2025-09-07T06:24:34.6276171Z Current runner version: '2.328.0' 2025-09-07T06:24:34.6282570Z Runner name: 'i-0dd977e7b70f3c8d7' 2025-09-07T06:24:34.6283479Z Runner group name: 'default' 2025-09-07T06:24:34.6284492Z Machine name: 'ip-10-0-22-62' 2025-09-07T06:24:34.6287652Z ##[group]GITHUB_TOKEN Permissions 2025-09-07T06:24:34.6290005Z Contents: read 2025-09-07T06:24:34.6290597Z Metadata: read 2025-09-07T06:24:34.6291304Z ##[endgroup] 2025-09-07T06:24:34.6293445Z Secret source: Actions 2025-09-07T06:24:34.6294130Z Prepare workflow directory 2025-09-07T06:24:34.6834979Z Prepare all required actions 2025-09-07T06:24:34.6875072Z Getting action download info 2025-09-07T06:24:34.9758339Z Download action repository 'pytorch/test-infra@main' (SHA:548a4bc624d43a01cdf165a63b041f0ae014ddbd) 2025-09-07T06:24:36.8970003Z Download action repository 'pytorch/pytorch@main' (SHA:93fb23d6fae7c4e82c4239a1033e522088742634) 2025-09-07T06:24:50.6690483Z Download action repository 'actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065' (SHA:a26af69be951a213d495a4c3e4e4022e16d87065) 2025-09-07T06:24:51.0131267Z Download action repository 'aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722' (SHA:ececac1a45f3b08a01d2dd070d28d111c5fe6722) 2025-09-07T06:24:51.2623754Z Download action repository 'aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076' (SHA:062b18b96a7aff071d4dc91bc00c4c1a7945b076) 2025-09-07T06:24:51.4382572Z Download action repository 'seemethere/upload-artifact-s3@baba72d0712b404f646cebe0730933554ebce96a' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-09-07T06:24:51.7601590Z Getting action download info 2025-09-07T06:24:51.8899780Z Download action repository 'actions/checkout@v4' (SHA:08eba0b27e820071cde6df949e0beb9ba4906955) 2025-09-07T06:24:52.1779340Z Getting action download info 2025-09-07T06:24:52.4147047Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-09-07T06:24:52.6090511Z Getting action download info 2025-09-07T06:24:52.7330051Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-09-07T06:24:52.9216823Z Getting action download info 2025-09-07T06:24:53.0496023Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/main (93fb23d6fae7c4e82c4239a1033e522088742634) 2025-09-07T06:24:53.0500265Z ##[group] Inputs 2025-09-07T06:24:53.0500640Z build-environment: linux-jammy-py3.13-clang12 2025-09-07T06:24:53.0503647Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-09-07T06:24:53.0507103Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:24:53.0507929Z sync-tag: 2025-09-07T06:24:53.0508715Z timeout-minutes: 240 2025-09-07T06:24:53.0508993Z use-gha: 2025-09-07T06:24:53.0509227Z dashboard-tag: 2025-09-07T06:24:53.0509485Z s3-bucket: gha-artifacts 2025-09-07T06:24:53.0509755Z aws-role-to-assume: 2025-09-07T06:24:53.0510322Z disable-monitor: false 2025-09-07T06:24:53.0510640Z monitor-log-interval: 5 2025-09-07T06:24:53.0511209Z monitor-data-collect-interval: 1 2025-09-07T06:24:53.0511636Z ##[endgroup] 2025-09-07T06:24:53.0512080Z Complete job name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:24:53.0991263Z A job started hook has been configured by the self-hosted runner administrator 2025-09-07T06:24:53.1095493Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-09-07T06:24:53.1104703Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:24:53.1105380Z ##[endgroup] 2025-09-07T06:24:54.3444301Z Runner Type: linux.2xlarge 2025-09-07T06:24:54.3444947Z Instance Type: c5.2xlarge 2025-09-07T06:24:54.3445217Z AMI Name: unknown 2025-09-07T06:24:54.3467965Z AMI ID: ami-05ffe3c48a9991133 2025-09-07T06:24:59.8013857Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-09-07T06:24:59.8014330Z with: 2025-09-07T06:24:59.8014926Z github-secret: *** 2025-09-07T06:24:59.8015677Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-09-07T06:24:59.8016504Z activate-with-label: false 2025-09-07T06:24:59.8016795Z label: with-ssh 2025-09-07T06:24:59.8017052Z remove-existing-keys: true 2025-09-07T06:24:59.8017333Z fail-silently: true 2025-09-07T06:24:59.8017586Z env: 2025-09-07T06:24:59.8017819Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.8018105Z ##[endgroup] 2025-09-07T06:24:59.9166185Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-09-07T06:24:59.9167687Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys 2025-09-07T06:24:59.9317953Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-09-07T06:24:59.9318430Z with: 2025-09-07T06:24:59.9318649Z no-sudo: true 2025-09-07T06:24:59.9318910Z submodules: recursive 2025-09-07T06:24:59.9319182Z fetch-depth: 0 2025-09-07T06:24:59.9319408Z env: 2025-09-07T06:24:59.9319635Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.9319922Z ##[endgroup] 2025-09-07T06:24:59.9403841Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:24:59.9404962Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:24:59.9415195Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:24:59.9415607Z env: 2025-09-07T06:24:59.9415855Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.9416168Z ##[endgroup] 2025-09-07T06:24:59.9504811Z ##[group]Run # Use all available CPUs for fetching 2025-09-07T06:24:59.9505275Z # Use all available CPUs for fetching 2025-09-07T06:24:59.9505640Z cd "${GITHUB_WORKSPACE}" 2025-09-07T06:24:59.9505983Z git config --global fetch.parallel 0 2025-09-07T06:24:59.9506392Z git config --global submodule.fetchJobs 0 2025-09-07T06:24:59.9506748Z  2025-09-07T06:24:59.9507133Z # Clean workspace. The default checkout action should also do this, but 2025-09-07T06:24:59.9507613Z # do it here as well just in case 2025-09-07T06:24:59.9507945Z if [[ -d .git ]]; then 2025-09-07T06:24:59.9508253Z  if [ -z "${NO_SUDO}" ]; then 2025-09-07T06:24:59.9508581Z  sudo git clean -ffdx 2025-09-07T06:24:59.9508861Z  else 2025-09-07T06:24:59.9509108Z  git clean -ffdx 2025-09-07T06:24:59.9509385Z  fi 2025-09-07T06:24:59.9509614Z fi 2025-09-07T06:24:59.9515011Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:24:59.9515408Z env: 2025-09-07T06:24:59.9515724Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.9516024Z NO_SUDO: true 2025-09-07T06:24:59.9516261Z ##[endgroup] 2025-09-07T06:24:59.9664597Z ##[group]Run actions/checkout@v4 2025-09-07T06:24:59.9664917Z with: 2025-09-07T06:24:59.9665172Z ref: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:24:59.9665694Z fetch-depth: 0 