt/conda 2024-08-06T21:24:21.1843834Z creating install/opt/conda/envs 2024-08-06T21:24:21.1844306Z creating install/opt/conda/envs/py_3.12 2024-08-06T21:24:21.1844966Z creating install/opt/conda/envs/py_3.12/lib 2024-08-06T21:24:21.1845788Z creating install/opt/conda/envs/py_3.12/lib/python3.12 2024-08-06T21:24:21.1846397Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-08-06T21:24:21.1847691Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-08-06T21:24:21.1849032Z 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 2024-08-06T21:24:21.1850942Z 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 2024-08-06T21:24:21.1937615Z 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 2024-08-06T21:24:21.2024461Z 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 2024-08-06T21:24:21.2119590Z 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 2024-08-06T21:24:21.2122100Z running install_egg_info 2024-08-06T21:24:21.2291278Z running egg_info 2024-08-06T21:24:21.2291614Z creating torch_test_cpp_extension.egg-info 2024-08-06T21:24:21.2349850Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-08-06T21:24:21.2353375Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-08-06T21:24:21.2355207Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-08-06T21:24:21.2357117Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-08-06T21:24:21.2358437Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:21.2420038Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:21.2425328Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:21.2426972Z 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 2024-08-06T21:24:21.2433271Z running install_scripts 2024-08-06T21:24:23.2796187Z running install 2024-08-06T21:24:23.2797308Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-08-06T21:24:23.2798113Z !! 2024-08-06T21:24:23.2798456Z 2024-08-06T21:24:23.2798593Z ******************************************************************************** 2024-08-06T21:24:23.2798991Z Please avoid running ``setup.py`` directly. 2024-08-06T21:24:23.2799389Z Instead, use pypa/build, pypa/installer or other 2024-08-06T21:24:23.2799766Z standards-based tools. 2024-08-06T21:24:23.2799952Z 2024-08-06T21:24:23.2800267Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-08-06T21:24:23.2800790Z ******************************************************************************** 2024-08-06T21:24:23.2801047Z 2024-08-06T21:24:23.2801136Z !! 2024-08-06T21:24:23.2801358Z self.initialize_options() 2024-08-06T21:24:23.2915645Z running build 2024-08-06T21:24:23.2915888Z running build_ext 2024-08-06T21:24:23.3243594Z building 'no_python_abi_suffix_test' extension 2024-08-06T21:24:23.3244544Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build 2024-08-06T21:24:23.3245584Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312 2024-08-06T21:24:23.3551582Z 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... 2024-08-06T21:24:23.3552622Z Compiling objects... 2024-08-06T21:24:23.3552931Z Using envvar MAX_JOBS (6) as the number of workers... 2024-08-06T21:24:23.4486399Z [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 -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 2024-08-06T21:24:23.4530204Z creating build/lib.linux-x86_64-cpython-312 2024-08-06T21:24:23.4533562Z g++ -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 2024-08-06T21:24:23.5134464Z running install_lib 2024-08-06T21:24:23.5197819Z creating install 2024-08-06T21:24:23.5199027Z creating install/opt 2024-08-06T21:24:23.5199571Z creating install/opt/conda 2024-08-06T21:24:23.5200001Z creating install/opt/conda/envs 2024-08-06T21:24:23.5200697Z creating install/opt/conda/envs/py_3.12 2024-08-06T21:24:23.5201219Z creating install/opt/conda/envs/py_3.12/lib 2024-08-06T21:24:23.5201740Z creating install/opt/conda/envs/py_3.12/lib/python3.12 2024-08-06T21:24:23.5202367Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-08-06T21:24:23.5204051Z 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 2024-08-06T21:24:23.5208920Z running install_egg_info 2024-08-06T21:24:23.5363672Z running egg_info 2024-08-06T21:24:23.5364214Z creating no_python_abi_suffix_test.egg-info 2024-08-06T21:24:23.5421509Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-08-06T21:24:23.5425545Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-08-06T21:24:23.5427549Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-08-06T21:24:23.5429326Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-08-06T21:24:23.5490867Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-08-06T21:24:23.5496391Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-08-06T21:24:23.5498021Z 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 2024-08-06T21:24:23.5504195Z running install_scripts 2024-08-06T21:24:23.9787848Z 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'] ... [2024-08-06 21:24:23.978364] 2024-08-06T21:24:28.2812014Z 2024-08-06T21:24:28.2813219Z 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_101e804a96055140_.log 2024-08-06T21:24:28.2820243Z Running 17 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::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 2024-08-06T21:24:28.2826710Z 2024-08-06T21:24:28.2826925Z Running test_autoload_enable 1/1 ... [2024-08-06 21:24:28.281571] 2024-08-06T21:24:30.6252326Z running install 2024-08-06T21:24:30.6253470Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-08-06T21:24:30.6254280Z !! 2024-08-06T21:24:30.6254397Z 2024-08-06T21:24:30.6254522Z ******************************************************************************** 2024-08-06T21:24:30.6254913Z Please avoid running ``setup.py`` directly. 2024-08-06T21:24:30.6255321Z Instead, use pypa/build, pypa/installer or other 2024-08-06T21:24:30.6255711Z standards-based tools. 2024-08-06T21:24:30.6255908Z 2024-08-06T21:24:30.6256211Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-08-06T21:24:30.6256974Z ******************************************************************************** 2024-08-06T21:24:30.6257220Z 2024-08-06T21:24:30.6257305Z !! 2024-08-06T21:24:30.6369969Z self.initialize_options() 2024-08-06T21:24:30.6370312Z running build 2024-08-06T21:24:30.6370541Z running build_py 2024-08-06T21:24:30.6434157Z creating build 2024-08-06T21:24:30.6435316Z creating build/lib.linux-x86_64-cpython-312 2024-08-06T21:24:30.6436221Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-08-06T21:24:30.6437400Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-08-06T21:24:30.6440576Z running build_ext 2024-08-06T21:24:30.6456459Z building 'torch_test_cpp_extension.cpp' extension 2024-08-06T21:24:30.6457175Z creating build/temp.linux-x86_64-cpython-312 2024-08-06T21:24:30.6462434Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:31.6873210Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-08-06T21:24:31.6874229Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-08-06T21:24:31.6875297Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2024-08-06T21:24:31.6875919Z from extension.cpp:1: 2024-08-06T21:24:31.6877228Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-08-06T21:24:31.6878122Z extension.cpp:45:53: required from here 2024-08-06T21:24:31.6880076Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1588:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-08-06T21:24:31.6881708Z 1588 | class class_ : public detail::generic_type { 2024-08-06T21:24:31.6882053Z | ^~~~~~ 2024-08-06T21:24:31.6883704Z /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 = {}]’: 2024-08-06T21:24:31.6885104Z extension.cpp:45:53: required from here 2024-08-06T21:24:31.6888392Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1648:28: 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] 2024-08-06T21:24:31.6891092Z 1648 | with_internals([&](internals &internals) { 2024-08-06T21:24:31.6891547Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:31.6892057Z 1649 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-08-06T21:24:31.6892633Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:31.6893081Z 1650 | : internals.registered_types_cpp; 2024-08-06T21:24:31.6893479Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:31.6893903Z 1651 | instances[std::type_index(typeid(type_alias))] 2024-08-06T21:24:31.6894315Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:31.6894706Z 1652 | = instances[std::type_index(typeid(type))]; 2024-08-06T21:24:31.6895104Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:31.6895436Z 1653 | }); 2024-08-06T21:24:31.6895675Z | ~ 2024-08-06T21:24:31.6898049Z g++ -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 2024-08-06T21:24:32.1292061Z building 'torch_test_cpp_extension.maia' extension 2024-08-06T21:24:32.1296816Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:33.1680532Z g++ -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 2024-08-06T21:24:33.5710951Z building 'torch_test_cpp_extension.rng' extension 2024-08-06T21:24:33.5715507Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:34.9247677Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:34.9248605Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:34.9249392Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:34.9250306Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:34.9250986Z from rng_extension.cpp:6: 2024-08-06T21:24:34.9251795Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1106: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-08-06T21:24:34.9252605Z 1106 | # pragma unroll 2024-08-06T21:24:34.9252855Z | 2024-08-06T21:24:34.9253472Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1141, 2024-08-06T21:24:34.9254840Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:34.9255654Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:34.9256445Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:34.9257344Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:34.9258025Z from rng_extension.cpp:6: 2024-08-06T21:24:34.9258806Z /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] 2024-08-06T21:24:34.9259755Z 59 | #pragma unroll 2024-08-06T21:24:34.9260008Z | 2024-08-06T21:24:34.9260747Z /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] 2024-08-06T21:24:34.9261508Z 72 | #pragma unroll 2024-08-06T21:24:34.9261750Z | 2024-08-06T21:24:34.9262289Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1142, 2024-08-06T21:24:34.9263194Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:34.9263990Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:34.9264846Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:34.9265758Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:34.9266418Z from rng_extension.cpp:6: 2024-08-06T21:24:34.9267298Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:131: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-08-06T21:24:34.9268094Z 131 | #pragma unroll 2024-08-06T21:24:34.9268331Z | 2024-08-06T21:24:34.9270638Z g++ -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 2024-08-06T21:24:35.3734469Z running install_lib 2024-08-06T21:24:35.3805678Z 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 2024-08-06T21:24:35.3902672Z 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 2024-08-06T21:24:35.3999444Z 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 2024-08-06T21:24:35.4103459Z running install_egg_info 2024-08-06T21:24:35.4255177Z running egg_info 2024-08-06T21:24:35.4311317Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-08-06T21:24:35.4315072Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-08-06T21:24:35.4326385Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-08-06T21:24:35.4336637Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-08-06T21:24:35.4409203Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:35.4415009Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:35.4425712Z 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) 2024-08-06T21:24:35.4427567Z 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 2024-08-06T21:24:35.4433996Z running install_scripts 2024-08-06T21:24:37.9469464Z 2024-08-06T21:24:37.9469984Z Running tests... 2024-08-06T21:24:37.9470349Z ---------------------------------------------------------------------- 2024-08-06T21:24:38.1921966Z . 2024-08-06T21:24:38.1922376Z ---------------------------------------------------------------------- 2024-08-06T21:24:38.1922808Z Ran 1 test in 0.245s 2024-08-06T21:24:38.1922994Z 2024-08-06T21:24:38.1923668Z OK 2024-08-06T21:24:38.1923790Z 2024-08-06T21:24:38.1923902Z Generating XML reports... 2024-08-06T21:24:38.6641290Z Running test_autoload_disable 1/1 ... [2024-08-06 21:24:38.663786] 2024-08-06T21:24:41.0205024Z running install 2024-08-06T21:24:41.0206033Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-08-06T21:24:41.0206850Z !! 2024-08-06T21:24:41.0206966Z 2024-08-06T21:24:41.0207095Z ******************************************************************************** 2024-08-06T21:24:41.0207723Z Please avoid running ``setup.py`` directly. 2024-08-06T21:24:41.0208134Z Instead, use pypa/build, pypa/installer or other 2024-08-06T21:24:41.0208503Z standards-based tools. 2024-08-06T21:24:41.0208704Z 2024-08-06T21:24:41.0209018Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-08-06T21:24:41.0209555Z ******************************************************************************** 2024-08-06T21:24:41.0209795Z 2024-08-06T21:24:41.0209878Z !! 2024-08-06T21:24:41.0210098Z self.initialize_options() 2024-08-06T21:24:41.0324458Z running build 2024-08-06T21:24:41.0324692Z running build_py 2024-08-06T21:24:41.0391299Z creating build 2024-08-06T21:24:41.0391809Z creating build/lib.linux-x86_64-cpython-312 2024-08-06T21:24:41.0392702Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-08-06T21:24:41.0393636Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-08-06T21:24:41.0397704Z running build_ext 2024-08-06T21:24:41.0413000Z building 'torch_test_cpp_extension.cpp' extension 2024-08-06T21:24:41.0413837Z creating build/temp.linux-x86_64-cpython-312 2024-08-06T21:24:41.0420294Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:42.2389884Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-08-06T21:24:42.2391327Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-08-06T21:24:42.2392215Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2024-08-06T21:24:42.2392767Z from extension.cpp:1: 2024-08-06T21:24:42.2394019Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-08-06T21:24:42.2394876Z extension.cpp:45:53: required from here 2024-08-06T21:24:42.2396647Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1588:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-08-06T21:24:42.2398322Z 1588 | class class_ : public detail::generic_type { 2024-08-06T21:24:42.2398825Z | ^~~~~~ 2024-08-06T21:24:42.2400558Z /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 = {}]’: 2024-08-06T21:24:42.2401956Z extension.cpp:45:53: required from here 2024-08-06T21:24:42.2405193Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1648:28: 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] 2024-08-06T21:24:42.2407957Z 1648 | with_internals([&](internals &internals) { 2024-08-06T21:24:42.2408347Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:42.2408868Z 1649 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-08-06T21:24:42.2409447Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:42.2409885Z 1650 | : internals.registered_types_cpp; 2024-08-06T21:24:42.2410301Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:42.2410722Z 1651 | instances[std::type_index(typeid(type_alias))] 2024-08-06T21:24:42.2411120Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:42.2411520Z 1652 | = instances[std::type_index(typeid(type))]; 2024-08-06T21:24:42.2411964Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-08-06T21:24:42.2412283Z 1653 | }); 2024-08-06T21:24:42.2412535Z | ~ 2024-08-06T21:24:42.2414906Z g++ -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 2024-08-06T21:24:42.6733645Z building 'torch_test_cpp_extension.maia' extension 2024-08-06T21:24:42.6737991Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:43.7232964Z g++ -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 2024-08-06T21:24:44.1292858Z building 'torch_test_cpp_extension.rng' extension 2024-08-06T21:24:44.1296908Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -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 2024-08-06T21:24:45.4933001Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:45.4933924Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:45.4934712Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:45.4935648Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:45.4936325Z from rng_extension.cpp:6: 2024-08-06T21:24:45.4937331Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1106: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-08-06T21:24:45.4938125Z 1106 | # pragma unroll 2024-08-06T21:24:45.4938443Z | 