2025-09-07T06:24:59.9665929Z submodules: recursive 2025-09-07T06:24:59.9666202Z show-progress: false 2025-09-07T06:24:59.9666481Z repository: pytorch/pytorch 2025-09-07T06:24:59.9666915Z token: *** 2025-09-07T06:24:59.9667135Z ssh-strict: true 2025-09-07T06:24:59.9667379Z ssh-user: git 2025-09-07T06:24:59.9667632Z persist-credentials: true 2025-09-07T06:24:59.9667912Z clean: true 2025-09-07T06:24:59.9668164Z sparse-checkout-cone-mode: true 2025-09-07T06:24:59.9668473Z fetch-tags: false 2025-09-07T06:24:59.9668713Z lfs: false 2025-09-07T06:24:59.9668948Z set-safe-directory: true 2025-09-07T06:24:59.9669211Z env: 2025-09-07T06:24:59.9669430Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.9669698Z ##[endgroup] 2025-09-07T06:25:00.0777282Z Syncing repository: pytorch/pytorch 2025-09-07T06:25:00.0779362Z ##[group]Getting Git version info 2025-09-07T06:25:00.0780174Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-09-07T06:25:00.0781355Z [command]/usr/bin/git version 2025-09-07T06:25:00.0781835Z git version 2.47.1 2025-09-07T06:25:00.0790573Z ##[endgroup] 2025-09-07T06:25:00.0799908Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/32b8c10e-a67f-4be9-90d5-4243d8f6d65e/.gitconfig' 2025-09-07T06:25:00.0818019Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/32b8c10e-a67f-4be9-90d5-4243d8f6d65e' before making global git config changes 2025-09-07T06:25:00.0819096Z Adding repository directory to the temporary git global config as a safe directory 2025-09-07T06:25:00.0822785Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T06:25:00.0857784Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-09-07T06:25:00.0860794Z ##[group]Initializing the repository 2025-09-07T06:25:00.0864971Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T06:25:00.0896697Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-07T06:25:00.0897367Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-07T06:25:00.0897966Z hint: of your new repositories, which will suppress this warning, call: 2025-09-07T06:25:00.0898390Z hint: 2025-09-07T06:25:00.0898708Z hint: git config --global init.defaultBranch 2025-09-07T06:25:00.0899072Z hint: 2025-09-07T06:25:00.0899422Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-07T06:25:00.0900022Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-07T06:25:00.0900479Z hint: 2025-09-07T06:25:00.0900711Z hint: git branch -m 2025-09-07T06:25:00.0901255Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-09-07T06:25:00.0907684Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-09-07T06:25:00.0933285Z ##[endgroup] 2025-09-07T06:25:00.0933778Z ##[group]Disabling automatic garbage collection 2025-09-07T06:25:00.0937467Z [command]/usr/bin/git config --local gc.auto 0 2025-09-07T06:25:00.0961364Z ##[endgroup] 2025-09-07T06:25:00.0961772Z ##[group]Setting up auth 2025-09-07T06:25:00.0968211Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-07T06:25:00.0994370Z [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' || :" 2025-09-07T06:25:00.1275408Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-07T06:25:00.1299412Z [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' || :" 2025-09-07T06:25:00.1558016Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-07T06:25:00.1597454Z ##[endgroup] 2025-09-07T06:25:00.1597906Z ##[group]Fetching the repository 2025-09-07T06:25:00.1613873Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-09-07T06:25:54.2487096Z From https://github.com/pytorch/pytorch 2025-09-07T06:25:54.2487606Z * [new branch] 160583 -> origin/160583 2025-09-07T06:25:54.2488195Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-09-07T06:25:54.2488761Z * [new branch] 5addvllmbuild -> origin/5addvllmbuild 2025-09-07T06:25:54.2489902Z * [new branch] AaronWang04_addmmfusion_perftest -> origin/AaronWang04_addmmfusion_perftest 2025-09-07T06:25:54.2490630Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-09-07T06:25:54.2491257Z * [new branch] ISSUE-154849 -> origin/ISSUE-154849 2025-09-07T06:25:54.2492729Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-09-07T06:25:54.2494356Z * [new branch] NicoshevSVE128 -> origin/NicoshevSVE128 2025-09-07T06:25:54.2495737Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-09-07T06:25:54.2497102Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-09-07T06:25:54.2498848Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-09-07T06:25:54.2499903Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-09-07T06:25:54.2500857Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-09-07T06:25:54.2502147Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-09-07T06:25:54.2503329Z * [new branch] VLA_exp -> origin/VLA_exp 2025-09-07T06:25:54.2505051Z * [new branch] actually-run-mps-aot-inductor -> origin/actually-run-mps-aot-inductor 2025-09-07T06:25:54.2506113Z * [new branch] add-missing-args-normalization -> origin/add-missing-args-normalization 2025-09-07T06:25:54.2507296Z * [new branch] add-user-guide-structure -> origin/add-user-guide-structure 2025-09-07T06:25:54.2508694Z * [new branch] add-vllm-nightly-build -> origin/add-vllm-nightly-build 2025-09-07T06:25:54.2509821Z * [new branch] add_compile_benchmarking -> origin/add_compile_benchmarking 2025-09-07T06:25:54.2511040Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-09-07T06:25:54.2512236Z * [new branch] addsimde -> origin/addsimde 2025-09-07T06:25:54.2513487Z * [new branch] addvllmtest -> origin/addvllmtest 2025-09-07T06:25:54.2515287Z * [new branch] adi/acl_upgrade -> origin/adi/acl_upgrade 2025-09-07T06:25:54.2516479Z * [new branch] adi/test -> origin/adi/test 2025-09-07T06:25:54.2517683Z * [new branch] adi/test_bgemm -> origin/adi/test_bgemm 2025-09-07T06:25:54.2518826Z * [new branch] adi/test_fusions -> origin/adi/test_fusions 2025-09-07T06:25:54.2520023Z * [new branch] adi/test_onednn_v3.9 -> origin/adi/test_onednn_v3.9 2025-09-07T06:25:54.2521393Z * [new branch] adi/test_presve_change -> origin/adi/test_presve_change 2025-09-07T06:25:54.2522413Z * [new branch] adi/test_timm -> origin/adi/test_timm 2025-09-07T06:25:54.2524238Z * [new branch] adi/testpresve_change -> origin/adi/testpresve_change 2025-09-07T06:25:54.2526330Z * [new branch] aditew01/test/vec_bf16 -> origin/aditew01/test/vec_bf16 2025-09-07T06:25:54.2527726Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-09-07T06:25:54.2528852Z * [new branch] alt-disable -> origin/alt-disable 2025-09-07T06:25:54.2530776Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-09-07T06:25:54.2531879Z * [new branch] angelayi/aoti_inductor_fx -> origin/angelayi/aoti_inductor_fx 2025-09-07T06:25:54.2533070Z * [new branch] angelayi/benchmark -> origin/angelayi/benchmark 2025-09-07T06:25:54.2534424Z * [new branch] angelayi/benchmark2 -> origin/angelayi/benchmark2 2025-09-07T06:25:54.2535671Z * [new branch] angelayi/change_pytree_serialization -> origin/angelayi/change_pytree_serialization 2025-09-07T06:25:54.2536756Z * [new branch] angelayi/cpp_loader -> origin/angelayi/cpp_loader 2025-09-07T06:25:54.2538453Z * [new branch] angelayi/custom_op_subgraph -> origin/angelayi/custom_op_subgraph 2025-09-07T06:25:54.2539997Z * [new branch] angelayi/customop -> origin/angelayi/customop 2025-09-07T06:25:54.2541641Z * [new branch] angelayi/fake_cache_empty -> origin/angelayi/fake_cache_empty 2025-09-07T06:25:54.2542877Z * [new branch] angelayi/is_symbolic_tracing -> origin/angelayi/is_symbolic_tracing 2025-09-07T06:25:54.2543957Z * [new branch] angelayi/item -> origin/angelayi/item 2025-09-07T06:25:54.2545335Z * [new branch] angelayi/no_so_weight -> origin/angelayi/no_so_weight 2025-09-07T06:25:54.2546456Z * [new branch] angelayi/opoverload -> origin/angelayi/opoverload 2025-09-07T06:25:54.2547637Z * [new branch] angelayi/pattern -> origin/angelayi/pattern 2025-09-07T06:25:54.2548850Z * [new branch] angelayi/pytree -> origin/angelayi/pytree 2025-09-07T06:25:54.2550062Z * [new branch] angelayi/scan_layers -> origin/angelayi/scan_layers 2025-09-07T06:25:54.2551272Z * [new branch] angelayi/symint_input -> origin/angelayi/symint_input 2025-09-07T06:25:54.2552476Z * [new branch] angelayi/test_cpp -> origin/angelayi/test_cpp 2025-09-07T06:25:54.2553658Z * [new branch] angelayi/torch_size -> origin/angelayi/torch_size 2025-09-07T06:25:54.2554903Z * [new branch] aoti-cuda-alloc -> origin/aoti-cuda-alloc 2025-09-07T06:25:54.2556147Z * [new branch] aoti_target_windows -> origin/aoti_target_windows 2025-09-07T06:25:54.2557278Z * [new branch] aoti_weight_sharing -> origin/aoti_weight_sharing 2025-09-07T06:25:54.2558680Z * [new branch] atalman-inductor-perf-cu124 -> origin/atalman-inductor-perf-cu124 2025-09-07T06:25:54.2559796Z * [new branch] atalman-inductor-perf-cu124.1 -> origin/atalman-inductor-perf-cu124.1 2025-09-07T06:25:54.2560993Z * [new branch] atalman-patch-1 -> origin/atalman-patch-1 2025-09-07T06:25:54.2562267Z * [new branch] atalman-patch-3 -> origin/atalman-patch-3 2025-09-07T06:25:54.2563470Z * [new branch] atalman-patch-4 -> origin/atalman-patch-4 2025-09-07T06:25:54.2565052Z * [new branch] atalman-patch-5 -> origin/atalman-patch-5 2025-09-07T06:25:54.2566215Z * [new branch] atalman-patch-6 -> origin/atalman-patch-6 2025-09-07T06:25:54.2567568Z * [new branch] atalman_inductor_2.3.0 -> origin/atalman_inductor_2.3.0 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origin/xmfan/ca_1051b93192 2025-09-07T06:25:54.8719039Z * [new branch] xmfan/ca_1a722f62c248391fc4a542e8851a5559aa356ae8 -> origin/xmfan/ca_1a722f62c248391fc4a542e8851a5559aa356ae8 2025-09-07T06:25:54.8719906Z * [new branch] xmfan/ca_5a2be192d1 -> origin/xmfan/ca_5a2be192d1 2025-09-07T06:25:54.8721502Z * [new branch] xmfan/ca_9d59b516e9 -> origin/xmfan/ca_9d59b516e9 2025-09-07T06:25:54.8723221Z * [new branch] xmfan/ca_api -> origin/xmfan/ca_api 2025-09-07T06:25:54.8724670Z * [new branch] xmfan/ca_apr8 -> origin/xmfan/ca_apr8 2025-09-07T06:25:54.8726664Z * [new branch] xmfan/ca_base -> origin/xmfan/ca_base 2025-09-07T06:25:54.8728718Z * [new branch] xmfan/ca_cudagraphs -> origin/xmfan/ca_cudagraphs 2025-09-07T06:25:54.8730181Z * [new branch] xmfan/ca_dynamic -> origin/xmfan/ca_dynamic 2025-09-07T06:25:54.8731717Z * [new branch] xmfan/ca_fix_dyn -> origin/xmfan/ca_fix_dyn 2025-09-07T06:25:54.8733300Z * [new branch] xmfan/ca_fix_lowering -> origin/xmfan/ca_fix_lowering 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2025-09-07T06:25:54.8768808Z * [new branch] xmfan/disable_duck_shape -> origin/xmfan/disable_duck_shape 2025-09-07T06:25:54.8770626Z * [new branch] xmfan/fca_cpp_node_passthrough -> origin/xmfan/fca_cpp_node_passthrough 2025-09-07T06:25:54.8772017Z * [new branch] xmfan/issue_123374 -> origin/xmfan/issue_123374 2025-09-07T06:25:54.8774303Z * [new branch] xmfan/post_3945954741e2d37023c5d6954f9483008e0892f9 -> origin/xmfan/post_3945954741e2d37023c5d6954f9483008e0892f9 2025-09-07T06:25:54.8775808Z * [new branch] xmfan/pre_3945954741e2d37023c5d6954f9483008e0892f9 -> origin/xmfan/pre_3945954741e2d37023c5d6954f9483008e0892f9 2025-09-07T06:25:54.8776960Z * [new branch] xmfan/segfault_test -> origin/xmfan/segfault_test 2025-09-07T06:25:54.8778658Z * [new branch] xmfan/single_step -> origin/xmfan/single_step 2025-09-07T06:25:54.8780240Z * [new branch] xmfan/sth_0829 -> origin/xmfan/sth_0829 2025-09-07T06:25:54.8781885Z * [new branch] xmfan/test -> origin/xmfan/test 2025-09-07T06:25:54.8784506Z * [new branch] yguo/debug-0226-constexpr -> origin/yguo/debug-0226-constexpr 2025-09-07T06:25:54.8786394Z * [new branch] yguo/new_latest_changes -> origin/yguo/new_latest_changes 2025-09-07T06:25:54.8787953Z * [new branch] yguo/patch_constexpr_changes -> origin/yguo/patch_constexpr_changes 2025-09-07T06:25:54.8789590Z * [new branch] yihan_quantization -> origin/yihan_quantization 2025-09-07T06:25:54.8791807Z * [new branch] yiming/add_jit_trace_benchmark -> origin/yiming/add_jit_trace_benchmark 2025-09-07T06:25:54.8793112Z * [new branch] yiming/add_nativert_benchmark -> origin/yiming/add_nativert_benchmark 2025-09-07T06:25:54.8794724Z * [new branch] yiming/bootcamp -> origin/yiming/bootcamp 2025-09-07T06:25:54.8796737Z * [new branch] zainr/canary-test -> origin/zainr/canary-test 2025-09-07T06:25:54.8798516Z * [new branch] zainr/cleanup-gh-runners -> origin/zainr/cleanup-gh-runners 2025-09-07T06:25:54.8799713Z * [new branch] zainr/git-push-v2 -> origin/zainr/git-push-v2 2025-09-07T06:25:54.8801372Z * [new branch] zainr/pull-migration-c -> origin/zainr/pull-migration-c 2025-09-07T06:25:54.8802874Z * [new branch] zainr/test -> origin/zainr/test 2025-09-07T06:25:54.8804550Z * [new branch] zainr/test2 -> origin/zainr/test2 2025-09-07T06:25:54.8805977Z * [new branch] zainr/unstable -> origin/zainr/unstable 2025-09-07T06:25:54.8807483Z * [new