2024-08-06T21:24:45.4939161Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1141, 2024-08-06T21:24:45.4940062Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:45.4940872Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:45.4941662Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:45.4942752Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:45.4943442Z from rng_extension.cpp:6: 2024-08-06T21:24:45.4944542Z /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] 2024-08-06T21:24:45.4945327Z 59 | #pragma unroll 2024-08-06T21:24:45.4945567Z | 2024-08-06T21:24:45.4946249Z /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] 2024-08-06T21:24:45.4947060Z 72 | #pragma unroll 2024-08-06T21:24:45.4947289Z | 2024-08-06T21:24:45.4947827Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1142, 2024-08-06T21:24:45.4948734Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-08-06T21:24:45.4949547Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-08-06T21:24:45.4950454Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-08-06T21:24:45.4951425Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-08-06T21:24:45.4952100Z from rng_extension.cpp:6: 2024-08-06T21:24:45.4952896Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:131: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-08-06T21:24:45.4953689Z 131 | #pragma unroll 2024-08-06T21:24:45.4953953Z | 2024-08-06T21:24:45.4956246Z g++ -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 2024-08-06T21:24:45.9318059Z running install_lib 2024-08-06T21:24:45.9389881Z 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 2024-08-06T21:24:45.9486442Z 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 2024-08-06T21:24:45.9582378Z 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 2024-08-06T21:24:45.9685838Z running install_egg_info 2024-08-06T21:24:45.9837200Z running egg_info 2024-08-06T21:24:45.9892920Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-08-06T21:24:45.9896838Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-08-06T21:24:45.9898791Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-08-06T21:24:45.9900681Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-08-06T21:24:45.9962714Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:45.9968420Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:45.9970274Z 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) 2024-08-06T21:24:45.9971783Z 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 2024-08-06T21:24:45.9978348Z running install_scripts 2024-08-06T21:24:48.4828907Z 2024-08-06T21:24:48.4829365Z Running tests... 2024-08-06T21:24:48.4829751Z ---------------------------------------------------------------------- 2024-08-06T21:24:48.7247040Z . 2024-08-06T21:24:48.7247405Z ---------------------------------------------------------------------- 2024-08-06T21:24:48.7247818Z Ran 1 test in 0.242s 2024-08-06T21:24:48.7247976Z 2024-08-06T21:24:48.7248074Z OK 2024-08-06T21:24:48.7248189Z 2024-08-06T21:24:48.7248296Z Generating XML reports... 2024-08-06T21:24:49.1930177Z Running test_cpp_extensions_aot_ninja 1/1 ... [2024-08-06 21:24:49.192725] 2024-08-06T21:24:51.6127204Z running install 2024-08-06T21:24:51.6128282Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-08-06T21:24:51.6129107Z !! 2024-08-06T21:24:51.6129232Z 2024-08-06T21:24:51.6129357Z ******************************************************************************** 2024-08-06T21:24:51.6129965Z Please avoid running ``setup.py`` directly. 2024-08-06T21:24:51.6130369Z Instead, use pypa/build, pypa/installer or other 2024-08-06T21:24:51.6130746Z standards-based tools. 2024-08-06T21:24:51.6130931Z 2024-08-06T21:24:51.6131311Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-08-06T21:24:51.6131837Z ******************************************************************************** 2024-08-06T21:24:51.6132090Z 2024-08-06T21:24:51.6132171Z !! 2024-08-06T21:24:51.6132398Z self.initialize_options() 2024-08-06T21:24:51.6245714Z running build 2024-08-06T21:24:51.6246181Z running build_py 2024-08-06T21:24:51.6309780Z creating build 2024-08-06T21:24:51.6310596Z creating build/lib.linux-x86_64-cpython-312 2024-08-06T21:24:51.6311278Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-08-06T21:24:51.6312563Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-08-06T21:24:51.6315858Z running build_ext 2024-08-06T21:24:51.6639615Z building 'torch_test_cpp_extension.cpp' extension 2024-08-06T21:24:51.6640318Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312 2024-08-06T21:24:51.6947160Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-08-06T21:24:51.6947957Z Compiling objects... 2024-08-06T21:24:51.6948338Z Using envvar MAX_JOBS (6) as the number of workers... 2024-08-06T21:24:52.5267005Z [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 -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 2024-08-06T21:24:52.5362370Z g++ -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 2024-08-06T21:24:52.8221850Z building 'torch_test_cpp_extension.maia' extension 2024-08-06T21:24:52.8528317Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-08-06T21:24:52.8535509Z Compiling objects... 2024-08-06T21:24:52.8535863Z Using envvar MAX_JOBS (6) as the number of workers... 2024-08-06T21:24:53.6863912Z [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 -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 2024-08-06T21:24:53.6911971Z g++ -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 2024-08-06T21:24:53.9569878Z building 'torch_test_cpp_extension.rng' extension 2024-08-06T21:24:53.9878171Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-08-06T21:24:53.9879051Z Compiling objects... 2024-08-06T21:24:53.9879377Z Using envvar MAX_JOBS (6) as the number of workers... 2024-08-06T21:24:55.0240724Z [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 -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 2024-08-06T21:24:55.0290976Z g++ -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 2024-08-06T21:24:55.3218646Z running install_lib 2024-08-06T21:24:55.3291649Z 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 2024-08-06T21:24:55.3336810Z 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 2024-08-06T21:24:55.3384640Z 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 2024-08-06T21:24:55.3435485Z running install_egg_info 2024-08-06T21:24:55.3583396Z running egg_info 2024-08-06T21:24:55.3639321Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-08-06T21:24:55.3643729Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-08-06T21:24:55.3646696Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-08-06T21:24:55.3649106Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-08-06T21:24:55.3714989Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:55.3720862Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-08-06T21:24:55.3722463Z 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) 2024-08-06T21:24:55.3724164Z 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 2024-08-06T21:24:55.3730588Z running install_scripts 2024-08-06T21:24:57.3923298Z running install 2024-08-06T21:24:57.3924703Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-08-06T21:24:57.3926085Z !! 2024-08-06T21:24:57.3926286Z 2024-08-06T21:24:57.3926496Z ******************************************************************************** 2024-08-06T21:24:57.3927202Z Please avoid running ``setup.py`` directly. 2024-08-06T21:24:57.3927940Z Instead, use pypa/build, pypa/installer or other 2024-08-06T21:24:57.3928631Z standards-based tools. 2024-08-06T21:24:57.3928985Z 2024-08-06T21:24:57.3929544Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-08-06T21:24:57.3930546Z ******************************************************************************** 2024-08-06T21:24:57.3931281Z 2024-08-06T21:24:57.3931430Z !! 2024-08-06T21:24:57.3931819Z self.initialize_options() 2024-08-06T21:24:57.4047858Z running build 2024-08-06T21:24:57.4048147Z running build_ext 2024-08-06T21:24:57.4372384Z building 'no_python_abi_suffix_test' extension 2024-08-06T21:24:57.4682188Z 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... 2024-08-06T21:24:57.4683002Z Compiling objects... 2024-08-06T21:24:57.4683334Z Using envvar MAX_JOBS (6) as the number of workers... 2024-08-06T21:24:57.4952859Z ninja: no work to do. 2024-08-06T21:24:57.4995744Z g++ -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 2024-08-06T21:24:57.5604582Z running install_lib 2024-08-06T21:24:57.5668914Z 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 2024-08-06T21:24:57.5673454Z running install_egg_info 2024-08-06T21:24:57.5824146Z running egg_info 2024-08-06T21:24:57.5880638Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-08-06T21:24:57.5884152Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-08-06T21:24:57.5886277Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-08-06T21:24:57.5945949Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-08-06T21:24:57.5951193Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-08-06T21:24:57.5952632Z 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) 2024-08-06T21:24:57.5954733Z 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 2024-08-06T21:24:57.5959297Z running install_scripts 2024-08-06T21:24:58.0340883Z 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'] ... [2024-08-06 21:24:58.033688] 2024-08-06T21:25:02.3297085Z 2024-08-06T21:25:02.3298075Z 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_795f74f612cd2e48_.log 2024-08-06T21:25:02.3305091Z Running 17 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::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 2024-08-06T21:25:02.3311156Z 2024-08-06T21:25:02.3311399Z Running dynamo/test_dynamic_shapes 1/1 ... [2024-08-06 21:25:02.329892] 2024-08-06T21:25:02.3312645Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.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'] ... [2024-08-06 21:25:02.330226] 2024-08-06T21:25:06.5746495Z 2024-08-06T21:25:06.5747658Z dynamo/test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.1_5aa5661c8c95845d_.log 2024-08-06T21:25:06.5748469Z 2024-08-06T21:25:06.5749174Z Running dynamo/test_frame_init 1/1 ... [2024-08-06 21:25:06.574771] 2024-08-06T21:25:06.5753222Z 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'] ... [2024-08-06 21:25:06.575115] 2024-08-06T21:25:08.9692335Z 2024-08-06T21:25:08.9693298Z 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_d38e133ef36e3915_.log 2024-08-06T21:25:08.9694150Z 2024-08-06T21:25:08.9694853Z Running dynamo/test_interop 1/1 ... [2024-08-06 21:25:08.969334] 2024-08-06T21:25:08.9698123Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_interop.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'] ... [2024-08-06 21:25:08.969611] 2024-08-06T21:25:11.4058831Z 2024-08-06T21:25:11.4059793Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_56785f4ba8b77f6f_.log 2024-08-06T21:25:11.4060530Z 2024-08-06T21:25:11.4061167Z Running test_matmul_cuda 1/1 ... [2024-08-06 21:25:11.405958] 2024-08-06T21:25:11.4064522Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:25:11.406248] 2024-08-06T21:25:14.0986228Z 2024-08-06T21:25:14.0987477Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_d2d4c8b6d3f8d7f9_.log 2024-08-06T21:25:14.0988416Z Running 0 items in this shard: 2024-08-06T21:25:14.0988652Z 2024-08-06T21:25:14.0989320Z Running dynamo/test_global 1/1 ... [2024-08-06 21:25:14.098725] 2024-08-06T21:25:14.0992540Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_global.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'] ... [2024-08-06 21:25:14.099020] 2024-08-06T21:25:16.5456124Z 2024-08-06T21:25:16.5457029Z dynamo/test_global 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_global_1.1_53b39df28f2a57be_.log 2024-08-06T21:25:16.5457755Z 2024-08-06T21:25:16.5458654Z Running dynamo/test_exceptions 1/1 ... [2024-08-06 21:25:16.545707] 2024-08-06T21:25:16.5462284Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exceptions.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'] ... [2024-08-06 21:25:16.545998] 2024-08-06T21:25:18.9497031Z 2024-08-06T21:25:18.9498012Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_c0df036e77a18d36_.log 2024-08-06T21:25:18.9498793Z 2024-08-06T21:25:18.9499653Z Running dynamo/test_subgraphs 1/1 ... [2024-08-06 21:25:18.949812] 2024-08-06T21:25:18.9503003Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_subgraphs.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'] ... [2024-08-06 21:25:18.950095] 2024-08-06T21:25:21.3777154Z 2024-08-06T21:25:21.3778120Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_d400e3fc0a56e7b1_.log 2024-08-06T21:25:21.3778826Z 2024-08-06T21:25:21.3780113Z Running dynamo/test_modes 1/1 ... [2024-08-06 21:25:21.377849] 2024-08-06T21:25:21.3783662Z 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'] ... [2024-08-06 21:25:21.378153] 2024-08-06T21:25:23.7485194Z 2024-08-06T21:25:23.7486160Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_7c6c73503d158776_.log 2024-08-06T21:25:23.7486861Z 2024-08-06T21:25:23.7488015Z Running dynamo/test_higher_order_ops 1/1 ... [2024-08-06 21:25:23.748630] 2024-08-06T21:25:23.7491582Z 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'] ... [2024-08-06 21:25:23.748935] 2024-08-06T21:25:26.2385166Z 2024-08-06T21:25:26.2386402Z 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_bdf6557ce5b05386_.log 2024-08-06T21:25:26.2387403Z 2024-08-06T21:25:26.2388085Z Running dynamo/test_functions 1/1 ... [2024-08-06 21:25:26.238641] 2024-08-06T21:25:26.2391742Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_functions.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'] ... [2024-08-06 21:25:26.238956] 2024-08-06T21:25:28.8322780Z 2024-08-06T21:25:28.8323702Z dynamo/test_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_functions_1.1_61518fb190869d88_.log 2024-08-06T21:25:28.8324410Z 2024-08-06T21:25:28.8325794Z Running dynamo/test_modules 1/1 ... [2024-08-06 21:25:28.832417] 2024-08-06T21:25:28.8329296Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_modules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:25:28.832728] 2024-08-06T21:25:31.5930398Z 2024-08-06T21:25:31.5931754Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_4b1291eb3f342907_.log 2024-08-06T21:25:31.5932764Z 2024-08-06T21:25:31.5933147Z Running dynamo/test_model_output 1/1 ... [2024-08-06 21:25:31.592900] 2024-08-06T21:25:31.5935543Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_model_output.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:25:31.593289] 2024-08-06T21:25:34.0641959Z 2024-08-06T21:25:34.0643304Z dynamo/test_model_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_model_output_1.1_e0f9a3c26aa4e3ed_.log 2024-08-06T21:25:34.0644212Z 2024-08-06T21:25:34.0644477Z Running dynamo/test_export 1/1 ... [2024-08-06 21:25:34.064305] 2024-08-06T21:25:34.0648258Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:25:34.064603] 2024-08-06T21:25:36.5565479Z 2024-08-06T21:25:36.5566374Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_47a929a8c137c1c8_.log 2024-08-06T21:25:36.5567138Z 2024-08-06T21:25:36.5568053Z Running dynamo/test_ctx_manager 1/1 ... [2024-08-06 21:25:36.556657] 2024-08-06T21:25:36.5571443Z 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'] ... [2024-08-06 21:25:36.556933] 2024-08-06T21:25:38.9509171Z 2024-08-06T21:25:38.9510130Z 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_dba4649287189ea1_.log 2024-08-06T21:25:38.9510825Z 2024-08-06T21:25:38.9512685Z Running functorch/test_ac 1/1 ... [2024-08-06 21:25:38.951091] 2024-08-06T21:25:38.9516191Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_ac.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'] ... [2024-08-06 21:25:38.951419] 2024-08-06T21:25:41.3604017Z 2024-08-06T21:25:41.3605041Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_10b95c3511458c30_.log 2024-08-06T21:25:41.3605753Z 2024-08-06T21:25:41.3607435Z Running dynamo/test_profiler 1/1 ... [2024-08-06 21:25:41.360560] 2024-08-06T21:25:41.3610897Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.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'] ... [2024-08-06 21:25:41.360882] 2024-08-06T21:25:43.8647882Z 2024-08-06T21:25:43.8648827Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_4492e28954d984df_.log 2024-08-06T21:25:43.8649759Z 2024-08-06T21:25:43.8651353Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-08-06 21:25:43.864959] 2024-08-06T21:25:43.8655279Z 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'] ... [2024-08-06 21:25:43.865274] 2024-08-06T21:25:46.3594587Z 2024-08-06T21:25:46.3595675Z 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_714382207555a166_.log 2024-08-06T21:25:46.3596669Z 2024-08-06T21:25:46.3597257Z Running dynamo/test_trace_rules 1/1 ... [2024-08-06 21:25:46.359583] 2024-08-06T21:25:46.3601023Z 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'] ... [2024-08-06 21:25:46.359891] 2024-08-06T21:25:48.7563872Z 2024-08-06T21:25:48.7565091Z 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_1083e31986ef516d_.log 2024-08-06T21:25:48.7565900Z 2024-08-06T21:25:48.7566636Z Running dynamo/test_unspec 1/1 ... [2024-08-06 21:25:48.756522] 2024-08-06T21:25:48.7570543Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_unspec.