branch] zainr/unstable-xla -> origin/zainr/unstable-xla 2025-09-07T06:25:54.8809336Z * [new branch] zasdfgbnm-patch-3 -> origin/zasdfgbnm-patch-3 2025-09-07T06:25:54.8810950Z * [new branch] zb2p -> origin/zb2p 2025-09-07T06:25:54.8812703Z * [new branch] zero_grad_optimization -> origin/zero_grad_optimization 2025-09-07T06:25:54.8814377Z * [new branch] zeros-and-scatter-part2 -> origin/zeros-and-scatter-part2 2025-09-07T06:25:54.8817206Z * [new branch] zhxchen17/scratch/0 -> origin/zhxchen17/scratch/0 2025-09-07T06:25:54.8819271Z * [new branch] zhxhcen17/moodycamel -> origin/zhxhcen17/moodycamel 2025-09-07T06:25:54.8821432Z * [new branch] zxiiro/main -> origin/zxiiro/main 2025-09-07T06:25:54.8823112Z * [new tag] bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug -> bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug 2025-09-07T06:25:54.8824196Z * [new tag] ci/binaries/77164 -> ci/binaries/77164 2025-09-07T06:25:54.8825891Z * [new tag] ciflow/binaries/156049 -> ciflow/binaries/156049 2025-09-07T06:25:54.8826682Z * [new tag] ciflow/binaries/156712 -> ciflow/binaries/156712 2025-09-07T06:25:54.8827702Z * [new tag] ciflow/binaries/157432 -> ciflow/binaries/157432 2025-09-07T06:25:54.8828793Z * [new tag] ciflow/binaries/157685 -> ciflow/binaries/157685 2025-09-07T06:25:54.8829856Z * [new tag] ciflow/binaries/157689 -> ciflow/binaries/157689 2025-09-07T06:25:54.8830849Z * [new tag] ciflow/binaries/158104 -> ciflow/binaries/158104 2025-09-07T06:25:54.8832287Z * [new tag] ciflow/binaries/160229 -> ciflow/binaries/160229 2025-09-07T06:25:54.8833358Z * [new tag] ciflow/binaries/160720 -> ciflow/binaries/160720 2025-09-07T06:25:54.8834351Z * [new tag] ciflow/binaries/162080 -> ciflow/binaries/162080 2025-09-07T06:25:54.8835400Z * [new tag] ciflow/binaries/162329 -> ciflow/binaries/162329 2025-09-07T06:25:54.8837021Z * [new tag] ciflow/binaries_libtorch/156049 -> ciflow/binaries_libtorch/156049 2025-09-07T06:25:54.8837955Z * [new tag] ciflow/binaries_libtorch/156711 -> ciflow/binaries_libtorch/156711 2025-09-07T06:25:54.8838949Z * [new tag] ciflow/binaries_libtorch/157432 -> ciflow/binaries_libtorch/157432 2025-09-07T06:25:54.8840209Z * [new tag] ciflow/binaries_wheel/156049 -> ciflow/binaries_wheel/156049 2025-09-07T06:25:54.8841210Z * [new tag] ciflow/binaries_wheel/156711 -> ciflow/binaries_wheel/156711 2025-09-07T06:25:54.8842396Z * [new tag] ciflow/binaries_wheel/157432 -> ciflow/binaries_wheel/157432 2025-09-07T06:25:54.8843415Z * [new tag] ciflow/binaries_wheel/162136 -> ciflow/binaries_wheel/162136 2025-09-07T06:25:54.8844688Z * [new tag] ciflow/binaries_wheel/162252 -> ciflow/binaries_wheel/162252 2025-09-07T06:25:54.8845837Z * [new tag] ciflow/binaries_wheel/162325 -> ciflow/binaries_wheel/162325 2025-09-07T06:25:54.8847397Z * [new tag] ciflow/h100-distributed/156703 -> ciflow/h100-distributed/156703 2025-09-07T06:25:54.8848509Z * [new tag] ciflow/h100-symm-mem/157635 -> ciflow/h100-symm-mem/157635 2025-09-07T06:25:54.8849511Z * [new tag] ciflow/h100-symm-mem/161984 -> ciflow/h100-symm-mem/161984 2025-09-07T06:25:54.8850559Z * [new tag] ciflow/h100-symm-mem/162003 -> ciflow/h100-symm-mem/162003 2025-09-07T06:25:54.8851688Z * [new tag] ciflow/h100-symm-mem/162011 -> ciflow/h100-symm-mem/162011 2025-09-07T06:25:54.8852600Z * [new tag] ciflow/h100-symm-mem/162026 -> ciflow/h100-symm-mem/162026 2025-09-07T06:25:54.8853638Z * [new tag] ciflow/h100-symm-mem/162033 -> ciflow/h100-symm-mem/162033 2025-09-07T06:25:54.8854696Z * [new tag] ciflow/h100-symm-mem/162040 -> ciflow/h100-symm-mem/162040 2025-09-07T06:25:54.8855712Z * [new tag] ciflow/h100-symm-mem/162041 -> ciflow/h100-symm-mem/162041 2025-09-07T06:25:54.8856783Z * [new tag] ciflow/h100-symm-mem/162142 -> ciflow/h100-symm-mem/162142 2025-09-07T06:25:54.8857779Z * [new tag] ciflow/h100-symm-mem/162150 -> ciflow/h100-symm-mem/162150 2025-09-07T06:25:54.8859501Z * [new tag] ciflow/h100-symm-mem/162243 -> ciflow/h100-symm-mem/162243 2025-09-07T06:25:54.8860859Z * [new tag] ciflow/h100-symm-mem/162320 -> ciflow/h100-symm-mem/162320 2025-09-07T06:25:54.8861965Z * [new tag] ciflow/h100/159158 -> ciflow/h100/159158 2025-09-07T06:25:54.8863871Z * [new tag] ciflow/h100/160480 -> ciflow/h100/160480 2025-09-07T06:25:54.8864878Z * [new tag] ciflow/h100/161749 -> ciflow/h100/161749 2025-09-07T06:25:54.8866175Z * [new tag] ciflow/h100/162022 -> ciflow/h100/162022 2025-09-07T06:25:54.8867091Z * [new tag] ciflow/h100/162278 -> ciflow/h100/162278 2025-09-07T06:25:54.8869136Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/156592 -> ciflow/inductor-perf-test-nightly-rocm/156592 2025-09-07T06:25:54.8870373Z * [new tag] ciflow/inductor-perf-test-nightly/156592 -> ciflow/inductor-perf-test-nightly/156592 2025-09-07T06:25:54.8871553Z * [new tag] ciflow/inductor-periodic/162063 -> ciflow/inductor-periodic/162063 2025-09-07T06:25:54.8872508Z * [new tag] ciflow/inductor-periodic/162227 -> ciflow/inductor-periodic/162227 2025-09-07T06:25:54.8874043Z * [new tag] ciflow/inductor-periodic/162323 -> ciflow/inductor-periodic/162323 2025-09-07T06:25:54.8877318Z * [new tag] ciflow/inductor-rocm/154170 -> ciflow/inductor-rocm/154170 2025-09-07T06:25:54.8878085Z * [new tag] ciflow/inductor-rocm/159146 -> ciflow/inductor-rocm/159146 2025-09-07T06:25:54.8878799Z * [new tag] ciflow/inductor-rocm/159158 -> ciflow/inductor-rocm/159158 2025-09-07T06:25:54.8879572Z * [new tag] ciflow/inductor-rocm/161715 -> ciflow/inductor-rocm/161715 2025-09-07T06:25:54.8881005Z * [new tag] ciflow/inductor-rocm/162053 -> ciflow/inductor-rocm/162053 2025-09-07T06:25:54.8881787Z * [new tag] ciflow/inductor-rocm/162056 -> ciflow/inductor-rocm/162056 2025-09-07T06:25:54.8882740Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-09-07T06:25:54.8883321Z * [new tag] ciflow/inductor/148180 -> ciflow/inductor/148180 2025-09-07T06:25:54.8884007Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-09-07T06:25:54.8884803Z * [new tag] ciflow/inductor/148484 -> ciflow/inductor/148484 2025-09-07T06:25:54.8885487Z * [new tag] ciflow/inductor/148492 -> ciflow/inductor/148492 2025-09-07T06:25:54.8886178Z * [new tag] ciflow/inductor/152624 -> ciflow/inductor/152624 2025-09-07T06:25:54.8886855Z * [new tag] ciflow/inductor/154694 -> ciflow/inductor/154694 2025-09-07T06:25:54.8887553Z * [new tag] ciflow/inductor/156049 -> ciflow/inductor/156049 2025-09-07T06:25:54.8888157Z * [new tag] ciflow/inductor/156592 -> ciflow/inductor/156592 2025-09-07T06:25:54.8889366Z * [new tag] ciflow/inductor/157635 -> ciflow/inductor/157635 2025-09-07T06:25:54.8890013Z * [new tag] ciflow/inductor/157685 -> ciflow/inductor/157685 2025-09-07T06:25:54.8890999Z * [new tag] ciflow/inductor/157686 -> ciflow/inductor/157686 2025-09-07T06:25:54.8891931Z * [new tag] ciflow/inductor/157689 -> ciflow/inductor/157689 2025-09-07T06:25:54.8892750Z * [new tag] ciflow/inductor/157699 -> ciflow/inductor/157699 2025-09-07T06:25:54.8893674Z * [new tag] ciflow/inductor/157743 -> ciflow/inductor/157743 2025-09-07T06:25:54.8894413Z * [new tag] ciflow/inductor/157994 -> ciflow/inductor/157994 2025-09-07T06:25:54.8895177Z * [new tag] ciflow/inductor/158091 -> ciflow/inductor/158091 2025-09-07T06:25:54.8895880Z * [new tag] ciflow/inductor/158104 -> ciflow/inductor/158104 2025-09-07T06:25:54.8896699Z * [new tag] ciflow/inductor/158404 -> ciflow/inductor/158404 2025-09-07T06:25:54.8897408Z * [new tag] ciflow/inductor/158647 -> ciflow/inductor/158647 2025-09-07T06:25:54.8898506Z * [new tag] ciflow/inductor/158932 -> ciflow/inductor/158932 2025-09-07T06:25:54.8899161Z * [new tag] ciflow/inductor/159146 -> ciflow/inductor/159146 2025-09-07T06:25:54.8899878Z * [new tag] ciflow/inductor/159158 -> ciflow/inductor/159158 2025-09-07T06:25:54.8900773Z * [new tag] ciflow/inductor/159274 -> ciflow/inductor/159274 2025-09-07T06:25:54.8901493Z * [new tag] ciflow/inductor/159664 -> ciflow/inductor/159664 2025-09-07T06:25:54.8902418Z * [new tag] ciflow/inductor/159778 -> ciflow/inductor/159778 2025-09-07T06:25:54.8903032Z * [new tag] ciflow/inductor/159835 -> ciflow/inductor/159835 2025-09-07T06:25:54.8904022Z * [new tag] ciflow/inductor/159944 -> ciflow/inductor/159944 2025-09-07T06:25:54.8904788Z * [new tag] ciflow/inductor/160161 -> ciflow/inductor/160161 2025-09-07T06:25:54.8905521Z * [new tag] ciflow/inductor/160174 -> ciflow/inductor/160174 2025-09-07T06:25:54.8906439Z * [new tag] ciflow/inductor/160323 -> ciflow/inductor/160323 2025-09-07T06:25:54.8907470Z * [new tag] ciflow/inductor/160324 -> ciflow/inductor/160324 2025-09-07T06:25:54.8908255Z * [new tag] ciflow/inductor/160325 -> ciflow/inductor/160325 2025-09-07T06:25:54.8909285Z * [new tag] ciflow/inductor/160326 -> ciflow/inductor/160326 2025-09-07T06:25:54.8909976Z * [new tag] ciflow/inductor/160327 -> ciflow/inductor/160327 2025-09-07T06:25:54.8910747Z * [new tag] ciflow/inductor/160328 -> ciflow/inductor/160328 2025-09-07T06:25:54.8911715Z * [new tag] ciflow/inductor/160329 -> ciflow/inductor/160329 2025-09-07T06:25:54.8912333Z * [new tag] ciflow/inductor/160480 -> ciflow/inductor/160480 2025-09-07T06:25:54.8913327Z * [new tag] ciflow/inductor/160532 -> ciflow/inductor/160532 2025-09-07T06:25:54.8914688Z * [new tag] ciflow/inductor/160539 -> ciflow/inductor/160539 2025-09-07T06:25:54.8915498Z * [new tag] ciflow/inductor/160580 -> ciflow/inductor/160580 2025-09-07T06:25:54.8916142Z * [new tag] ciflow/inductor/160685 -> ciflow/inductor/160685 2025-09-07T06:25:54.8916828Z * [new tag] ciflow/inductor/160686 -> ciflow/inductor/160686 2025-09-07T06:25:54.8917544Z * [new tag] ciflow/inductor/160687 -> ciflow/inductor/160687 2025-09-07T06:25:54.8918240Z * [new tag] ciflow/inductor/160688 -> ciflow/inductor/160688 2025-09-07T06:25:54.8918967Z * [new tag] ciflow/inductor/160690 -> ciflow/inductor/160690 2025-09-07T06:25:54.8919670Z * [new tag] ciflow/inductor/160706 -> ciflow/inductor/160706 2025-09-07T06:25:54.8920494Z * [new tag] ciflow/inductor/160729 -> ciflow/inductor/160729 2025-09-07T06:25:54.8921224Z * [new tag] ciflow/inductor/160798 -> ciflow/inductor/160798 2025-09-07T06:25:54.8922080Z * [new tag] ciflow/inductor/160836 -> ciflow/inductor/160836 2025-09-07T06:25:54.8922771Z * [new tag] ciflow/inductor/160843 -> ciflow/inductor/160843 2025-09-07T06:25:54.8923947Z * [new tag] ciflow/inductor/160869 -> ciflow/inductor/160869 2025-09-07T06:25:54.8924540Z * [new tag] ciflow/inductor/160920 -> ciflow/inductor/160920 2025-09-07T06:25:54.8925543Z * [new tag] ciflow/inductor/160943 -> ciflow/inductor/160943 2025-09-07T06:25:54.8926209Z * [new tag] ciflow/inductor/161092 -> ciflow/inductor/161092 2025-09-07T06:25:54.8926876Z * [new tag] ciflow/inductor/161093 -> ciflow/inductor/161093 2025-09-07T06:25:54.8927914Z * [new tag] ciflow/inductor/161109 -> ciflow/inductor/161109 2025-09-07T06:25:54.8928473Z * [new tag] ciflow/inductor/161118 -> ciflow/inductor/161118 2025-09-07T06:25:54.8929390Z * [new tag] ciflow/inductor/161178 -> ciflow/inductor/161178 2025-09-07T06:25:54.8930113Z * [new tag] ciflow/inductor/161246 -> ciflow/inductor/161246 2025-09-07T06:25:54.8930747Z * [new tag] ciflow/inductor/161349 -> ciflow/inductor/161349 2025-09-07T06:25:54.8931483Z * [new tag] ciflow/inductor/161350 -> ciflow/inductor/161350 2025-09-07T06:25:54.8932206Z * [new tag] ciflow/inductor/161351 -> ciflow/inductor/161351 2025-09-07T06:25:54.8933152Z * [new tag] ciflow/inductor/161397 -> ciflow/inductor/161397 2025-09-07T06:25:54.8933840Z * [new tag] ciflow/inductor/161404 -> ciflow/inductor/161404 2025-09-07T06:25:54.8934587Z * [new tag] ciflow/inductor/161405 -> ciflow/inductor/161405 2025-09-07T06:25:54.8935339Z * [new tag] ciflow/inductor/161406 -> ciflow/inductor/161406 2025-09-07T06:25:54.8936303Z * [new tag] ciflow/inductor/161410 -> ciflow/inductor/161410 2025-09-07T06:25:54.8936954Z * [new tag] ciflow/inductor/161414 -> ciflow/inductor/161414 2025-09-07T06:25:54.8937920Z * [new tag] ciflow/inductor/161442 -> ciflow/inductor/161442 2025-09-07T06:25:54.8938629Z * [new tag] ciflow/inductor/161458 -> ciflow/inductor/161458 2025-09-07T06:25:54.8939322Z * [new tag] ciflow/inductor/161468 -> ciflow/inductor/161468 2025-09-07T06:25:54.8940037Z * [new tag] ciflow/inductor/161469 -> ciflow/inductor/161469 2025-09-07T06:25:54.8941416Z * [new tag] ciflow/inductor/161485 -> ciflow/inductor/161485 2025-09-07T06:25:54.8942091Z * [new tag] ciflow/inductor/161499 -> ciflow/inductor/161499 2025-09-07T06:25:54.8942783Z * [new tag] ciflow/inductor/161534 -> ciflow/inductor/161534 2025-09-07T06:25:54.8943504Z * [new tag] ciflow/inductor/161595 -> ciflow/inductor/161595 2025-09-07T06:25:54.8944226Z * [new tag] ciflow/inductor/161596 -> ciflow/inductor/161596 2025-09-07T06:25:54.8945597Z * [new