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:25:48.756848] 2024-08-06T21:25:51.1531417Z 2024-08-06T21:25:51.1532613Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_299835feecc98536_.log 2024-08-06T21:25:51.1533312Z 2024-08-06T21:25:51.1535013Z Running dynamo/test_input_attr_tracking 1/1 ... [2024-08-06 21:25:51.153310] 2024-08-06T21:25:51.1538403Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_input_attr_tracking.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'] ... [2024-08-06 21:25:51.153628] 2024-08-06T21:25:53.5878241Z 2024-08-06T21:25:53.5879280Z dynamo/test_input_attr_tracking 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_input_attr_tracking_1.1_e87f7bc80efaaf91_.log 2024-08-06T21:25:53.5880063Z 2024-08-06T21:25:53.5880768Z Running dynamo/test_recompile_ux 1/1 ... [2024-08-06 21:25:53.587925] 2024-08-06T21:25:53.5884171Z 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'] ... [2024-08-06 21:25:53.588204] 2024-08-06T21:25:55.9548735Z 2024-08-06T21:25:55.9549727Z 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_4e2da4dce81be903_.log 2024-08-06T21:25:55.9550449Z 2024-08-06T21:25:55.9553174Z Running dynamo/test_structured_trace 1/1 ... [2024-08-06 21:25:55.955061] 2024-08-06T21:25:55.9556343Z 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'] ... [2024-08-06 21:25:55.955378] 2024-08-06T21:25:58.3656639Z 2024-08-06T21:25:58.3657636Z 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_4df4730202c67915_.log 2024-08-06T21:25:58.3658788Z 2024-08-06T21:25:58.3659272Z Running test_cuda_sanitizer 1/1 ... [2024-08-06 21:25:58.365760] 2024-08-06T21:25:58.3662697Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_sanitizer.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:25:58.366058] 2024-08-06T21:26:00.8469910Z 2024-08-06T21:26:00.8470830Z test_cuda_sanitizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_sanitizer_1.1_273f77499f069bd1_.log 2024-08-06T21:26:00.8471606Z Running 0 items in this shard: 2024-08-06T21:26:00.8471800Z 2024-08-06T21:26:00.8473219Z Running test_cuda 1/1 ... [2024-08-06 21:26:00.847160] 2024-08-06T21:26:00.8476858Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:00.847481] 2024-08-06T21:26:04.4820165Z 2024-08-06T21:26:04.4821557Z test_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_1.1_4934c654fac26f3a_.log 2024-08-06T21:26:04.4822293Z Running 0 items in this shard: 2024-08-06T21:26:04.4822501Z 2024-08-06T21:26:04.4823398Z Running test_cuda_multigpu 1/1 ... [2024-08-06 21:26:04.482143] 2024-08-06T21:26:04.4827423Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.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'] ... [2024-08-06 21:26:04.482462] 2024-08-06T21:26:07.0552266Z 2024-08-06T21:26:07.0553183Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_2acd1e830528dcef_.log 2024-08-06T21:26:07.0553973Z Running 0 items in this shard: 2024-08-06T21:26:07.0554165Z 2024-08-06T21:26:07.0555341Z Running test_quantization 3/6 ... [2024-08-06 21:26:07.055363] 2024-08-06T21:26:07.0558983Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=3', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:07.055672] 2024-08-06T21:26:11.0782308Z 2024-08-06T21:26:11.0783305Z test_quantization 3/6 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.6_d138b557b71a8bca_.log 2024-08-06T21:26:11.0784111Z Running 0 items in this shard: 2024-08-06T21:26:11.0784366Z 2024-08-06T21:26:11.0785557Z Running optim/test_lrscheduler 1/1 ... [2024-08-06 21:26:11.078378] 2024-08-06T21:26:11.0789180Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_lrscheduler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:11.078696] 2024-08-06T21:26:13.4955168Z 2024-08-06T21:26:13.4956239Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_a8c93abf00652011_.log 2024-08-06T21:26:13.4957163Z 2024-08-06T21:26:13.5016248Z Running dynamo/test_dynamic_shapes 1/1 ... [2024-08-06 21:26:13.501343] 2024-08-06T21:26:13.5017727Z Running dynamo/test_frame_init 1/1 ... [2024-08-06 21:26:13.501552] 2024-08-06T21:26:13.5021399Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.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'] ... [2024-08-06 21:26:13.501812] 2024-08-06T21:26:13.5023594Z Running dynamo/test_interop 1/1 ... [2024-08-06 21:26:13.501938] 2024-08-06T21:26:13.5025495Z 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'] ... [2024-08-06 21:26:13.501980] 2024-08-06T21:26:13.5028561Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_interop.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'] ... [2024-08-06 21:26:13.502412] 2024-08-06T21:26:15.9884520Z 2024-08-06T21:26:15.9885782Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_6ef229f731962a5d_.log 2024-08-06T21:26:15.9886775Z 2024-08-06T21:26:15.9892912Z 2024-08-06T21:26:15.9893838Z 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_12566f0e7b4f790e_.log 2024-08-06T21:26:15.9903289Z 2024-08-06T21:26:17.6208539Z 2024-08-06T21:26:17.6210395Z dynamo/test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.1_1a0e8b50b7d60caa_.log 2024-08-06T21:26:17.6211810Z 2024-08-06T21:26:18.6070414Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:18.6444846Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:18.7252741Z Running test_matmul_cuda 1/1 ... [2024-08-06 21:26:18.724868] 2024-08-06T21:26:18.7255921Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:18.725233] 2024-08-06T21:26:18.7478071Z Running dynamo/test_global 1/1 ... [2024-08-06 21:26:18.747469] 2024-08-06T21:26:18.7481442Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_global.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'] ... [2024-08-06 21:26:18.747870] 2024-08-06T21:26:20.1536014Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:20.2130181Z Running dynamo/test_exceptions 1/1 ... [2024-08-06 21:26:20.212650] 2024-08-06T21:26:20.2133280Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exceptions.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'] ... [2024-08-06 21:26:20.213003] 2024-08-06T21:26:21.2538602Z 2024-08-06T21:26:21.2540055Z dynamo/test_global 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_global_1.1_85a0ef1513c7e4c8_.log 2024-08-06T21:26:21.2540999Z 2024-08-06T21:26:21.4754457Z 2024-08-06T21:26:21.4756281Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_d3335804ae7bc03a_.log 2024-08-06T21:26:21.4758007Z Running 0 items in this shard: 2024-08-06T21:26:21.4758432Z 2024-08-06T21:26:22.7820665Z 2024-08-06T21:26:22.7821822Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_4dd5ff5c5ab27f08_.log 2024-08-06T21:26:22.7822547Z 2024-08-06T21:26:23.8645155Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:23.9236802Z Running dynamo/test_subgraphs 1/1 ... [2024-08-06 21:26:23.923323] 2024-08-06T21:26:23.9241112Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_subgraphs.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'] ... [2024-08-06 21:26:23.923715] 2024-08-06T21:26:24.0864656Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:24.1458835Z Running dynamo/test_modes 1/1 ... [2024-08-06 21:26:24.145509] 2024-08-06T21:26:24.1461749Z 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'] ... [2024-08-06 21:26:24.145856] 2024-08-06T21:26:25.3724709Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:25.4324648Z Running dynamo/test_higher_order_ops 1/1 ... [2024-08-06 21:26:25.432015] 2024-08-06T21:26:25.4327253Z 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'] ... [2024-08-06 21:26:25.432389] 2024-08-06T21:26:26.4584526Z 2024-08-06T21:26:26.4585500Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_2facd8fb431468f5_.log 2024-08-06T21:26:26.4586259Z 2024-08-06T21:26:26.6867448Z 2024-08-06T21:26:26.6868592Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_5e12ab54c88af52f_.log 2024-08-06T21:26:26.6869565Z 2024-08-06T21:26:28.1361461Z 2024-08-06T21:26:28.1363069Z 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_b7c6c231930d8140_.log 2024-08-06T21:26:28.1364479Z 2024-08-06T21:26:29.0246848Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:29.0839453Z Running dynamo/test_functions 1/1 ... [2024-08-06 21:26:29.083549] 2024-08-06T21:26:29.0842120Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_functions.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'] ... [2024-08-06 21:26:29.083933] 2024-08-06T21:26:29.3483054Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:29.4084360Z Running dynamo/test_modules 1/1 ... [2024-08-06 21:26:29.408050] 2024-08-06T21:26:29.4087938Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_modules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:29.408411] 2024-08-06T21:26:30.7393791Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:30.7986304Z Running dynamo/test_model_output 1/1 ... [2024-08-06 21:26:30.798182] 2024-08-06T21:26:30.7988825Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_model_output.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:30.798563] 2024-08-06T21:26:31.6730122Z 2024-08-06T21:26:31.6731466Z dynamo/test_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_functions_1.1_8b5b3a9d787f6637_.log 2024-08-06T21:26:31.6732895Z 2024-08-06T21:26:32.1207472Z 2024-08-06T21:26:32.1208899Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_e2ea42dc650b607a_.log 2024-08-06T21:26:32.1210096Z 2024-08-06T21:26:33.4136473Z 2024-08-06T21:26:33.4137764Z dynamo/test_model_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_model_output_1.1_dc96d190cf1f205c_.log 2024-08-06T21:26:33.4138570Z 2024-08-06T21:26:34.2357817Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:34.2944665Z Running dynamo/test_export 1/1 ... [2024-08-06 21:26:34.294023] 2024-08-06T21:26:34.2948150Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:34.294456] 2024-08-06T21:26:34.8514426Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:34.9294040Z Running dynamo/test_ctx_manager 1/1 ... [2024-08-06 21:26:34.928915] 2024-08-06T21:26:34.9296603Z 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'] ... [2024-08-06 21:26:34.929282] 2024-08-06T21:26:36.0392936Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:36.0992791Z Running functorch/test_ac 1/1 ... [2024-08-06 21:26:36.098862] 2024-08-06T21:26:36.0996228Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_ac.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'] ... [2024-08-06 21:26:36.099254] 2024-08-06T21:26:36.9712815Z 2024-08-06T21:26:36.9714294Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_0d17e5b69a354f95_.log 2024-08-06T21:26:36.9715051Z 2024-08-06T21:26:37.4357582Z 2024-08-06T21:26:37.4358833Z 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_5e01df51f5fe1a0b_.log 2024-08-06T21:26:37.4359529Z 2024-08-06T21:26:38.7052531Z 2024-08-06T21:26:38.7054907Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_cfa93949a6d65bd0_.log 2024-08-06T21:26:38.7055893Z 2024-08-06T21:26:39.5591962Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:39.6189182Z Running dynamo/test_profiler 1/1 ... [2024-08-06 21:26:39.618566] 2024-08-06T21:26:39.6192512Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.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'] ... [2024-08-06 21:26:39.618959] 2024-08-06T21:26:40.0671651Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:40.1711441Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-08-06 21:26:40.170595] 2024-08-06T21:26:40.1717855Z 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'] ... [2024-08-06 21:26:40.171304] 2024-08-06T21:26:41.3086978Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:41.3676088Z Running dynamo/test_trace_rules 1/1 ... [2024-08-06 21:26:41.367264] 2024-08-06T21:26:41.3679943Z 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'] ... [2024-08-06 21:26:41.367652] 2024-08-06T21:26:42.2076988Z 2024-08-06T21:26:42.2078529Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_e7a5bdee083096f6_.log 2024-08-06T21:26:42.2079520Z 2024-08-06T21:26:42.7723619Z 2024-08-06T21:26:42.7725671Z 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_b011a02b0a0b8f7f_.log 2024-08-06T21:26:42.7727223Z 2024-08-06T21:26:43.9681004Z 2024-08-06T21:26:43.9682491Z 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_96817b3a0bb21f37_.log 2024-08-06T21:26:43.9683750Z 2024-08-06T21:26:44.7528374Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:44.8125273Z Running dynamo/test_unspec 1/1 ... [2024-08-06 21:26:44.812145] 2024-08-06T21:26:44.8128933Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_unspec.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:44.812525] 2024-08-06T21:26:45.4229935Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:45.4817602Z Running dynamo/test_input_attr_tracking 1/1 ... [2024-08-06 21:26:45.481365] 2024-08-06T21:26:45.4820409Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_input_attr_tracking.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'] ... [2024-08-06 21:26:45.481721] 2024-08-06T21:26:46.5510705Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:46.6098463Z Running dynamo/test_recompile_ux 1/1 ... [2024-08-06 21:26:46.609450] 2024-08-06T21:26:46.6101771Z 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'] ... [2024-08-06 21:26:46.609823] 2024-08-06T21:26:47.3830218Z 2024-08-06T21:26:47.3831623Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_5e42758882be8cc9_.log 2024-08-06T21:26:47.3833040Z 2024-08-06T21:26:47.9558520Z 2024-08-06T21:26:47.9560244Z dynamo/test_input_attr_tracking 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_input_attr_tracking_1.1_c397e50618664f46_.log 2024-08-06T21:26:47.9561937Z 2024-08-06T21:26:49.1636173Z 2024-08-06T21:26:49.1637564Z 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_9c99b152d882da50_.log 2024-08-06T21:26:49.1638699Z 2024-08-06T21:26:49.9533650Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:50.0124692Z Running dynamo/test_structured_trace 1/1 ... [2024-08-06 21:26:50.012123] 2024-08-06T21:26:50.0128787Z 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'] ... [2024-08-06 21:26:50.012508] 2024-08-06T21:26:50.6029102Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:50.6624146Z Running test_cuda_sanitizer 1/1 ... [2024-08-06 21:26:50.661965] 2024-08-06T21:26:50.6626633Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_sanitizer.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:50.662362] 2024-08-06T21:26:51.7315175Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:51.7913184Z Running test_cuda 1/1 ... [2024-08-06 21:26:51.790956] 2024-08-06T21:26:51.7916218Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:51.791321] 2024-08-06T21:26:52.6164293Z 2024-08-06T21:26:52.6165811Z 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_7008734835e82c96_.log 2024-08-06T21:26:52.6166984Z 2024-08-06T21:26:53.2633942Z 2024-08-06T21:26:53.2635442Z test_cuda_sanitizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_sanitizer_1.1_09bc1ad69f3c1630_.log 2024-08-06T21:26:53.2637120Z Running 0 items in this shard: 2024-08-06T21:26:53.2637559Z 2024-08-06T21:26:55.2126055Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:55.2714464Z Running test_cuda_multigpu 1/1 ... [2024-08-06 21:26:55.271034] 2024-08-06T21:26:55.2717173Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.