tag] ciflow/inductor/161630 -> ciflow/inductor/161630 2025-09-07T06:25:54.8946207Z * [new tag] ciflow/inductor/161667 -> ciflow/inductor/161667 2025-09-07T06:25:54.8946873Z * [new tag] ciflow/inductor/161670 -> ciflow/inductor/161670 2025-09-07T06:25:54.8947628Z * [new tag] ciflow/inductor/161673 -> ciflow/inductor/161673 2025-09-07T06:25:54.8948350Z * [new tag] ciflow/inductor/161674 -> ciflow/inductor/161674 2025-09-07T06:25:54.8949121Z * [new tag] ciflow/inductor/161675 -> ciflow/inductor/161675 2025-09-07T06:25:54.8949783Z * [new tag] ciflow/inductor/161693 -> ciflow/inductor/161693 2025-09-07T06:25:54.8950751Z * [new tag] ciflow/inductor/161695 -> ciflow/inductor/161695 2025-09-07T06:25:54.8951306Z * [new tag] ciflow/inductor/161715 -> ciflow/inductor/161715 2025-09-07T06:25:54.8952047Z * [new tag] ciflow/inductor/161730 -> ciflow/inductor/161730 2025-09-07T06:25:54.8952765Z * [new tag] ciflow/inductor/161732 -> ciflow/inductor/161732 2025-09-07T06:25:54.8953668Z * [new tag] ciflow/inductor/161744 -> ciflow/inductor/161744 2025-09-07T06:25:54.8954292Z * [new tag] ciflow/inductor/161746 -> ciflow/inductor/161746 2025-09-07T06:25:54.8955037Z * [new tag] ciflow/inductor/161747 -> ciflow/inductor/161747 2025-09-07T06:25:54.8955789Z * [new tag] ciflow/inductor/161819 -> ciflow/inductor/161819 2025-09-07T06:25:54.8956507Z * [new tag] ciflow/inductor/161821 -> ciflow/inductor/161821 2025-09-07T06:25:54.8957209Z * [new tag] ciflow/inductor/161828 -> ciflow/inductor/161828 2025-09-07T06:25:54.8957919Z * [new tag] ciflow/inductor/161879 -> ciflow/inductor/161879 2025-09-07T06:25:54.8958659Z * [new tag] ciflow/inductor/161880 -> ciflow/inductor/161880 2025-09-07T06:25:54.8959364Z * [new tag] ciflow/inductor/161881 -> ciflow/inductor/161881 2025-09-07T06:25:54.8960374Z * [new tag] ciflow/inductor/161907 -> ciflow/inductor/161907 2025-09-07T06:25:54.8960984Z * [new tag] ciflow/inductor/161914 -> ciflow/inductor/161914 2025-09-07T06:25:54.8961952Z * [new tag] ciflow/inductor/161924 -> ciflow/inductor/161924 2025-09-07T06:25:54.8962687Z * [new tag] ciflow/inductor/161936 -> ciflow/inductor/161936 2025-09-07T06:25:54.8963399Z * [new tag] ciflow/inductor/161938 -> ciflow/inductor/161938 2025-09-07T06:25:54.8964238Z * [new tag] ciflow/inductor/161939 -> ciflow/inductor/161939 2025-09-07T06:25:54.8964984Z * [new tag] ciflow/inductor/161940 -> ciflow/inductor/161940 2025-09-07T06:25:54.8965721Z * [new tag] ciflow/inductor/161955 -> ciflow/inductor/161955 2025-09-07T06:25:54.8966424Z * [new tag] ciflow/inductor/161957 -> ciflow/inductor/161957 2025-09-07T06:25:54.8967164Z * [new tag] ciflow/inductor/161975 -> ciflow/inductor/161975 2025-09-07T06:25:54.8967862Z * [new tag] ciflow/inductor/161977 -> ciflow/inductor/161977 2025-09-07T06:25:54.8968571Z * [new tag] ciflow/inductor/161978 -> ciflow/inductor/161978 2025-09-07T06:25:54.8969282Z * [new tag] ciflow/inductor/161979 -> ciflow/inductor/161979 2025-09-07T06:25:54.8969993Z * [new tag] ciflow/inductor/161980 -> ciflow/inductor/161980 2025-09-07T06:25:54.8970710Z * [new tag] ciflow/inductor/161988 -> ciflow/inductor/161988 2025-09-07T06:25:54.8971474Z * [new tag] ciflow/inductor/161994 -> ciflow/inductor/161994 2025-09-07T06:25:54.8972198Z * [new tag] ciflow/inductor/162013 -> ciflow/inductor/162013 2025-09-07T06:25:54.8972883Z * [new tag] ciflow/inductor/162014 -> ciflow/inductor/162014 2025-09-07T06:25:54.8973755Z * [new tag] ciflow/inductor/162017 -> ciflow/inductor/162017 2025-09-07T06:25:54.8974765Z * [new tag] ciflow/inductor/162021 -> ciflow/inductor/162021 2025-09-07T06:25:54.8975776Z * [new tag] ciflow/inductor/162023 -> ciflow/inductor/162023 2025-09-07T06:25:54.8976389Z * [new tag] ciflow/inductor/162027 -> ciflow/inductor/162027 2025-09-07T06:25:54.8977241Z * [new tag] ciflow/inductor/162029 -> ciflow/inductor/162029 2025-09-07T06:25:54.8977887Z * [new tag] ciflow/inductor/162030 -> ciflow/inductor/162030 2025-09-07T06:25:54.8978583Z * [new tag] ciflow/inductor/162031 -> ciflow/inductor/162031 2025-09-07T06:25:54.8979296Z * [new tag] ciflow/inductor/162033 -> ciflow/inductor/162033 2025-09-07T06:25:54.8980325Z * [new tag] ciflow/inductor/162052 -> ciflow/inductor/162052 2025-09-07T06:25:54.8980959Z * [new tag] ciflow/inductor/162053 -> ciflow/inductor/162053 2025-09-07T06:25:54.8981648Z * [new tag] ciflow/inductor/162056 -> ciflow/inductor/162056 2025-09-07T06:25:54.8982382Z * [new tag] ciflow/inductor/162063 -> ciflow/inductor/162063 2025-09-07T06:25:54.8983119Z * [new tag] ciflow/inductor/162066 -> ciflow/inductor/162066 2025-09-07T06:25:54.8983872Z * [new tag] ciflow/inductor/162068 -> ciflow/inductor/162068 2025-09-07T06:25:54.8984817Z * [new tag] ciflow/inductor/162081 -> ciflow/inductor/162081 2025-09-07T06:25:54.8985467Z * [new tag] ciflow/inductor/162088 -> ciflow/inductor/162088 2025-09-07T06:25:54.8986185Z * [new tag] ciflow/inductor/162089 -> ciflow/inductor/162089 2025-09-07T06:25:54.8986960Z * [new tag] ciflow/inductor/162094 -> ciflow/inductor/162094 2025-09-07T06:25:54.8987659Z * [new tag] ciflow/inductor/162098 -> ciflow/inductor/162098 2025-09-07T06:25:54.8988376Z * [new tag] ciflow/inductor/162101 -> ciflow/inductor/162101 2025-09-07T06:25:54.8989117Z * [new tag] ciflow/inductor/162102 -> ciflow/inductor/162102 2025-09-07T06:25:54.8990294Z * [new tag] ciflow/inductor/162104 -> ciflow/inductor/162104 2025-09-07T06:25:54.8990995Z * [new tag] ciflow/inductor/162106 -> ciflow/inductor/162106 2025-09-07T06:25:54.8991726Z * [new tag] ciflow/inductor/162108 -> ciflow/inductor/162108 2025-09-07T06:25:54.8992450Z * [new tag] ciflow/inductor/162126 -> ciflow/inductor/162126 2025-09-07T06:25:54.8993189Z * [new tag] ciflow/inductor/162149 -> ciflow/inductor/162149 2025-09-07T06:25:54.8993949Z * [new tag] ciflow/inductor/162164 -> ciflow/inductor/162164 2025-09-07T06:25:54.8994641Z * [new tag] ciflow/inductor/162166 -> ciflow/inductor/162166 2025-09-07T06:25:54.8995404Z * [new tag] ciflow/inductor/162169 -> ciflow/inductor/162169 2025-09-07T06:25:54.8996069Z * [new tag] ciflow/inductor/162170 -> ciflow/inductor/162170 2025-09-07T06:25:54.8996809Z * [new tag] ciflow/inductor/162171 -> ciflow/inductor/162171 2025-09-07T06:25:54.8997516Z * [new tag] ciflow/inductor/162183 -> ciflow/inductor/162183 2025-09-07T06:25:54.8998222Z * [new tag] ciflow/inductor/162189 -> ciflow/inductor/162189 