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'] ... [2024-08-06 21:26:55.271388] 2024-08-06T21:26:55.7999373Z 2024-08-06T21:26:55.8000590Z test_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_1.1_787f8191139a065b_.log 2024-08-06T21:26:55.8001689Z Running 0 items in this shard: 2024-08-06T21:26:55.8001990Z 2024-08-06T21:26:55.9621718Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:56.0219122Z Running test_quantization 3/6 ... [2024-08-06 21:26:56.021543] 2024-08-06T21:26:56.0221973Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=3', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:56.021915] 2024-08-06T21:26:57.9322001Z 2024-08-06T21:26:57.9323924Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_4162b90e5aae429a_.log 2024-08-06T21:26:57.9324824Z Running 0 items in this shard: 2024-08-06T21:26:57.9325020Z 2024-08-06T21:26:58.3704727Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:26:58.4301123Z Running optim/test_lrscheduler 1/1 ... [2024-08-06 21:26:58.429714] 2024-08-06T21:26:58.4304256Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_lrscheduler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-06 21:26:58.430082] 2024-08-06T21:27:00.8048426Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:27:00.9200607Z 2024-08-06T21:27:00.9202191Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_e29bebd290a57613_.log 2024-08-06T21:27:00.9203478Z 2024-08-06T21:27:03.4023835Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:34:30.1176964Z 2024-08-06T21:34:30.1177946Z test_quantization 3/6 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.6_6aa67db22efcb4b6_.log 2024-08-06T21:34:30.1326595Z Running 200 items in this shard: test/test_quantization.py::TestQuantizedOps::test_batch_norm_relu, test/test_quantization.py::TestQuantizedOps::test_equal, test/test_quantization.py::TestQuantizedOps::test_interpolate, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_cudnn, test/test_quantization.py::TestQuantizedOps::test_max_pool3d, test/test_quantization.py::TestQuantizedOps::test_max_pool3d_nhwc, test/test_quantization.py::TestQNNPackOps::test_adaptive_avg_pool2d, test/test_quantization.py::TestQuantizedLinear::test_qlinear_leaky_relu, test/test_quantization.py::TestQuantizedConv::test_benchmark, test/test_quantization.py::TestQuantizedConv::test_conv_reorder_issue_onednn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_relu_float_output_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv2d, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear_dynamic_fp16, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_4bit, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_half_precision_numerics, test/test_quantization.py::TestFakeQuantizeOps::test_fq_module_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_tensor_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_tensor_cuda, test/test_quantization.py::TestQuantizedTensor::test_bfp16_quantize, test/test_quantization.py::TestQuantizedTensor::test_choose_qparams, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dynamic_quant_pattern, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel_group, test/test_quantization.py::TestQuantizedTensor::test_qtensor_channel_float_assignment, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_channel, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_load_save, test/test_quantization.py::TestQuantizedTensor::test_qtensor_per_channel_permute, test/test_quantization.py::TestQuantizedTensor::test_quant_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_repeat, test/test_quantization.py::TestFakeQuantize::test_quant_min_max_override, test/test_quantization.py::TestObserver::test_histogram_observer_consistent_buffer_shape, test/test_quantization.py::TestObserver::test_state_dict_respects_device_affinity, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_add, test/test_quantization.py::TestStaticQuantizedModule::test_leaky_relu, test/test_quantization.py::TestStaticQuantizedModule::test_linear, test/test_quantization.py::TestStaticQuantizedModule::test_prelu, test/test_quantization.py::TestRecordHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestDistributed::test_fake_quant_preserves_buffers, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_simple, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_tensor, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_ptdq, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_ptsq, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested2, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_skip_quant, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_two_layers, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_per_channel_linear_quantize, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_two_layers, test/test_quantization.py::TestQuantizeEagerOps::test_conv_3d, test/test_quantization.py::TestQuantizeEagerOps::test_relu, 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_relu, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_fixed_qparam_ops, test/test_quantization.py::TestFuseEager::test_fuse_module_eval, test/test_quantization.py::TestFuseEager::test_fuse_modules_with_nested_hooks, test/test_quantization.py::TestFuseEager::test_fusion_sequential_model_eval, test/test_quantization.py::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_weights_linear_static, test/test_quantization.py::TestEqualizeEager::test_equalize_fused_convrelu, test/test_quantization.py::TestFuseFx::test_fuse_addtional_fuser_method, test/test_quantization.py::TestFuseFx::test_fuse_linear_bn_eval, test/test_quantization.py::TestFuseFx::test_fuse_linear_tanh_for_onednn_backend, test/test_quantization.py::TestFuseFx::test_fuse_module_relu, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_matchallnode, test/test_quantization.py::TestFuseFx::test_qconfig_fused_module, test/test_quantization.py::TestQuantizeFx::test_conv_linear_not_reference, test/test_quantization.py::TestQuantizeFx::test_conv_lowering, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_set_observed_to_quantized_mapping, test/test_quantization.py::TestQuantizeFx::test_default_qconfig_mapping_override_global, test/test_quantization.py::TestQuantizeFx::test_get_executorch_backend_config, test/test_quantization.py::TestQuantizeFx::test_linear_shape_view, test/test_quantization.py::TestQuantizeFx::test_match_pattern_with_multiple_args, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_input_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_names, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_output_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_qat_and_script, test/test_quantization.py::TestQuantizeFx::test_qat_skip_untraced, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_setup, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_repr, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_global, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_to_dict, test/test_quantization.py::TestQuantizeFx::test_quant_output_always_observed, test/test_quantization.py::TestQuantizeFx::test_size_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_static_lstm_with_custom_fixed_qparams, test/test_quantization.py::TestQuantizeFx::test_trace_quantize_per_tensor, test/test_quantization.py::TestQuantizeFxOps::test_bmm, test/test_quantization.py::TestQuantizeFxOps::test_clamp, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_wrong_qconfig, test/test_quantization.py::TestQuantizeFxOps::test_functional_conv, test/test_quantization.py::TestQuantizeFxOps::test_functional_linear, test/test_quantization.py::TestQuantizeFxOps::test_getitem, test/test_quantization.py::TestQuantizeFxOps::test_narrow, test/test_quantization.py::TestQuantizeFxOps::test_pixel_unshuffle, test/test_quantization.py::TestQuantizeFxOps::test_ref_pattern_multi_use, test/test_quantization.py::TestQuantizeFxModels::test_model_dropout, test/test_quantization.py::TestQuantizeFxModels::test_torchvision, test/test_quantization.py::TestGraphUtils::test_customized_equivalet_types_dict, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_two_dq, test/test_quantization.py::TestMetaDataPorting::test_no_metadata_porting_through_unknown_ops, test/test_quantization.py::TestNumericDebugger::test_simple, test/test_quantization.py::TestQuantizePT2E::test_allow_implicit_sharing, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_transform_for_annotation, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_observer_dedup, test/test_quantization.py::TestQuantizePT2E::test_fold_quantize, test/test_quantization.py::TestQuantizePT2E::test_fold_quantize_per_channel, test/test_quantization.py::TestQuantizePT2E::test_groupwise_per_channel_quant, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_int16, test/test_quantization.py::TestQuantizePT2E::test_save_load, test/test_quantization.py::TestQuantizePT2E::test_transform_for_annotation, test/test_quantization.py::TestQuantizePT2E::test_wo_annotate_conv_output_quantizer, test/test_quantization.py::TestXNNPACKQuantizer::test_add_mul_long, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_gru, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_relu, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_with_dynamic_shape, test/test_quantization.py::TestXNNPACKQuantizer::test_mul_float32_max, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_type_case_2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_maxpool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_unary, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_prepare_qat_conv_bn_fusion_getitem_placeholder, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_bias_derived_qspec, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype, test/test_quantization.py::TestFXGraphMatcher::test_op_relationship_mapping, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_conv_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_int8_mod, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_vs_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_kwargs_shadow, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_shadow_activations_fqn, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_functions, test/test_quantization.py::TestFXNumericSuiteNShadows::test_extract_weights_linear, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_logger_enabled_and_save_activations_flags, test/test_quantization.py::TestFXNumericSuiteNShadows::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_from_list, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_repr, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_linear, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_resnet18, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_report_gen_empty, test/test_quantization.py::TestFxDetectOutliers::test_multiple_run_consistent_spike_outlier_report_gen, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_no_match, test/test_quantization.py::TestEqualizeFx::test_input_weight_eq_observer, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_results, test/test_quantization.py::TestSerialization::test_linear_relu_package_quantization_transforms, test/test_quantization.py::TestQuantizeJit::test_conv_bn, test/test_quantization.py::TestQuantizeJit::test_single_linear, test/test_quantization.py::TestQuantizeJit::test_single_linear_dynamic, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_complex_cases, test/test_quantization.py::TestQuantizeJitPasses::test_inplace_option, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_child_qconfig, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_reused_weight, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_skip_values, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequant_same_value, test/test_quantization.py::TestQuantizeJitOps::test_group_norm, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_convert_dynamic_fp16, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_shared_weights, test/test_quantization.py::TestDeprecatedJitQuantized::test_rnn_quantized, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_qconfig, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quant_type, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize_jit, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_utils, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_utils, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_qat, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_linear_relu, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_convert, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fusion_patterns, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_mul_cpu_float8_e4m3fn 2024-08-06T21:34:30.1432740Z 2024-08-06T21:34:31.2338995Z 2024-08-06T21:34:31.2339550Z real 68m16.495s 2024-08-06T21:34:31.2339995Z user 97m40.968s 2024-08-06T21:34:31.2340333Z sys 8m15.531s 2024-08-06T21:34:31.2340645Z + assert_git_not_dirty 2024-08-06T21:34:31.2341258Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *rocm* ]] 2024-08-06T21:34:31.2342099Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *xla* ]] 2024-08-06T21:34:31.2345963Z ++ git status --porcelain 2024-08-06T21:34:31.2346326Z ++ grep -v '?? third_party' 2024-08-06T21:34:53.9253890Z ++ true 2024-08-06T21:34:53.9257564Z + git_status= 2024-08-06T21:34:53.9257924Z + [[ -n '' ]] 2024-08-06T21:34:53.9258172Z + [[ 1 == 1 ]] 2024-08-06T21:34:53.9258410Z + test_aten 2024-08-06T21:34:53.9258788Z + echo 'Running ATen tests with pytorch lib' 2024-08-06T21:34:53.9259264Z Running ATen tests with pytorch lib 2024-08-06T21:34:53.9259766Z + [[ -n '' ]] 2024-08-06T21:34:53.9260235Z + echo 'Running test with the build folder' 2024-08-06T21:34:53.9260838Z Running test with the build folder 2024-08-06T21:34:53.9261157Z + TEST_BASE_DIR=build/bin 2024-08-06T21:34:53.9261634Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so build/bin 2024-08-06T21:34:53.9299403Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2*' build/bin 2024-08-06T21:34:53.9308894Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libmkldnn*' build/bin 2024-08-06T21:34:53.9318834Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnccl*' build/bin 2024-08-06T21:34:53.9330455Z + 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 2024-08-06T21:34:53.9338269Z + ls build/bin 2024-08-06T21:34:53.9385561Z BackoffTest cpu_rng_test 2024-08-06T21:34:53.9386353Z CMakeFiles dispatch_key_set_test 2024-08-06T21:34:53.9387104Z CTestTestfile.cmake dlconvertor_test 2024-08-06T21:34:53.9387795Z CppSignature_test example_allreduce 2024-08-06T21:34:53.9388184Z Dict_test extension_backend_test 2024-08-06T21:34:53.9388503Z Dimname_test half_test 2024-08-06T21:34:53.9388893Z FileStoreTest inline_container_test 2024-08-06T21:34:53.9389292Z HashStoreTest ivalue_test 2024-08-06T21:34:53.9389682Z IListRef_test kernel_function_legacy_test 2024-08-06T21:34:53.9390101Z KernelFunction_test kernel_function_test 2024-08-06T21:34:53.9390486Z List_test kernel_lambda_legacy_test 2024-08-06T21:34:53.9391057Z Makefile kernel_lambda_test 2024-08-06T21:34:53.9391409Z MaybeOwned_test kernel_stackbased_test 2024-08-06T21:34:53.9391788Z NamedTensor_test lazy_tensor_test 2024-08-06T21:34:53.9392210Z ProcessGroupGlooTest legacy_vmap_test 2024-08-06T21:34:53.9392573Z StorageUtils_test libc10.so 2024-08-06T21:34:53.9392892Z TCPStoreTest 'libcaffe2*' 2024-08-06T21:34:53.9393210Z aot_model_compiler_test 'libmkldnn*' 2024-08-06T21:34:53.9393552Z apply_utils_test 'libnccl*' 2024-08-06T21:34:53.9393862Z atest libtorch.so 2024-08-06T21:34:53.9394164Z backend_fallback_test libtorch_cpu.so 2024-08-06T21:34:53.9394522Z basic libtorch_global_deps.so 2024-08-06T21:34:53.9394865Z broadcast_test libtorch_python.so 2024-08-06T21:34:53.9395266Z c10_Bitset_test libtorchbind_test.so 2024-08-06T21:34:53.9395873Z c10_CompileTimeFunctionPointer_test make_boxed_from_unboxed_functor_test 2024-08-06T21:34:53.9396395Z c10_ConstexprCrc_test math_kernel_test 2024-08-06T21:34:53.9396789Z c10_DeadlockDetection_test memory_format_test 2024-08-06T21:34:53.9397218Z c10_DeviceGuard_test memory_overlapping_test 2024-08-06T21:34:53.9397616Z c10_Device_test mobile_memory_cleanup 2024-08-06T21:34:53.9397965Z c10_DispatchKeySet_test native_test 2024-08-06T21:34:53.9398314Z c10_Half_test op_allowlist_test 2024-08-06T21:34:53.9398701Z c10_InlineDeviceGuard_test op_registration_test 2024-08-06T21:34:53.9399118Z c10_InlineStreamGuard_test operator_name_test 2024-08-06T21:34:53.9399517Z c10_LeftRight_test operators_test 2024-08-06T21:34:53.9399936Z c10_Metaprogramming_test packedtensoraccessor_test 2024-08-06T21:34:53.9400346Z c10_Scalar_test parallel_benchmark 2024-08-06T21:34:53.9400699Z c10_SizesAndStrides_test pow_test 2024-08-06T21:34:53.9401055Z c10_StreamGuard_test protoc 2024-08-06T21:34:53.9401370Z c10_SymInt_test protoc-3.13.0.0 2024-08-06T21:34:53.9401725Z c10_Synchronized_test quantized_test 2024-08-06T21:34:53.9402151Z c10_ThreadLocal_test reduce_ops_test 2024-08-06T21:34:53.9402523Z c10_TypeIndex_test reportMemoryUsage_test 2024-08-06T21:34:53.9402915Z c10_TypeList_test scalar_tensor_test 2024-08-06T21:34:53.9403268Z c10_TypeTraits_test scalar_test 2024-08-06T21:34:53.9403616Z c10_accumulate_test static_runtime_bench 2024-08-06T21:34:53.9403999Z c10_bfloat16_test static_runtime_test 2024-08-06T21:34:53.9404380Z c10_bit_cast_test stride_properties_test 2024-08-06T21:34:53.9404763Z c10_complex_math_test tensor_iterator_test 2024-08-06T21:34:53.9405136Z c10_complex_test test_api 2024-08-06T21:34:53.9405448Z c10_cow_test test_cpp_rpc 2024-08-06T21:34:53.9405773Z c10_exception_test test_dist_autograd 2024-08-06T21:34:53.9406160Z c10_flags_test test_edge_op_registration 2024-08-06T21:34:53.9406528Z c10_generic_math_test test_jit 2024-08-06T21:34:53.9406860Z c10_intrusive_ptr_benchmark test_lazy 2024-08-06T21:34:53.9407232Z c10_intrusive_ptr_test test_mobile_nnc 2024-08-06T21:34:53.9407585Z c10_irange_test test_parallel 2024-08-06T21:34:53.9407895Z c10_lazy_test test_tensorexpr 2024-08-06T21:34:53.9408225Z c10_logging_test thread_init_test 2024-08-06T21:34:53.9408572Z c10_optional_test torch_shm_manager 2024-08-06T21:34:53.9408962Z c10_ordered_preserving_dict_test tutorial_tensorexpr 2024-08-06T21:34:53.9409359Z c10_registry_test type_ptr_test 