2025-09-07T06:25:54.8999039Z * [new tag] ciflow/inductor/162190 -> ciflow/inductor/162190 2025-09-07T06:25:54.8999750Z * [new tag] ciflow/inductor/162191 -> ciflow/inductor/162191 2025-09-07T06:25:54.9000482Z * [new tag] ciflow/inductor/162194 -> ciflow/inductor/162194 2025-09-07T06:25:54.9001389Z * [new tag] ciflow/inductor/162200 -> ciflow/inductor/162200 2025-09-07T06:25:54.9002126Z * [new tag] ciflow/inductor/162201 -> ciflow/inductor/162201 2025-09-07T06:25:54.9002843Z * [new tag] ciflow/inductor/162208 -> ciflow/inductor/162208 2025-09-07T06:25:54.9003681Z * [new tag] ciflow/inductor/162211 -> ciflow/inductor/162211 2025-09-07T06:25:54.9004555Z * [new tag] ciflow/inductor/162216 -> ciflow/inductor/162216 2025-09-07T06:25:54.9005278Z * [new tag] ciflow/inductor/162220 -> ciflow/inductor/162220 2025-09-07T06:25:54.9006137Z * [new tag] ciflow/inductor/162222 -> ciflow/inductor/162222 2025-09-07T06:25:54.9006885Z * [new tag] ciflow/inductor/162227 -> ciflow/inductor/162227 2025-09-07T06:25:54.9007611Z * [new tag] ciflow/inductor/162238 -> ciflow/inductor/162238 2025-09-07T06:25:54.9008329Z * [new tag] ciflow/inductor/162239 -> ciflow/inductor/162239 2025-09-07T06:25:54.9009028Z * [new tag] ciflow/inductor/162240 -> ciflow/inductor/162240 2025-09-07T06:25:54.9009755Z * [new tag] ciflow/inductor/162244 -> ciflow/inductor/162244 2025-09-07T06:25:54.9010463Z * [new tag] ciflow/inductor/162245 -> ciflow/inductor/162245 2025-09-07T06:25:54.9011219Z * [new tag] ciflow/inductor/162262 -> ciflow/inductor/162262 2025-09-07T06:25:54.9011912Z * [new tag] ciflow/inductor/162275 -> ciflow/inductor/162275 2025-09-07T06:25:54.9012626Z * [new tag] ciflow/inductor/162278 -> ciflow/inductor/162278 2025-09-07T06:25:54.9013358Z * [new tag] ciflow/inductor/162284 -> ciflow/inductor/162284 2025-09-07T06:25:54.9014079Z * [new tag] ciflow/inductor/162286 -> ciflow/inductor/162286 2025-09-07T06:25:54.9014794Z * [new tag] ciflow/inductor/162288 -> ciflow/inductor/162288 2025-09-07T06:25:54.9015517Z * [new tag] ciflow/inductor/162293 -> ciflow/inductor/162293 2025-09-07T06:25:54.9016244Z * [new tag] ciflow/inductor/162294 -> ciflow/inductor/162294 2025-09-07T06:25:54.9016980Z * [new tag] ciflow/inductor/162295 -> ciflow/inductor/162295 2025-09-07T06:25:54.9017699Z * [new tag] ciflow/inductor/162296 -> ciflow/inductor/162296 2025-09-07T06:25:54.9018434Z * [new tag] ciflow/inductor/162298 -> ciflow/inductor/162298 2025-09-07T06:25:54.9019169Z * [new tag] ciflow/inductor/162307 -> ciflow/inductor/162307 2025-09-07T06:25:54.9019881Z * [new tag] ciflow/inductor/162309 -> ciflow/inductor/162309 2025-09-07T06:25:54.9020580Z * [new tag] ciflow/inductor/162311 -> ciflow/inductor/162311 2025-09-07T06:25:54.9021362Z * [new tag] ciflow/inductor/162312 -> ciflow/inductor/162312 2025-09-07T06:25:54.9022086Z * [new tag] ciflow/inductor/162315 -> ciflow/inductor/162315 2025-09-07T06:25:54.9022880Z * [new tag] ciflow/inductor/162316 -> ciflow/inductor/162316 2025-09-07T06:25:54.9023565Z * [new tag] ciflow/inductor/162318 -> ciflow/inductor/162318 2025-09-07T06:25:54.9024311Z * [new tag] ciflow/inductor/162323 -> ciflow/inductor/162323 2025-09-07T06:25:54.9025012Z * [new tag] ciflow/inductor/162341 -> ciflow/inductor/162341 2025-09-07T06:25:54.9025743Z * [new tag] ciflow/inductor/162345 -> ciflow/inductor/162345 2025-09-07T06:25:54.9026789Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-09-07T06:25:54.9027620Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-09-07T06:25:54.9028457Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-09-07T06:25:54.9029337Z * [new tag] ciflow/linux-aarch64/157994 -> ciflow/linux-aarch64/157994 2025-09-07T06:25:54.9030040Z * [new tag] ciflow/linux-aarch64/159737 -> ciflow/linux-aarch64/159737 2025-09-07T06:25:54.9030700Z * [new tag] ciflow/linux-aarch64/160078 -> ciflow/linux-aarch64/160078 2025-09-07T06:25:54.9032056Z * [new tag] ciflow/mps/157553 -> ciflow/mps/157553 2025-09-07T06:25:54.9032763Z * [new tag] ciflow/mps/157635 -> ciflow/mps/157635 2025-09-07T06:25:54.9033261Z * [new tag] ciflow/mps/161988 -> ciflow/mps/161988 2025-09-07T06:25:54.9033994Z * [new tag] ciflow/mps/162108 -> ciflow/mps/162108 2025-09-07T06:25:54.9034679Z * [new tag] ciflow/mps/162153 -> ciflow/mps/162153 2025-09-07T06:25:54.9035326Z * [new tag] ciflow/mps/162281 -> ciflow/mps/162281 2025-09-07T06:25:54.9036349Z * [new tag] ciflow/nightly/156049 -> ciflow/nightly/156049 2025-09-07T06:25:54.9036913Z * [new tag] ciflow/nightly/158104 -> ciflow/nightly/158104 2025-09-07T06:25:54.9037815Z * [new tag] ciflow/op-benchmark/157994 -> ciflow/op-benchmark/157994 2025-09-07T06:25:54.9038894Z * [new tag] ciflow/periodic-rocm-mi300/161529 -> ciflow/periodic-rocm-mi300/161529 2025-09-07T06:25:54.9039470Z * [new tag] ciflow/periodic-rocm-mi300/161715 -> ciflow/periodic-rocm-mi300/161715 2025-09-07T06:25:54.9040528Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-09-07T06:25:54.9041106Z * [new tag] ciflow/periodic/156703 -> ciflow/periodic/156703 2025-09-07T06:25:54.9041779Z * [new tag] ciflow/periodic/161715 -> ciflow/periodic/161715 2025-09-07T06:25:54.9042518Z * [new tag] ciflow/periodic/162021 -> ciflow/periodic/162021 2025-09-07T06:25:54.9043233Z * [new tag] ciflow/periodic/162323 -> ciflow/periodic/162323 2025-09-07T06:25:54.9044232Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-09-07T06:25:54.9045027Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-09-07T06:25:54.9045956Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-09-07T06:25:54.9046753Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-09-07T06:25:54.9047755Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-09-07T06:25:54.9048855Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-09-07T06:25:54.9049993Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-09-07T06:25:54.9051095Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-09-07T06:25:54.9052024Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-09-07T06:25:54.9053048Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-09-07T06:25:54.9054004Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-09-07T06:25:54.9054791Z * [new tag] ciflow/rocm-mi300/154170 -> ciflow/rocm-mi300/154170 2025-09-07T06:25:54.9055730Z * [new tag] ciflow/rocm-mi300/158747 -> ciflow/rocm-mi300/158747 2025-09-07T06:25:54.9056280Z * [new