2024-08-06T21:34:53.9409689Z c10_small_vector_test type_test 2024-08-06T21:34:53.9410023Z c10_ssize_test undefined_tensor_test 2024-08-06T21:34:53.9410406Z c10_string_util_test vec_test_all_types_AVX2 2024-08-06T21:34:53.9410819Z c10_string_view_test vec_test_all_types_AVX512 2024-08-06T21:34:53.9411219Z c10_tempfile_test vec_test_all_types_DEFAULT 2024-08-06T21:34:53.9411650Z c10_typeid_test verify_api_visibility 2024-08-06T21:34:53.9412011Z cmake_install.cmake weakref_test 2024-08-06T21:34:53.9412341Z cpu_allocator_test wrapdim_test 2024-08-06T21:34:53.9412722Z cpu_generator_test xla_tensor_test 2024-08-06T21:34:53.9413066Z cpu_profiling_allocator_test 2024-08-06T21:34:53.9413359Z + aten/tools/run_tests.sh build/bin 2024-08-06T21:34:53.9427239Z + set -e 2024-08-06T21:34:53.9429736Z ++ dirname aten/tools/run_tests.sh 2024-08-06T21:34:53.9444968Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2024-08-06T21:34:53.9445644Z + export CPP_TESTS_DIR=build/bin 2024-08-06T21:34:53.9445955Z + CPP_TESTS_DIR=build/bin 2024-08-06T21:34:53.9446256Z + VALGRIND=ON 2024-08-06T21:34:53.9448093Z + 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 2024-08-06T21:34:54.0487255Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-08-06T21:34:54.0488152Z import pkg_resources 2024-08-06T21:34:56.0019660Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:34:56.9470305Z Downloading https://ossci-metrics.s3.amazonaws.com/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json 2024-08-06T21:34:56.9472612Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-06T21:34:56.9581540Z Found test times from artifacts 2024-08-06T21:34:57.0005284Z Found test times from artifacts 2024-08-06T21:34:57.0020096Z Running all tests 2024-08-06T21:34:57.0025007Z Running parallel tests on 3 processes 2024-08-06T21:34:57.0026564Z Name: tests to run (est. time: 0.0min) 2024-08-06T21:34:57.0027218Z Serial tests (0): 2024-08-06T21:34:57.0027630Z Parallel tests (19): 2024-08-06T21:34:57.0028017Z cpp/Dict_test 1/1 2024-08-06T21:34:57.0028383Z cpp/Dimname_test 1/1 2024-08-06T21:34:57.0028812Z cpp/NamedTensor_test 1/1 2024-08-06T21:34:57.0029270Z cpp/apply_utils_test 1/1 2024-08-06T21:34:57.0029701Z cpp/atest 1/1 2024-08-06T21:34:57.0030084Z cpp/basic 1/1 2024-08-06T21:34:57.0030458Z cpp/broadcast_test 1/1 2024-08-06T21:34:57.0030897Z cpp/cpu_generator_test 1/1 2024-08-06T21:34:57.0031370Z cpp/dlconvertor_test 1/1 2024-08-06T21:34:57.0031818Z cpp/extension_backend_test 1/1 2024-08-06T21:34:57.0032318Z cpp/lazy_tensor_test 1/1 2024-08-06T21:34:57.0032782Z cpp/legacy_vmap_test 1/1 2024-08-06T21:34:57.0033224Z cpp/native_test 1/1 2024-08-06T21:34:57.0033657Z cpp/operators_test 1/1 2024-08-06T21:34:57.0034097Z cpp/scalar_tensor_test 1/1 2024-08-06T21:34:57.0034549Z cpp/scalar_test 1/1 2024-08-06T21:34:57.0035014Z cpp/tensor_iterator_test 1/1 2024-08-06T21:34:57.0035544Z cpp/undefined_tensor_test 1/1 2024-08-06T21:34:57.0036056Z cpp/wrapdim_test 1/1 2024-08-06T21:34:57.0036555Z Name: excluded (est. time: 0.0min) 2024-08-06T21:34:57.0037024Z Serial tests (0): 2024-08-06T21:34:57.0037388Z Parallel tests (0): 2024-08-06T21:34:57.0038027Z Starting test batch 'tests to run' 0.0 seconds after initiating testing 2024-08-06T21:34:57.0090572Z Running cpp/Dict_test 1/1 ... [2024-08-06 21:34:57.008686] 2024-08-06T21:34:57.0107881Z 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-06430c59b411466f.xml', '-x', '--reruns=2'] ... [2024-08-06 21:34:57.010361] 2024-08-06T21:34:59.2968581Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:34:59.3281120Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:34:59.4216275Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:34:59.7315656Z 2024-08-06T21:34:59.7316867Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_8f99dfeaa09d96ab_.log 2024-08-06T21:34:59.7317927Z 2024-08-06T21:34:59.7318239Z Running cpp/Dimname_test 1/1 ... [2024-08-06 21:34:59.731575] 2024-08-06T21:34:59.7323687Z 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-7f5a521a97b21722.xml', '-x', '--reruns=2'] ... [2024-08-06 21:34:59.732019] 2024-08-06T21:35:01.2991334Z 2024-08-06T21:35:01.2992245Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_c4f0ee99f3fa51e5_.log 2024-08-06T21:35:01.2992888Z 2024-08-06T21:35:01.2993124Z Running cpp/NamedTensor_test 1/1 ... [2024-08-06 21:35:01.298978] 2024-08-06T21:35:01.2995178Z 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-78c8585dea426095.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:01.299305] 2024-08-06T21:35:02.8163532Z 2024-08-06T21:35:02.8164521Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_dcc454fbe0477804_.log 2024-08-06T21:35:02.8165230Z 2024-08-06T21:35:02.8165444Z Running cpp/apply_utils_test 1/1 ... [2024-08-06 21:35:02.816219] 2024-08-06T21:35:02.8168074Z 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-c98a7b1f8c2b467f.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:02.816544] 2024-08-06T21:35:04.3334173Z 2024-08-06T21:35:04.3335626Z 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_8c56eea70f965e2d_.log 2024-08-06T21:35:04.3336772Z 2024-08-06T21:35:04.3337018Z Running cpp/atest 1/1 ... [2024-08-06 21:35:04.333258] 2024-08-06T21:35:04.3341966Z 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-4b0d31c60eb1064f.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:04.333848] 2024-08-06T21:35:05.8512033Z 2024-08-06T21:35:05.8512886Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_977a46d700aecd19_.log 2024-08-06T21:35:05.8513515Z 2024-08-06T21:35:05.8513684Z Running cpp/basic 1/1 ... [2024-08-06 21:35:05.851061] 2024-08-06T21:35:05.8516258Z 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-e3b65775f576e97b.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:05.851393] 2024-08-06T21:35:07.3685335Z 2024-08-06T21:35:07.3686347Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_53aecee2349ad7c5_.log 2024-08-06T21:35:07.3686924Z 2024-08-06T21:35:07.3687124Z Running cpp/broadcast_test 1/1 ... [2024-08-06 21:35:07.368424] 2024-08-06T21:35:07.3689723Z 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-c5e560f4730f3643.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:07.368741] 2024-08-06T21:35:08.8856305Z 2024-08-06T21:35:08.8858723Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_ccae9ba9578cbda9_.log 2024-08-06T21:35:08.8860017Z 2024-08-06T21:35:08.8860423Z Running cpp/cpu_generator_test 1/1 ... [2024-08-06 21:35:08.885506] 2024-08-06T21:35:08.8862562Z 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-49f0000d2419172c.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:08.885867] 2024-08-06T21:35:10.4032472Z 2024-08-06T21:35:10.4033845Z 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_6553cb788a8bda5a_.log 2024-08-06T21:35:10.4035196Z 2024-08-06T21:35:10.4035549Z Running cpp/dlconvertor_test 1/1 ... [2024-08-06 21:35:10.403110] 2024-08-06T21:35:10.4038141Z 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-ec15817df29817df.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:10.403507] 2024-08-06T21:35:11.9206059Z 2024-08-06T21:35:11.9207419Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_f878e4a9147f034d_.log 2024-08-06T21:35:11.9208146Z 2024-08-06T21:35:11.9208393Z Running cpp/extension_backend_test 1/1 ... [2024-08-06 21:35:11.920473] 2024-08-06T21:35:11.9211070Z 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-d468c42915e2f3b1.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:11.920820] 2024-08-06T21:35:13.4879357Z 2024-08-06T21:35:13.4881336Z 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_b4b299f8d0559118_.log 2024-08-06T21:35:13.4882314Z 2024-08-06T21:35:13.4882708Z Running cpp/lazy_tensor_test 1/1 ... [2024-08-06 21:35:13.487775] 2024-08-06T21:35:13.4884259Z 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-e9f47cad3fd7cee3.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:13.488093] 2024-08-06T21:35:15.0054856Z 2024-08-06T21:35:15.0056125Z 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_6e7d8c75392120e2_.log 2024-08-06T21:35:15.0056831Z 2024-08-06T21:35:15.0057058Z Running cpp/legacy_vmap_test 1/1 ... [2024-08-06 21:35:15.005340] 2024-08-06T21:35:15.0059483Z 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-50dc7bbe18b5c66e.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:15.005675] 2024-08-06T21:35:16.5228708Z 2024-08-06T21:35:16.5229759Z 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_f4183d3c0b5f8fe8_.log 2024-08-06T21:35:16.5230540Z 2024-08-06T21:35:16.5230730Z Running cpp/native_test 1/1 ... [2024-08-06 21:35:16.522570] 2024-08-06T21:35:16.5232155Z 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-7469301701ad57c5.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:16.522930] 2024-08-06T21:35:18.0401099Z 2024-08-06T21:35:18.0402067Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_4e1a58b57e342816_.log 2024-08-06T21:35:18.0403020Z 2024-08-06T21:35:18.0403234Z Running cpp/operators_test 1/1 ... [2024-08-06 21:35:18.039954] 2024-08-06T21:35:18.0405828Z 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-6521f455716e5e83.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:18.040305] 2024-08-06T21:35:19.5571490Z 2024-08-06T21:35:19.5572457Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_afc98b7030dc8a42_.log 2024-08-06T21:35:19.5573142Z 2024-08-06T21:35:19.5573360Z Running cpp/scalar_tensor_test 1/1 ... [2024-08-06 21:35:19.556996] 2024-08-06T21:35:19.5575253Z 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-e8df3943610d7f7a.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:19.557307] 2024-08-06T21:35:21.0744387Z 2024-08-06T21:35:21.0745841Z 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_e409fe064359b479_.log 2024-08-06T21:35:21.0746556Z 2024-08-06T21:35:21.0746815Z Running cpp/scalar_test 1/1 ... [2024-08-06 21:35:21.074327] 2024-08-06T21:35:21.0749521Z 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-6b4f46db7d1ba2ef.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:21.074696] 2024-08-06T21:35:22.5917739Z 2024-08-06T21:35:22.5918939Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_0238958a8655647d_.log 2024-08-06T21:35:22.5919593Z 2024-08-06T21:35:22.5919843Z Running cpp/tensor_iterator_test 1/1 ... [2024-08-06 21:35:22.591623] 2024-08-06T21:35:22.5922406Z 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-1ec1395d568d3354.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:22.591965] 2024-08-06T21:35:24.1087397Z 2024-08-06T21:35:24.1088761Z 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_8eeb806baa4c406b_.log 2024-08-06T21:35:24.1089478Z 2024-08-06T21:35:24.1089720Z Running cpp/undefined_tensor_test 1/1 ... [2024-08-06 21:35:24.108593] 2024-08-06T21:35:24.1091960Z 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-95852b2671d406cf.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:24.108920] 2024-08-06T21:35:25.6258196Z 2024-08-06T21:35:25.6259584Z 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_200e5a6d3392f5a0_.log 2024-08-06T21:35:25.6260472Z 2024-08-06T21:35:25.6260703Z Running cpp/wrapdim_test 1/1 ... [2024-08-06 21:35:25.625545] 2024-08-06T21:35:25.6262074Z 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-641e4c11c2ff4ec5.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:25.625875] 2024-08-06T21:35:27.1426580Z 2024-08-06T21:35:27.1427944Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_fe23e98007e59d9a_.log 2024-08-06T21:35:27.1428683Z 2024-08-06T21:35:27.1438619Z Running cpp/Dict_test 1/1 ... [2024-08-06 21:35:27.143599] 2024-08-06T21:35:27.1439656Z Running cpp/Dimname_test 1/1 ... [2024-08-06 21:35:27.143743] 2024-08-06T21:35:27.1442134Z Running cpp/NamedTensor_test 1/1 ... [2024-08-06 21:35:27.143882] 2024-08-06T21:35:27.1445928Z 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-770aa853e58a7fb1.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:27.144213] 2024-08-06T21:35:27.1449173Z 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-aabcf7720632383f.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:27.144400] 2024-08-06T21:35:27.1451746Z 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-c7ae2fff4cbd7d67.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:27.144509] 2024-08-06T21:35:31.0177143Z 2024-08-06T21:35:31.0178596Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_84deef9b439d6327_.log 2024-08-06T21:35:31.0179857Z 2024-08-06T21:35:32.0323231Z 2024-08-06T21:35:32.0324624Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_a434b98fb927ffaf_.log 2024-08-06T21:35:32.0325782Z 2024-08-06T21:35:34.0447366Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:34.1049454Z Running cpp/apply_utils_test 1/1 ... [2024-08-06 21:35:34.104514] 2024-08-06T21:35:34.1054446Z 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-b9bf1dec73d0ee97.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:34.105014] 2024-08-06T21:35:34.9276876Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:35.0351170Z Running cpp/atest 1/1 ... [2024-08-06 21:35:35.034443] 2024-08-06T21:35:35.0357152Z 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-2d2e2f44fae837e5.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:35.035049] 2024-08-06T21:35:37.7268132Z 2024-08-06T21:35:37.7269919Z 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_7b157c4efd158241_.log 2024-08-06T21:35:37.7276664Z 2024-08-06T21:35:38.2460538Z 2024-08-06T21:35:38.2462372Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_e2470f3548f0050e_.log 2024-08-06T21:35:38.2463813Z 2024-08-06T21:35:40.6111766Z 2024-08-06T21:35:40.6113125Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_608acb6cde8c0636_.log 2024-08-06T21:35:40.6114256Z 2024-08-06T21:35:40.7915486Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:40.8518924Z Running cpp/basic 1/1 ... [2024-08-06 21:35:40.851508] 2024-08-06T21:35:40.8523527Z 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-e508d9b5a04f5b6f.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:40.851974] 2024-08-06T21:35:41.1016023Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:41.1618312Z Running cpp/broadcast_test 1/1 ... [2024-08-06 21:35:41.161403] 2024-08-06T21:35:41.1622832Z 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-62305f8a712758e8.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:41.161889] 2024-08-06T21:35:43.5930801Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:43.6934384Z Running cpp/cpu_generator_test 1/1 ... [2024-08-06 21:35:43.693074] 2024-08-06T21:35:43.6938517Z 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-9041bcc037bb7b4b.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:43.693553] 2024-08-06T21:35:43.7890021Z 2024-08-06T21:35:43.7891500Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_5749bfe17a67a30e_.log 2024-08-06T21:35:43.7892661Z 2024-08-06T21:35:44.2728429Z 2024-08-06T21:35:44.2729698Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_5b5faa0944527836_.log 2024-08-06T21:35:44.2730789Z 2024-08-06T21:35:46.5470114Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:46.6074647Z Running cpp/dlconvertor_test 1/1 ... [2024-08-06 21:35:46.607028] 2024-08-06T21:35:46.6079713Z 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-b055a29e89591f36.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:46.607552] 2024-08-06T21:35:47.5677409Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:47.6748220Z Running cpp/extension_backend_test 1/1 ... [2024-08-06 21:35:47.674181] 2024-08-06T21:35:47.6755274Z 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-df1d46d7d60d9ec8.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:47.675023] 2024-08-06T21:35:48.4674168Z 2024-08-06T21:35:48.4675663Z 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_7b43cc6597759f16_.log 2024-08-06T21:35:48.4677030Z 2024-08-06T21:35:49.2274984Z 2024-08-06T21:35:49.2276466Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_0d66a438993c9bb7_.log 2024-08-06T21:35:49.2277719Z 2024-08-06T21:35:50.1953324Z 2024-08-06T21:35:50.1954550Z 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_5763a74396b37c07_.log 2024-08-06T21:35:51.2028612Z 2024-08-06T21:35:51.2029472Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:51.3118044Z Running cpp/lazy_tensor_test 1/1 ... [2024-08-06 21:35:51.311318] 2024-08-06T21:35:51.3121841Z 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-1d3e5d7a5ab1b742.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:51.311779] 2024-08-06T21:35:51.9041713Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:51.9643565Z Running cpp/legacy_vmap_test 1/1 ... [2024-08-06 21:35:51.963834] 2024-08-06T21:35:51.9648462Z 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-31384c18500150ab.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:51.964346] 2024-08-06T21:35:53.0836162Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:53.1457315Z Running cpp/native_test 1/1 ... [2024-08-06 21:35:53.145062] 2024-08-06T21:35:53.1461526Z 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-b24ea29943176081.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:53.145632] 2024-08-06T21:35:54.0318074Z 2024-08-06T21:35:54.0322087Z 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_3428dd4652983432_.log 2024-08-06T21:35:54.0327826Z 2024-08-06T21:35:55.9658545Z 2024-08-06T21:35:55.9660056Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_05ff40e72e250f1a_.log 2024-08-06T21:35:55.9661207Z 2024-08-06T21:35:56.9822326Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:57.0427131Z Running cpp/operators_test 1/1 ... [2024-08-06 21:35:57.042274] 2024-08-06T21:35:57.0431884Z 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-2e99dd67232c3d5a.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:57.042791] 2024-08-06T21:35:58.1062583Z 2024-08-06T21:35:58.1064003Z 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_7a777c67cf7c02c7_.log 2024-08-06T21:35:58.1064778Z 2024-08-06T21:35:59.0295154Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:35:59.1359867Z Running cpp/scalar_tensor_test 1/1 ... [2024-08-06 21:35:59.135423] 2024-08-06T21:35:59.1366350Z 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-e4e4dc3bdd4aa088.xml', '-x', '--reruns=2'] ... [2024-08-06 21:35:59.136035] 2024-08-06T21:36:00.1133006Z 2024-08-06T21:36:00.1134459Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_d24294aba52e4a7a_.log 2024-08-06T21:36:00.1135831Z 2024-08-06T21:36:00.8861309Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:36:00.9468035Z Running cpp/scalar_test 1/1 ... [2024-08-06 21:36:00.946377] 2024-08-06T21:36:00.9473115Z 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-d2bc3600d66f9b22.xml', '-x', '--reruns=2'] ... [2024-08-06 21:36:00.946891] 2024-08-06T21:36:01.7566537Z 2024-08-06T21:36:01.7568074Z 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_adf28f62cc6680e3_.log 2024-08-06T21:36:01.7569418Z 2024-08-06T21:36:02.9414095Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:36:03.0020948Z Running cpp/tensor_iterator_test 1/1 ... [2024-08-06 21:36:03.001625] 2024-08-06T21:36:03.0026673Z 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-5618030e9f4cd0dd.xml', '-x', '--reruns=2'] ... [2024-08-06 21:36:03.002076] 2024-08-06T21:36:03.7220957Z 2024-08-06T21:36:03.7222911Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_edaadddaf6328f51_.log 2024-08-06T21:36:03.7224194Z 2024-08-06T21:36:04.6682383Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:36:04.7283209Z Running cpp/undefined_tensor_test 1/1 ... [2024-08-06 21:36:04.727795] 2024-08-06T21:36:04.7288065Z 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-e70d55b955fe2773.xml', '-x', '--reruns=2'] ... [2024-08-06 21:36:04.728328] 2024-08-06T21:36:06.9324728Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:36:06.9928231Z Running cpp/wrapdim_test 1/1 ... [2024-08-06 21:36:06.992410] 2024-08-06T21:36:06.9933534Z 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-f62f2868ac08b861.xml', '-x', '--reruns=2'] ... [2024-08-06 21:36:06.992924] 2024-08-06T21:36:07.7492400Z 2024-08-06T21:36:07.7493976Z 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_d4fdc7494e942096_.log 2024-08-06T21:36:07.7495417Z 2024-08-06T21:36:09.5643134Z 2024-08-06T21:36:09.5644969Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_8c972c47e1abe097_.log 2024-08-06T21:36:09.5646397Z 2024-08-06T21:36:10.8433570Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:36:12.3053425Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-06T21:36:14.6912350Z 2024-08-06T21:36:14.6914093Z 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_c24dc9f7735eaae0_.log 2024-08-06T21:36:14.6914948Z 2024-08-06T21:36:15.6405355Z + run_if_exists tensor_interop_test 2024-08-06T21:36:15.6406056Z + local test_name=tensor_interop_test 2024-08-06T21:36:15.6407476Z + [[ -x build/bin/tensor_interop_test ]] 2024-08-06T21:36:15.6408224Z + echo 'Warning: tensor_interop_test does not exist.' 2024-08-06T21:36:15.6408917Z Warning: tensor_interop_test does not exist. 2024-08-06T21:36:15.6409552Z + run_if_exists cudnn_test 2024-08-06T21:36:15.6410025Z + local test_name=cudnn_test 2024-08-06T21:36:15.6410534Z + [[ -x build/bin/cudnn_test ]] 2024-08-06T21:36:15.6411106Z + echo 'Warning: cudnn_test does not exist.' 2024-08-06T21:36:15.6411727Z Warning: cudnn_test does not exist. 2024-08-06T21:36:15.6412267Z + run_if_exists cuda_generator_test 2024-08-06T21:36:15.6412846Z + local test_name=cuda_generator_test 2024-08-06T21:36:15.6413453Z + [[ -x build/bin/cuda_generator_test ]] 2024-08-06T21:36:15.6414129Z + echo 'Warning: cuda_generator_test does not exist.' 2024-08-06T21:36:15.6414764Z Warning: cuda_generator_test does not exist. 2024-08-06T21:36:15.6415115Z + run_if_exists apply_test 2024-08-06T21:36:15.6415393Z + local test_name=apply_test 2024-08-06T21:36:15.6415690Z + [[ -x build/bin/apply_test ]] 2024-08-06T21:36:15.6416009Z + echo 'Warning: apply_test does not exist.' 2024-08-06T21:36:15.6416346Z Warning: apply_test does not exist. 2024-08-06T21:36:15.6416663Z + run_if_exists stream_test 2024-08-06T21:36:15.6416950Z + local test_name=stream_test 2024-08-06T21:36:15.6417230Z + [[ -x build/bin/stream_test ]] 2024-08-06T21:36:15.6417552Z + echo 'Warning: stream_test does not exist.' 2024-08-06T21:36:15.6417905Z Warning: stream_test does not exist. 2024-08-06T21:36:15.6418215Z + run_if_exists cuda_half_test 2024-08-06T21:36:15.6418757Z + local test_name=cuda_half_test 2024-08-06T21:36:15.6419065Z + [[ -x build/bin/cuda_half_test ]] 2024-08-06T21:36:15.6419403Z + echo 'Warning: cuda_half_test does not exist.' 2024-08-06T21:36:15.6419770Z Warning: cuda_half_test does not exist. 2024-08-06T21:36:15.6420195Z + run_if_exists cuda_vectorized_test 2024-08-06T21:36:15.6420519Z + local test_name=cuda_vectorized_test 2024-08-06T21:36:15.6420862Z + [[ -x build/bin/cuda_vectorized_test ]] 2024-08-06T21:36:15.6421250Z + echo 'Warning: cuda_vectorized_test does not exist.' 2024-08-06T21:36:15.6421648Z Warning: cuda_vectorized_test does not exist. 2024-08-06T21:36:15.6422018Z + run_if_exists cuda_distributions_test 2024-08-06T21:36:15.6422439Z + local test_name=cuda_distributions_test 2024-08-06T21:36:15.6422785Z + [[ -x build/bin/cuda_distributions_test ]] 2024-08-06T21:36:15.6423261Z + echo 'Warning: cuda_distributions_test does not exist.' 2024-08-06T21:36:15.6423698Z Warning: cuda_distributions_test does not exist. 2024-08-06T21:36:15.6424068Z + run_if_exists cuda_optional_test 2024-08-06T21:36:15.6424399Z + local test_name=cuda_optional_test 2024-08-06T21:36:15.6424737Z + [[ -x build/bin/cuda_optional_test ]] 2024-08-06T21:36:15.6425100Z + echo 'Warning: cuda_optional_test does not exist.' 2024-08-06T21:36:15.6425501Z Warning: cuda_optional_test does not exist. 2024-08-06T21:36:15.6425863Z + run_if_exists cuda_tensor_interop_test 2024-08-06T21:36:15.6426208Z + local test_name=cuda_tensor_interop_test 2024-08-06T21:36:15.6426919Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2024-08-06T21:36:15.6427342Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2024-08-06T21:36:15.6427768Z Warning: cuda_tensor_interop_test does not exist. 2024-08-06T21:36:15.6428142Z + run_if_exists cuda_complex_test 2024-08-06T21:36:15.6428460Z + local test_name=cuda_complex_test 2024-08-06T21:36:15.6428774Z + [[ -x build/bin/cuda_complex_test ]] 2024-08-06T21:36:15.6429134Z + echo 'Warning: cuda_complex_test does not exist.' 2024-08-06T21:36:15.6429518Z Warning: cuda_complex_test does not exist. 2024-08-06T21:36:15.6429857Z + run_if_exists cuda_complex_math_test 2024-08-06T21:36:15.6430269Z + local test_name=cuda_complex_math_test 2024-08-06T21:36:15.6430618Z + [[ -x build/bin/cuda_complex_math_test ]] 2024-08-06T21:36:15.6431002Z + echo 'Warning: cuda_complex_math_test does not exist.' 2024-08-06T21:36:15.6431419Z Warning: cuda_complex_math_test does not exist. 2024-08-06T21:36:15.6431803Z + run_if_exists cuda_cub_test 2024-08-06T21:36:15.6432119Z + local test_name=cuda_cub_test 2024-08-06T21:36:15.6432419Z + [[ -x build/bin/cuda_cub_test ]] 2024-08-06T21:36:15.6432756Z + echo 'Warning: cuda_cub_test does not exist.' 2024-08-06T21:36:15.6433102Z Warning: cuda_cub_test does not exist. 2024-08-06T21:36:15.6433428Z + run_if_exists cuda_atomic_ops_test 2024-08-06T21:36:15.6433750Z + local test_name=cuda_atomic_ops_test 2024-08-06T21:36:15.6434074Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2024-08-06T21:36:15.6434452Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2024-08-06T21:36:15.6434869Z Warning: cuda_atomic_ops_test does not exist. 2024-08-06T21:36:15.6435192Z + '[' ON == ON ']' 2024-08-06T21:36:15.6435911Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2024-08-06T21:36:15.6709709Z ==5303== Memcheck, a memory error detector 2024-08-06T21:36:15.6710367Z ==5303== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2024-08-06T21:36:15.6710944Z ==5303== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2024-08-06T21:36:15.6711449Z ==5303== Command: build/bin/basic --gtest_filter=-*CUDA 2024-08-06T21:36:15.6711798Z ==5303== 2024-08-06T21:36:44.2910153Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2024-08-06T21:36:44.3127317Z Note: Google Test filter = -*CUDA 2024-08-06T21:36:44.3179589Z [==========] Running 4 tests from 1 test suite. 2024-08-06T21:36:44.3205058Z [----------] Global test environment set-up. 2024-08-06T21:36:44.3244003Z [----------] 4 tests from BasicTest 2024-08-06T21:36:44.3265167Z [ RUN ] BasicTest.BasicTestCPU 2024-08-06T21:36:45.7113733Z 360 ms 2024-08-06T21:36:45.7939891Z 52 ms 2024-08-06T21:36:45.8677689Z 65 ms 2024-08-06T21:36:46.4951144Z [ OK ] BasicTest.BasicTestCPU (2165 ms) 2024-08-06T21:36:46.5156363Z [ RUN ] BasicTest.BasicTestHalfCPU 2024-08-06T21:36:46.6578288Z 97 ms 2024-08-06T21:36:46.7071661Z 44 ms 2024-08-06T21:36:46.7736461Z 64 ms 2024-08-06T21:36:46.8278975Z [ OK ] BasicTest.BasicTestHalfCPU (311 ms) 2024-08-06T21:36:46.8281584Z [ RUN ] BasicTest.FactoryMethodsTest 2024-08-06T21:36:46.8651410Z [ OK ] BasicTest.FactoryMethodsTest (36 ms) 2024-08-06T21:36:46.8652172Z [ RUN ] BasicTest.BasicStdTestCPU 2024-08-06T21:36:46.9077626Z Simple example: called once 2024-08-06T21:36:47.0410410Z throw: call_once will retry 2024-08-06T21:36:47.0833877Z throw: call_once will retry 2024-08-06T21:36:47.0838744Z Didn't throw, call_once will not attempt again 2024-08-06T21:36:47.0859594Z [ OK ] BasicTest.BasicStdTestCPU (220 ms) 2024-08-06T21:36:47.0882157Z [----------] 4 tests from BasicTest (2760 ms total) 2024-08-06T21:36:47.0882569Z 2024-08-06T21:36:47.0892917Z [----------] Global test environment tear-down 2024-08-06T21:36:47.0924646Z [==========] 4 tests from 1 test suite ran. (2781 ms total) 2024-08-06T21:36:47.0935307Z [ PASSED ] 4 tests. 2024-08-06T21:36:48.8742650Z ==5303== 2024-08-06T21:36:48.8746058Z ==5303== HEAP SUMMARY: 2024-08-06T21:36:48.8746739Z ==5303== in use at exit: 239,952 bytes in 3,997 blocks 2024-08-06T21:36:48.8747253Z ==5303== total heap usage: 731,148 allocs, 727,151 frees, 213,120,700 bytes allocated 2024-08-06T21:36:48.8747722Z ==5303== 2024-08-06T21:36:48.9118912Z ==5303== LEAK SUMMARY: 2024-08-06T21:36:48.9119388Z ==5303== definitely lost: 0 bytes in 0 blocks 2024-08-06T21:36:48.9119777Z ==5303== indirectly lost: 0 bytes in 0 blocks 2024-08-06T21:36:48.9120699Z ==5303== possibly lost: 0 bytes in 0 blocks 2024-08-06T21:36:48.9121322Z ==5303== still reachable: 239,952 bytes in 3,997 blocks 2024-08-06T21:36:48.9121716Z ==5303== suppressed: 0 bytes in 0 blocks 2024-08-06T21:36:48.9122163Z ==5303== Rerun with --leak-check=full to see details of leaked memory 2024-08-06T21:36:48.9122570Z ==5303== 2024-08-06T21:36:48.9122885Z ==5303== For lists of detected and suppressed errors, rerun with: -s 2024-08-06T21:36:48.9123412Z ==5303== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2024-08-06T21:36:48.9585567Z + [[ -x build/bin/tensor_interop_test ]] 2024-08-06T21:36:48.9586401Z + [[ -n '' ]] 2024-08-06T21:36:48.9586820Z + assert_git_not_dirty 2024-08-06T21:36:48.9587449Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *rocm* ]] 2024-08-06T21:36:48.9588301Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *xla* ]] 2024-08-06T21:36:48.9593682Z ++ git status --porcelain 2024-08-06T21:36:48.9594825Z ++ grep -v '?? third_party' 2024-08-06T21:36:49.1432898Z ++ true 2024-08-06T21:36:49.1433532Z + git_status= 2024-08-06T21:36:49.1433912Z + [[ -n '' ]] 2024-08-06T21:36:49.1434864Z + cleanup_workspace 2024-08-06T21:36:49.1436122Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-08-06T21:36:49.1436980Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-08-06T21:36:49.1437619Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-08-06T21:36:49.1438100Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-08-06T21:36:49.1438650Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-08-06T21:36:49.1439269Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-08-06T21:36:49.1439764Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-08-06T21:36:51.5910344Z ##[group]Run cat test/**/*_toprint.log || true 2024-08-06T21:36:51.5910758Z cat test/**/*_toprint.log || true 2024-08-06T21:36:51.5952429Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:36:51.5952836Z env: 2024-08-06T21:36:51.5953051Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:51.5953526Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:51.5954032Z ##[endgroup] 2024-08-06T21:36:51.6031798Z cat: 'test/**/*_toprint.log': No such file or directory 2024-08-06T21:36:51.6076757Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-08-06T21:36:51.6077125Z kill "$MONITOR_SCRIPT_PID" 2024-08-06T21:36:51.6082951Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:36:51.6083433Z env: 2024-08-06T21:36:51.6083644Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:51.6084113Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:51.6084645Z MONITOR_SCRIPT_PID: 273578 2024-08-06T21:36:51.6084913Z ##[endgroup] 2024-08-06T21:36:51.6230729Z Prepare all required actions 2024-08-06T21:36:51.6231170Z Getting action download info 2024-08-06T21:36:51.8168157Z Download action repository 'actions/upload-artifact@v3' (SHA:a8a3f3ad30e3422c9c7b888a15615d19a852ae32) 2024-08-06T21:36:52.0117262Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-08-06T21:36:52.0117643Z with: 2024-08-06T21:36:52.0117969Z file-suffix: test-dynamo-1-3-amz2023.linux.2xlarge_28427567353 2024-08-06T21:36:52.0118398Z s3-bucket: gha-artifacts 2024-08-06T21:36:52.0118673Z env: 2024-08-06T21:36:52.0118886Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:52.0119356Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:52.0119872Z ##[endgroup] 2024-08-06T21:36:52.0152646Z ##[group]Run # Remove any previous test jsons if they exist 2024-08-06T21:36:52.0153488Z # Remove any previous test jsons if they exist 2024-08-06T21:36:52.0153881Z rm -f test-jsons-*.zip 2024-08-06T21:36:52.0154339Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json' 2024-08-06T21:36:52.0160006Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:36:52.0160407Z env: 2024-08-06T21:36:52.0160640Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:52.0161098Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:52.0161712Z FILE_SUFFIX: test-dynamo-1-3-amz2023.linux.2xlarge_28427567353 2024-08-06T21:36:52.0162118Z ##[endgroup] 2024-08-06T21:36:52.0474829Z adding: test/allowlist_for_publicAPI.json (deflated 79%) 2024-08-06T21:36:52.0502577Z adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%) 2024-08-06T21:36:52.0503483Z adding: test/minioptest_failures_dict.json (deflated 70%) 2024-08-06T21:36:52.0509696Z adding: test/profiler/profiler_utils_mock_events.json (deflated 87%) 2024-08-06T21:36:52.0516620Z adding: test/test-reports/td_exclusions-b41aa9e2815d32d7b464.json (deflated 81%) 2024-08-06T21:36:52.0517874Z adding: test/test-reports/td_exclusions-bc0c4e95889b73271976.json (deflated 73%) 2024-08-06T21:36:52.0518807Z adding: test/.pytorch-slow-tests.json (deflated 66%) 2024-08-06T21:36:52.0530862Z adding: test/.pytorch-disabled-tests.json (deflated 89%) 2024-08-06T21:36:52.0570299Z ##[group]Run # Remove any previous test reports if they exist 2024-08-06T21:36:52.0570779Z # Remove any previous test reports if they exist 2024-08-06T21:36:52.0571177Z rm -f test-reports-*.zip 2024-08-06T21:36:52.0571620Z zip -r "test-reports-${FILE_SUFFIX}.zip" test -i '*.xml' -i '*.csv' 2024-08-06T21:36:52.0577328Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:36:52.0577717Z env: 2024-08-06T21:36:52.0577950Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:52.0578414Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:52.0579119Z FILE_SUFFIX: test-dynamo-1-3-amz2023.linux.2xlarge_28427567353 2024-08-06T21:36:52.0579543Z ##[endgroup] 2024-08-06T21:36:52.0800549Z adding: test/test-reports/python-pytest/test_native_mha/test_native_mha-47eb619e42cfa00b.xml (deflated 95%) 2024-08-06T21:36:52.0834184Z adding: test/test-reports/python-pytest/test_nn/test_nn-9437183d6f26e4e1.xml (deflated 96%) 2024-08-06T21:36:52.0893225Z adding: test/test-reports/python-pytest/test_cpp_api_parity/test_cpp_api_parity-a1764f792c7e2ece.xml (deflated 99%) 2024-08-06T21:36:52.0926433Z adding: test/test-reports/python-pytest/test_torch/test_torch-6a01b809d97869b2.xml (deflated 95%) 2024-08-06T21:36:52.0927330Z adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-122d7d8f5d3f9173.xml (deflated 57%) 2024-08-06T21:36:52.0928270Z adding: test/test-reports/python-pytest/test_tensorexpr/test_tensorexpr-29373f7af32057c0.xml (deflated 95%) 2024-08-06T21:36:52.0929369Z adding: test/test-reports/python-pytest/test_autocast/test_autocast-99aa0dd7663a4fc4.xml (deflated 83%) 2024-08-06T21:36:52.0932158Z adding: test/test-reports/python-pytest/test_cpp_extensions_jit/test_cpp_extensions_jit-945a83a744d6f066.xml (deflated 88%) 2024-08-06T21:36:52.0934892Z adding: test/test-reports/python-pytest/test_sort_and_select/test_sort_and_select-6050f6ad466985b4.xml (deflated 92%) 2024-08-06T21:36:52.1034985Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-2c356e2bc12006d2.xml (deflated 98%) 2024-08-06T21:36:52.1052887Z adding: test/test-reports/python-pytest/nn.test_convolution/nn.test_convolution-7f8457c24530e4f2.xml (deflated 97%) 2024-08-06T21:36:52.1055403Z adding: test/test-reports/python-pytest/nn.test_pooling/nn.test_pooling-f3511f9cd1d488ae.xml (deflated 89%) 2024-08-06T21:36:52.1057659Z adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-8faeddf26a178025.xml (deflated 89%) 2024-08-06T21:36:52.1059342Z adding: test/test-reports/python-pytest/test_mobile_optimizer/test_mobile_optimizer-bed451d8136a685b.xml (deflated 58%) 2024-08-06T21:36:52.1060442Z adding: test/test-reports/python-pytest/test_multiprocessing_spawn/test_multiprocessing_spawn-223d9416c8f49032.xml (deflated 78%) 2024-08-06T21:36:52.1066687Z adding: test/test-reports/python-pytest/test_spectral_ops/test_spectral_ops-af8256fea27f3bac.xml (deflated 95%) 2024-08-06T21:36:52.1069833Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-fde21f655c8297f4.xml (deflated 94%) 2024-08-06T21:36:52.1088870Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-e49cdd2609eb3fd6.xml (deflated 93%) 2024-08-06T21:36:52.1090753Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_no_ninja/test_cpp_extensions_aot_no_ninja-ae58978aa6d2cd50.xml (deflated 85%) 2024-08-06T21:36:52.1091950Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_ninja/test_cpp_extensions_aot_ninja-749b0461fdfab2c5.xml (deflated 85%) 2024-08-06T21:36:52.1093002Z adding: test/test-reports/python-pytest/test_matmul_cuda/test_matmul_cuda-aba0f019aaa6f7f9.xml (deflated 28%) 2024-08-06T21:36:52.1093938Z adding: test/test-reports/python-pytest/test_matmul_cuda/test_matmul_cuda-28bfbba0faa4de51.xml (deflated 27%) 2024-08-06T21:36:52.1094879Z adding: test/test-reports/python-pytest/test_cuda_sanitizer/test_cuda_sanitizer-a834f3327f8678c4.xml (deflated 28%) 2024-08-06T21:36:52.1095851Z adding: test/test-reports/python-pytest/test_cuda_sanitizer/test_cuda_sanitizer-e561c3450892cc28.xml (deflated 27%) 2024-08-06T21:36:52.1096742Z adding: test/test-reports/python-pytest/test_cuda/test_cuda-1ed526f7cad30987.xml (deflated 27%) 2024-08-06T21:36:52.1097527Z adding: test/test-reports/python-pytest/test_cuda/test_cuda-b53881d582f823ea.xml (deflated 28%) 2024-08-06T21:36:52.1098415Z adding: test/test-reports/python-pytest/test_cuda_multigpu/test_cuda_multigpu-42e5c248ae5e7566.xml (deflated 28%) 2024-08-06T21:36:52.1099764Z adding: test/test-reports/python-pytest/test_cuda_multigpu/test_cuda_multigpu-f897b8c3041ea3bd.xml (deflated 27%) 2024-08-06T21:36:52.1100795Z adding: test/test-reports/python-pytest/test_quantization/test_quantization-8c0eda4fcb5db782.xml (deflated 28%) 2024-08-06T21:36:52.1103126Z adding: test/test-reports/python-pytest/test_quantization/test_quantization-1ac1c5f4cac8b45d.xml (deflated 95%) 2024-08-06T21:36:52.1104842Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-06430c59b411466f.xml (deflated 29%) 2024-08-06T21:36:52.1106430Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-7f5a521a97b21722.xml (deflated 29%) 2024-08-06T21:36:52.1108078Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-78c8585dea426095.xml (deflated 29%) 2024-08-06T21:36:52.1109717Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-c98a7b1f8c2b467f.xml (deflated 29%) 2024-08-06T21:36:52.1111280Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-4b0d31c60eb1064f.xml (deflated 29%) 2024-08-06T21:36:52.1112767Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e3b65775f576e97b.xml (deflated 29%) 2024-08-06T21:36:52.1114403Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-c5e560f4730f3643.xml (deflated 29%) 2024-08-06T21:36:52.1115905Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-49f0000d2419172c.xml (deflated 29%) 2024-08-06T21:36:52.1117085Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-ec15817df29817df.xml (deflated 28%) 2024-08-06T21:36:52.1118626Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-d468c42915e2f3b1.xml (deflated 29%) 2024-08-06T21:36:52.1120244Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e9f47cad3fd7cee3.xml (deflated 29%) 2024-08-06T21:36:52.1121636Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-50dc7bbe18b5c66e.xml (deflated 28%) 2024-08-06T21:36:52.1122706Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-7469301701ad57c5.xml (deflated 28%) 2024-08-06T21:36:52.1123728Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-6521f455716e5e83.xml (deflated 29%) 2024-08-06T21:36:52.1124622Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e8df3943610d7f7a.xml (deflated 29%) 2024-08-06T21:36:52.1125475Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-6b4f46db7d1ba2ef.xml (deflated 29%) 2024-08-06T21:36:52.1126345Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-1ec1395d568d3354.xml (deflated 28%) 2024-08-06T21:36:52.1127209Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-95852b2671d406cf.xml (deflated 29%) 2024-08-06T21:36:52.1128073Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-641e4c11c2ff4ec5.xml (deflated 29%) 2024-08-06T21:36:52.1128931Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-aabcf7720632383f.xml (deflated 57%) 2024-08-06T21:36:52.1129812Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-c7ae2fff4cbd7d67.xml (deflated 72%) 2024-08-06T21:36:52.1130686Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-b9bf1dec73d0ee97.xml (deflated 67%) 2024-08-06T21:36:52.1131545Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-770aa853e58a7fb1.xml (deflated 83%) 2024-08-06T21:36:52.1132412Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-2d2e2f44fae837e5.xml (deflated 79%) 2024-08-06T21:36:52.1133313Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-62305f8a712758e8.xml (deflated 37%) 2024-08-06T21:36:52.1134177Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e508d9b5a04f5b6f.xml (deflated 61%) 2024-08-06T21:36:52.1135034Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-9041bcc037bb7b4b.xml (deflated 80%) 2024-08-06T21:36:52.1135897Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-b055a29e89591f36.xml (deflated 50%) 2024-08-06T21:36:52.1136856Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-df1d46d7d60d9ec8.xml (deflated 35%) 2024-08-06T21:36:52.1137710Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-1d3e5d7a5ab1b742.xml (deflated 46%) 2024-08-06T21:36:52.1138581Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-b24ea29943176081.xml (deflated 47%) 2024-08-06T21:36:52.1139456Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-31384c18500150ab.xml (deflated 83%) 2024-08-06T21:36:52.1140335Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-2e99dd67232c3d5a.xml (deflated 58%) 2024-08-06T21:36:52.1141191Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e4e4dc3bdd4aa088.xml (deflated 58%) 2024-08-06T21:36:52.1142106Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-d2bc3600d66f9b22.xml (deflated 59%) 2024-08-06T21:36:52.1143639Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e70d55b955fe2773.xml (deflated 37%) 2024-08-06T21:36:52.1144510Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-f62f2868ac08b861.xml (deflated 37%) 2024-08-06T21:36:52.1145383Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-5618030e9f4cd0dd.xml (deflated 90%) 2024-08-06T21:36:52.1146360Z adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20240806212437.xml (deflated 42%) 2024-08-06T21:36:52.1147520Z adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20240806212448.xml (deflated 43%) 2024-08-06T21:36:52.1177204Z ##[group]Run # Remove any previous usage logs if they exist 2024-08-06T21:36:52.1177690Z # Remove any previous usage logs if they exist 2024-08-06T21:36:52.1178088Z rm -f logs-*.zip 2024-08-06T21:36:52.1178559Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2024-08-06T21:36:52.1179142Z # so check to see if the file exists first 2024-08-06T21:36:52.1179511Z if [ -f 'usage_log.txt' ]; then 2024-08-06T21:36:52.1179877Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2024-08-06T21:36:52.1180231Z fi 2024-08-06T21:36:52.1180499Z if ls test/**/*.log 1> /dev/null 2>&1; then 2024-08-06T21:36:52.1180896Z  zip -r "logs-${FILE_SUFFIX}.zip" test -i '*.log' 2024-08-06T21:36:52.1181255Z fi 2024-08-06T21:36:52.1186868Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:36:52.1187248Z env: 2024-08-06T21:36:52.1187481Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:52.1187955Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:52.1188560Z FILE_SUFFIX: test-dynamo-1-3-amz2023.linux.2xlarge_28427567353 2024-08-06T21:36:52.1188967Z ##[endgroup] 2024-08-06T21:36:52.1265311Z adding: usage_log.txt (deflated 92%) 2024-08-06T21:36:52.1513494Z adding: test/test-reports/test_native_mha_1.1_6db589fc60910015_.log (deflated 90%) 2024-08-06T21:36:52.1546246Z adding: test/test-reports/test_nn_2.2_88d5c9391779ebb5_.log (deflated 94%) 2024-08-06T21:36:52.1558206Z adding: test/test-reports/test_cpp_api_parity_1.1_12e804e602416c72_.log (deflated 95%) 2024-08-06T21:36:52.1596680Z adding: test/test-reports/test_torch_1.1_7c04fa92e64f1aa5_.log (deflated 92%) 2024-08-06T21:36:52.1597372Z adding: test/test-reports/test_jit_disabled_1.1_a5038321842e9a4f_.log (deflated 58%) 2024-08-06T21:36:52.1599192Z adding: test/test-reports/test_tensorexpr_1.1_83e9bb815d747091_.log (deflated 90%) 2024-08-06T21:36:52.1599930Z adding: test/test-reports/test_autocast_1.1_a837ce26ca83adea_.log (deflated 76%) 2024-08-06T21:36:52.1602471Z adding: test/test-reports/test_cpp_extensions_jit_1.1_b1456a04f369c744_.log (deflated 88%) 2024-08-06T21:36:52.1609328Z adding: test/test-reports/test_sort_and_select_1.1_609e5da0417ae5f0_.log (deflated 93%) 2024-08-06T21:36:52.1701740Z adding: test/test-reports/test_reductions_1.1_56806ce5cac37920_.log (deflated 96%) 2024-08-06T21:36:52.1702460Z adding: test/test-reports/test_cuda_primary_ctx_1.1_fafd7dda94f022bb_.log (deflated 16%) 2024-08-06T21:36:52.1703192Z adding: test/test-reports/test_cuda_nvml_based_avail_1.1_8312b71bc207f43c_.log (deflated 16%) 2024-08-06T21:36:52.1717402Z adding: test/test-reports/nn.test_convolution_1.1_9939d28985a36ef3_.log (deflated 95%) 2024-08-06T21:36:52.1721915Z adding: test/test-reports/nn.test_pooling_1.1_84afd7a5eb718186_.log (deflated 88%) 2024-08-06T21:36:52.1723595Z adding: test/test-reports/test_multiprocessing_1.1_3beffe099fce4971_.log (deflated 84%) 2024-08-06T21:36:52.1724427Z adding: test/test-reports/test_mobile_optimizer_1.1_82d26e45d77aa22d_.log (deflated 68%) 2024-08-06T21:36:52.1726037Z adding: 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(stored 0%) 2024-08-06T21:36:52.1765182Z adding: test/test-reports/dynamo.test_frame_init_1.1_12566f0e7b4f790e_.log (stored 0%) 2024-08-06T21:36:52.1765903Z adding: test/test-reports/dynamo.test_dynamic_shapes_1.1_1a0e8b50b7d60caa_.log (stored 0%) 2024-08-06T21:36:52.1766687Z adding: test/test-reports/dynamo.test_global_1.1_85a0ef1513c7e4c8_.log (stored 0%) 2024-08-06T21:36:52.1767366Z adding: test/test-reports/test_matmul_cuda_1.1_d3335804ae7bc03a_.log (deflated 49%) 2024-08-06T21:36:52.1768064Z adding: test/test-reports/dynamo.test_exceptions_1.1_4dd5ff5c5ab27f08_.log (stored 0%) 2024-08-06T21:36:52.1768758Z adding: test/test-reports/dynamo.test_subgraphs_1.1_2facd8fb431468f5_.log (stored 0%) 2024-08-06T21:36:52.1769447Z adding: test/test-reports/dynamo.test_modes_1.1_5e12ab54c88af52f_.log (stored 0%) 2024-08-06T21:36:52.1770160Z adding: test/test-reports/dynamo.test_higher_order_ops_1.1_b7c6c231930d8140_.log (stored 0%) 2024-08-06T21:36:52.1770866Z adding: 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2024-08-06T21:36:52.1803582Z adding: test/test-reports/cpp.basic_1.1_5b5faa0944527836_.log (deflated 62%) 2024-08-06T21:36:52.1804240Z adding: test/test-reports/cpp.cpu_generator_test_1.1_7b43cc6597759f16_.log (deflated 79%) 2024-08-06T21:36:52.1804964Z adding: test/test-reports/cpp.dlconvertor_test_1.1_0d66a438993c9bb7_.log (deflated 56%) 2024-08-06T21:36:52.1805706Z adding: test/test-reports/cpp.extension_backend_test_1.1_5763a74396b37c07_.log (deflated 50%) 2024-08-06T21:36:52.1806421Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_3428dd4652983432_.log (deflated 55%) 2024-08-06T21:36:52.1807100Z adding: test/test-reports/cpp.native_test_1.1_05ff40e72e250f1a_.log (deflated 54%) 2024-08-06T21:36:52.1807783Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_7a777c67cf7c02c7_.log (deflated 81%) 2024-08-06T21:36:52.1808482Z adding: test/test-reports/cpp.operators_test_1.1_d24294aba52e4a7a_.log (deflated 61%) 2024-08-06T21:36:52.1809184Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_adf28f62cc6680e3_.log (deflated 61%) 2024-08-06T21:36:52.1809887Z adding: test/test-reports/cpp.scalar_test_1.1_edaadddaf6328f51_.log (deflated 60%) 2024-08-06T21:36:52.1810603Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_d4fdc7494e942096_.log (deflated 50%) 2024-08-06T21:36:52.1811307Z adding: test/test-reports/cpp.wrapdim_test_1.1_8c972c47e1abe097_.log (deflated 50%) 2024-08-06T21:36:52.1812076Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_c24dc9f7735eaae0_.log (deflated 89%) 2024-08-06T21:36:52.1841721Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-08-06T21:36:52.1842272Z # Remove any previous debugging artifacts if they exist 2024-08-06T21:36:52.1842859Z rm -f debug-*.zip 2024-08-06T21:36:52.1843156Z if [ -d 'test/debug' ]; then 2024-08-06T21:36:52.1843527Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-08-06T21:36:52.1843869Z fi 2024-08-06T21:36:52.1849442Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:36:52.1849839Z env: 2024-08-06T21:36:52.1850057Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:52.1850559Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:52.1851261Z FILE_SUFFIX: test-dynamo-1-3-amz2023.linux.2xlarge_28427567353 2024-08-06T21:36:52.1851663Z ##[endgroup] 2024-08-06T21:36:52.1935432Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-06T21:36:52.1935784Z with: 2024-08-06T21:36:52.1936031Z s3-bucket: gha-artifacts 2024-08-06T21:36:52.1936371Z s3-prefix: pytorch/pytorch/10273124344/1/artifact 2024-08-06T21:36:52.1936732Z retention-days: 14 2024-08-06T21:36:52.1937010Z if-no-files-found: warn 2024-08-06T21:36:52.1937299Z path: test-jsons-*.zip 2024-08-06T21:36:52.1937563Z name: artifact 2024-08-06T21:36:52.1937821Z region: us-east-1 2024-08-06T21:36:52.1938068Z env: 2024-08-06T21:36:52.1938283Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:52.1938767Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:52.1939290Z ##[endgroup] 2024-08-06T21:36:52.5675319Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-06T21:36:52.5675899Z With the provided path, there will be 1 file uploaded 2024-08-06T21:36:52.5676394Z Uploading to s3 prefix: pytorch/pytorch/10273124344/1/artifact 2024-08-06T21:36:52.5833606Z Starting upload of test-jsons-test-dynamo-1-3-amz2023.linux.2xlarge_28427567353.zip 2024-08-06T21:36:52.7086634Z Finished upload of test-jsons-test-dynamo-1-3-amz2023.linux.2xlarge_28427567353.zip 2024-08-06T21:36:52.7278395Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-06T21:36:52.7278758Z with: 2024-08-06T21:36:52.7278982Z s3-bucket: gha-artifacts 2024-08-06T21:36:52.7279320Z s3-prefix: pytorch/pytorch/10273124344/1/artifact 2024-08-06T21:36:52.7279685Z retention-days: 14 2024-08-06T21:36:52.7279939Z if-no-files-found: error 2024-08-06T21:36:52.7280365Z path: test-reports-*.zip 2024-08-06T21:36:52.7280643Z name: artifact 2024-08-06T21:36:52.7280872Z region: us-east-1 2024-08-06T21:36:52.7281112Z env: 2024-08-06T21:36:52.7281333Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:52.7281812Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:52.7282324Z ##[endgroup] 2024-08-06T21:36:53.0765322Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-06T21:36:53.0765839Z With the provided path, there will be 1 file uploaded 2024-08-06T21:36:53.0766308Z Uploading to s3 prefix: pytorch/pytorch/10273124344/1/artifact 2024-08-06T21:36:53.0805487Z Starting upload of test-reports-test-dynamo-1-3-amz2023.linux.2xlarge_28427567353.zip 2024-08-06T21:36:53.3590402Z Finished upload of test-reports-test-dynamo-1-3-amz2023.linux.2xlarge_28427567353.zip 2024-08-06T21:36:53.3781260Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-06T21:36:53.3781625Z with: 2024-08-06T21:36:53.3781845Z s3-bucket: gha-artifacts 2024-08-06T21:36:53.3782187Z s3-prefix: pytorch/pytorch/10273124344/1/artifact 2024-08-06T21:36:53.3782564Z retention-days: 14 2024-08-06T21:36:53.3782825Z if-no-files-found: ignore 2024-08-06T21:36:53.3783122Z path: logs-*.zip 2024-08-06T21:36:53.3783364Z name: artifact 2024-08-06T21:36:53.3783589Z region: us-east-1 2024-08-06T21:36:53.3783828Z env: 2024-08-06T21:36:53.3784156Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:53.3784637Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:53.3785152Z ##[endgroup] 2024-08-06T21:36:53.7266316Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-06T21:36:53.7266920Z With the provided path, there will be 1 file uploaded 2024-08-06T21:36:53.7267386Z Uploading to s3 prefix: pytorch/pytorch/10273124344/1/artifact 2024-08-06T21:36:53.7306257Z Starting upload of logs-test-dynamo-1-3-amz2023.linux.2xlarge_28427567353.zip 2024-08-06T21:36:53.8781963Z Finished upload of logs-test-dynamo-1-3-amz2023.linux.2xlarge_28427567353.zip 2024-08-06T21:36:53.8990413Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-06T21:36:53.8991024Z with: 2024-08-06T21:36:53.8991399Z s3-bucket: gha-artifacts 2024-08-06T21:36:53.8992109Z s3-prefix: pytorch/pytorch/10273124344/1/artifact 2024-08-06T21:36:53.8992766Z retention-days: 14 2024-08-06T21:36:53.8993211Z if-no-files-found: ignore 2024-08-06T21:36:53.8993711Z path: debug-*.zip 2024-08-06T21:36:53.8994135Z name: artifact 2024-08-06T21:36:53.8994708Z region: us-east-1 2024-08-06T21:36:53.8995120Z env: 2024-08-06T21:36:53.8995502Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:53.8996340Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:53.8997220Z ##[endgroup] 2024-08-06T21:36:54.2430327Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-08-06T21:36:54.2621273Z ##[group]Run # shellcheck disable=SC2156 2024-08-06T21:36:54.2621660Z # shellcheck disable=SC2156 2024-08-06T21:36:54.2622270Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-08-06T21:36:54.2628385Z shell: /usr/bin/bash -e {0} 2024-08-06T21:36:54.2628672Z env: 2024-08-06T21:36:54.2628904Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:54.2629382Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:54.2629889Z ##[endgroup] 2024-08-06T21:36:54.4960302Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2024-08-06T21:36:54.4960776Z with: 2024-08-06T21:36:54.4960989Z env: 2024-08-06T21:36:54.4961241Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:54.4961717Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:54.4962241Z ##[endgroup] 2024-08-06T21:36:54.4985465Z ##[group]Run set -eou pipefail 2024-08-06T21:36:54.4985811Z set -eou pipefail 2024-08-06T21:36:54.4986084Z  2024-08-06T21:36:54.4986473Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-08-06T21:36:54.4987073Z for _ in $(seq 1440); do 2024-08-06T21:36:54.4987414Z  # Break if no ssh session exists anymore 2024-08-06T21:36:54.4987807Z  if [ "$(who)" = "" ]; then 2024-08-06T21:36:54.4988140Z  break 2024-08-06T21:36:54.4988387Z  fi 2024-08-06T21:36:54.4988674Z  echo "." 2024-08-06T21:36:54.4988919Z  sleep 5 2024-08-06T21:36:54.4989173Z done 2024-08-06T21:36:54.4995232Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:36:54.4995618Z env: 2024-08-06T21:36:54.4995854Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:54.4996332Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:54.4996833Z ##[endgroup] 2024-08-06T21:36:54.5019743Z Holding runner for 2 hours until all ssh sessions have logged out 2024-08-06T21:36:54.5094785Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-08-06T21:36:54.5095374Z # ignore expansion of "docker ps -q" since it could be empty 2024-08-06T21:36:54.5095803Z # shellcheck disable=SC2046 2024-08-06T21:36:54.5096155Z docker stop $(docker ps -q) || true 2024-08-06T21:36:54.5096514Z # Prune all of the docker images 2024-08-06T21:36:54.5096932Z docker system prune -af 2024-08-06T21:36:54.5102478Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:36:54.5102868Z env: 2024-08-06T21:36:54.5103076Z GIT_DEFAULT_BRANCH: main 2024-08-06T21:36:54.5103548Z DOCKER_CONTAINER_ID: b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:54.5104060Z ##[endgroup] 2024-08-06T21:36:55.1178640Z b394f538c1f1 2024-08-06T21:36:55.6499567Z Deleted Containers: 2024-08-06T21:36:55.6500214Z b394f538c1f16f859a77d14be7abece1039062530ae70051075c932210193476 2024-08-06T21:36:55.6500687Z 2024-08-06T21:37:01.1203360Z Deleted Images: 2024-08-06T21:37:01.1204485Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:02ec4fbd5adcb3fb91cf5ce431dec18b633de7d9 2024-08-06T21:37:01.1206040Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10@sha256:23992c65342e016e38c0d53a2566219b4cc3d16d20e9c923ad93c35946fb362b 2024-08-06T21:37:01.1207141Z deleted: sha256:7572c787a966437be85abe02d1578158976ce242058ff35a46097c5cce747b65 2024-08-06T21:37:01.1207793Z deleted: sha256:bb0e3a9f2e72853c2b52f7793f050008c489cfc06052e7b2e9368bb7210e5bbb 2024-08-06T21:37:01.1208461Z deleted: sha256:fd2ca1576d8f195a1592df62ba1bc1e3d1146a6f3b7d28f30d0bdeabcfd33ea4 2024-08-06T21:37:01.1209131Z deleted: sha256:43c8c30ff8298411dca3520537415b6911bba864a4a9b7ed3c5275e83feda0d6 2024-08-06T21:37:01.1209783Z deleted: sha256:9f1bee6a1d17a0e7704f2a852a487daa53695fdca7b556d2a682d833d0d9b4ad 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[command]/usr/bin/git version 2024-08-06T21:37:01.2342765Z git version 2.40.1 2024-08-06T21:37:01.2380341Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/d01b09fe-1e64-4463-bd7e-84bb88634208' before making global git config changes 2024-08-06T21:37:01.2381697Z Adding repository directory to the temporary git global config as a safe directory 2024-08-06T21:37:01.2385319Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-06T21:37:01.2424591Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-08-06T21:37:01.2456163Z [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' || :" 2024-08-06T21:37:01.2742327Z Entering 'android/libs/fbjni' 2024-08-06T21:37:01.2794523Z Entering 'third_party/FP16' 2024-08-06T21:37:01.2845153Z Entering 'third_party/FXdiv' 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Entering 'third_party/tensorpipe' 2024-08-06T21:37:01.6176664Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-06T21:37:01.6225004Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-06T21:37:01.6274593Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-06T21:37:01.6324336Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-06T21:37:01.6372455Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-06T21:37:01.6440690Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-08-06T21:37:01.6468207Z http.https://github.com/.extraheader 2024-08-06T21:37:01.6476807Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2024-08-06T21:37:01.6510295Z [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' || :" 2024-08-06T21:37:01.6789173Z Entering 'android/libs/fbjni' 2024-08-06T21:37:01.6823273Z http.https://github.com/.extraheader 2024-08-06T21:37:01.6855353Z Entering 'third_party/FP16' 2024-08-06T21:37:01.6889215Z http.https://github.com/.extraheader 2024-08-06T21:37:01.6919935Z Entering 'third_party/FXdiv' 2024-08-06T21:37:01.6953847Z http.https://github.com/.extraheader 2024-08-06T21:37:01.6986862Z Entering 'third_party/NNPACK' 2024-08-06T21:37:01.7018511Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7049184Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-06T21:37:01.7082396Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7112774Z Entering 'third_party/XNNPACK' 2024-08-06T21:37:01.7146751Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7195649Z Entering 'third_party/benchmark' 2024-08-06T21:37:01.7229887Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7262255Z Entering 'third_party/cpp-httplib' 2024-08-06T21:37:01.7295169Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7325506Z Entering 'third_party/cpuinfo' 2024-08-06T21:37:01.7360039Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7390284Z Entering 'third_party/cudnn_frontend' 2024-08-06T21:37:01.7423258Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7455851Z Entering 'third_party/cutlass' 2024-08-06T21:37:01.7489075Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7528330Z Entering 'third_party/eigen' 2024-08-06T21:37:01.7565015Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7598353Z Entering 'third_party/fbgemm' 2024-08-06T21:37:01.7632403Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7663680Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-06T21:37:01.7697460Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7728417Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-06T21:37:01.7762694Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7792639Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-08-06T21:37:01.7825382Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7863004Z Entering 'third_party/fbgemm/third_party/googletest' 2024-08-06T21:37:01.7895734Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7926931Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-08-06T21:37:01.7959861Z http.https://github.com/.extraheader 2024-08-06T21:37:01.7991978Z Entering 'third_party/flatbuffers' 2024-08-06T21:37:01.8026658Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8060296Z Entering 'third_party/fmt' 2024-08-06T21:37:01.8093363Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8124629Z Entering 'third_party/foxi' 2024-08-06T21:37:01.8158359Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8189516Z Entering 'third_party/gemmlowp/gemmlowp' 2024-08-06T21:37:01.8224310Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8257537Z Entering 'third_party/gloo' 2024-08-06T21:37:01.8290420Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8321790Z Entering 'third_party/googletest' 2024-08-06T21:37:01.8355039Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8385890Z Entering 'third_party/ideep' 2024-08-06T21:37:01.8419039Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8450123Z Entering 'third_party/ideep/mkl-dnn' 2024-08-06T21:37:01.8481969Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8520436Z Entering 'third_party/ittapi' 2024-08-06T21:37:01.8556825Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8587179Z Entering 'third_party/kineto' 2024-08-06T21:37:01.8620087Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8650708Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-06T21:37:01.8683171Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8714394Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-06T21:37:01.8747572Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8779593Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-06T21:37:01.8812735Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8844869Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-06T21:37:01.8878510Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8909480Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-06T21:37:01.8942032Z http.https://github.com/.extraheader 2024-08-06T21:37:01.8972456Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-06T21:37:01.9005514Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9038356Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-06T21:37:01.9071944Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9102726Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-06T21:37:01.9136141Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9168764Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-06T21:37:01.9201200Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9233053Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-06T21:37:01.9266761Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9299590Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-06T21:37:01.9332544Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9363982Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-06T21:37:01.9396733Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9429924Z Entering 'third_party/mimalloc' 2024-08-06T21:37:01.9463966Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9496125Z Entering 'third_party/nccl/nccl' 2024-08-06T21:37:01.9529591Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9560715Z Entering 'third_party/nlohmann' 2024-08-06T21:37:01.9593662Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9625181Z Entering 'third_party/onnx' 2024-08-06T21:37:01.9658180Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9704630Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-06T21:37:01.9737339Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9770244Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-06T21:37:01.9803800Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9837425Z Entering 'third_party/opentelemetry-cpp' 2024-08-06T21:37:01.9873695Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9907400Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-06T21:37:01.9939929Z http.https://github.com/.extraheader 2024-08-06T21:37:01.9972243Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-06T21:37:02.0006474Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0037322Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-06T21:37:02.0070973Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0101659Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-06T21:37:02.0134392Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0168154Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-06T21:37:02.0201014Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0231018Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-06T21:37:02.0264841Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0295052Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-06T21:37:02.0327349Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0358554Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-06T21:37:02.0391155Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0423666Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-06T21:37:02.0457405Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0489902Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-06T21:37:02.0522130Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0574105Z Entering 'third_party/pocketfft' 2024-08-06T21:37:02.0607547Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0638268Z Entering 'third_party/protobuf' 2024-08-06T21:37:02.0673310Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0707458Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-06T21:37:02.0740823Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0771791Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-06T21:37:02.0804920Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0837425Z Entering 'third_party/psimd' 2024-08-06T21:37:02.0871757Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0903111Z Entering 'third_party/pthreadpool' 2024-08-06T21:37:02.0937639Z http.https://github.com/.extraheader 2024-08-06T21:37:02.0968389Z Entering 'third_party/pybind11' 2024-08-06T21:37:02.1002608Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1034391Z Entering 'third_party/python-peachpy' 2024-08-06T21:37:02.1068434Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1098496Z Entering 'third_party/sleef' 2024-08-06T21:37:02.1131274Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1163012Z Entering 'third_party/tensorpipe' 2024-08-06T21:37:02.1196382Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1227639Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-06T21:37:02.1261332Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1291564Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-06T21:37:02.1323979Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1354243Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-06T21:37:02.1385785Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1416074Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-06T21:37:02.1447893Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1477728Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-06T21:37:02.1510719Z http.https://github.com/.extraheader 2024-08-06T21:37:02.1621732Z A job completed hook has been configured by the self-hosted runner administrator 2024-08-06T21:37:02.1650399Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2024-08-06T21:37:02.1655512Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-06T21:37:02.1655902Z ##[endgroup] 2024-08-06T21:37:10.1893727Z Cleaning up orphan processes