tag] ciflow/rocm-mi300/159146 -> ciflow/rocm-mi300/159146 2025-09-07T06:25:54.9056952Z * [new tag] ciflow/rocm-mi300/159158 -> ciflow/rocm-mi300/159158 2025-09-07T06:25:54.9057642Z * [new tag] ciflow/rocm-mi300/161715 -> ciflow/rocm-mi300/161715 2025-09-07T06:25:54.9058284Z * [new tag] ciflow/rocm-mi300/161957 -> ciflow/rocm-mi300/161957 2025-09-07T06:25:54.9058975Z * [new tag] ciflow/rocm-mi300/162053 -> ciflow/rocm-mi300/162053 2025-09-07T06:25:54.9059675Z * [new tag] ciflow/rocm-mi300/162056 -> ciflow/rocm-mi300/162056 2025-09-07T06:25:54.9060606Z * [new tag] ciflow/rocm-mi300/162112 -> ciflow/rocm-mi300/162112 2025-09-07T06:25:54.9061252Z * [new tag] ciflow/rocm-mi300/162245 -> ciflow/rocm-mi300/162245 2025-09-07T06:25:54.9061982Z * [new tag] ciflow/rocm-mi300/162278 -> ciflow/rocm-mi300/162278 2025-09-07T06:25:54.9062824Z * [new tag] ciflow/rocm-mi300/162288 -> ciflow/rocm-mi300/162288 2025-09-07T06:25:54.9063845Z * [new tag] ciflow/rocm-mi355/162053 -> ciflow/rocm-mi355/162053 2025-09-07T06:25:54.9064359Z * [new tag] ciflow/rocm-mi355/162056 -> ciflow/rocm-mi355/162056 2025-09-07T06:25:54.9065100Z * [new tag] ciflow/rocm/148492 -> ciflow/rocm/148492 2025-09-07T06:25:54.9065771Z * [new tag] ciflow/rocm/154170 -> ciflow/rocm/154170 2025-09-07T06:25:54.9066849Z * [new tag] ciflow/rocm/156491 -> ciflow/rocm/156491 2025-09-07T06:25:54.9067449Z * [new tag] ciflow/rocm/156592 -> ciflow/rocm/156592 2025-09-07T06:25:54.9068072Z * [new tag] ciflow/rocm/158747 -> ciflow/rocm/158747 2025-09-07T06:25:54.9068765Z * [new tag] ciflow/rocm/159146 -> ciflow/rocm/159146 2025-09-07T06:25:54.9069802Z * [new tag] ciflow/rocm/159158 -> ciflow/rocm/159158 2025-09-07T06:25:54.9070378Z * [new tag] ciflow/rocm/161715 -> ciflow/rocm/161715 2025-09-07T06:25:54.9071184Z * [new tag] ciflow/rocm/161972 -> ciflow/rocm/161972 2025-09-07T06:25:54.9071824Z * [new tag] ciflow/rocm/162052 -> ciflow/rocm/162052 2025-09-07T06:25:54.9072543Z * [new tag] ciflow/rocm/162053 -> ciflow/rocm/162053 2025-09-07T06:25:54.9073665Z * [new tag] ciflow/rocm/162056 -> ciflow/rocm/162056 2025-09-07T06:25:54.9074657Z * [new tag] ciflow/rocm/162112 -> ciflow/rocm/162112 2025-09-07T06:25:54.9075407Z * [new tag] ciflow/rocm/162278 -> ciflow/rocm/162278 2025-09-07T06:25:54.9076165Z * [new tag] ciflow/rocm/162288 -> ciflow/rocm/162288 2025-09-07T06:25:54.9076796Z * [new tag] ciflow/rocm/162305 -> ciflow/rocm/162305 2025-09-07T06:25:54.9077913Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-09-07T06:25:54.9078648Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-09-07T06:25:54.9080016Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-09-07T06:25:54.9080456Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-09-07T06:25:54.9081214Z * [new tag] ciflow/slow/161395 -> ciflow/slow/161395 2025-09-07T06:25:54.9082147Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-09-07T06:25:54.9082919Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-09-07T06:25:54.9083815Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-09-07T06:25:54.9085064Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-09-07T06:25:54.9086126Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-09-07T06:25:54.9086900Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-09-07T06:25:54.9087854Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-09-07T06:25:54.9088610Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-09-07T06:25:54.9090020Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-09-07T06:25:54.9090465Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-09-07T06:25:54.9091262Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-09-07T06:25:54.9092357Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-09-07T06:25:54.9093119Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-09-07T06:25:54.9094135Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-09-07T06:25:54.9095358Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-09-07T06:25:54.9095994Z * [new tag] ciflow/triton_binaries/162329 -> ciflow/triton_binaries/162329 2025-09-07T06:25:54.9096701Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-09-07T06:25:54.9097391Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-09-07T06:25:54.9098024Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-09-07T06:25:54.9098703Z * [new tag] ciflow/trunk/148328 -> ciflow/trunk/148328 2025-09-07T06:25:54.9099359Z * [new tag] ciflow/trunk/148492 -> ciflow/trunk/148492 2025-09-07T06:25:54.9100342Z * [new tag] ciflow/trunk/148919 -> ciflow/trunk/148919 2025-09-07T06:25:54.9101477Z * [new tag] ciflow/trunk/152624 -> ciflow/trunk/152624 2025-09-07T06:25:54.9102017Z * [new tag] ciflow/trunk/154170 -> ciflow/trunk/154170 2025-09-07T06:25:54.9102722Z * [new tag] ciflow/trunk/154694 -> ciflow/trunk/154694 2025-09-07T06:25:54.9103391Z * [new tag] ciflow/trunk/156049 -> ciflow/trunk/156049 2025-09-07T06:25:54.9104085Z * [new tag] ciflow/trunk/156703 -> ciflow/trunk/156703 2025-09-07T06:25:54.9105073Z * [new tag] ciflow/trunk/156711 -> ciflow/trunk/156711 2025-09-07T06:25:54.9106023Z * [new tag] ciflow/trunk/157432 -> ciflow/trunk/157432 2025-09-07T06:25:54.9106795Z * [new tag] ciflow/trunk/157685 -> ciflow/trunk/157685 2025-09-07T06:25:54.9107519Z * [new tag] ciflow/trunk/157689 -> ciflow/trunk/157689 2025-09-07T06:25:54.9108220Z * [new tag] ciflow/trunk/157699 -> ciflow/trunk/157699 2025-09-07T06:25:54.9108923Z * [new tag] ciflow/trunk/157813 -> ciflow/trunk/157813 2025-09-07T06:25:54.9109629Z * [new tag] ciflow/trunk/157994 -> ciflow/trunk/157994 2025-09-07T06:25:54.9110330Z * [new tag] ciflow/trunk/158091 -> ciflow/trunk/158091 2025-09-07T06:25:54.9111019Z * [new tag] ciflow/trunk/158104 -> ciflow/trunk/158104 2025-09-07T06:25:54.9111701Z * [new tag] ciflow/trunk/158404 -> ciflow/trunk/158404 2025-09-07T06:25:54.9112485Z * [new tag] ciflow/trunk/158647 -> ciflow/trunk/158647 2025-09-07T06:25:54.9113517Z * [new tag] ciflow/trunk/158846 -> ciflow/trunk/158846 2025-09-07T06:25:54.9114111Z * [new tag] ciflow/trunk/159158 -> ciflow/trunk/159158 2025-09-07T06:25:54.9115032Z * [new tag] ciflow/trunk/159682 -> ciflow/trunk/159682 2025-09-07T06:25:54.9115635Z * [new tag] ciflow/trunk/159835 -> ciflow/trunk/159835 2025-09-07T06:25:54.9116527Z * [new tag] ciflow/trunk/160161 -> ciflow/trunk/160161 2025-09-07T06:25:54.9117140Z * [new tag] ciflow/trunk/160236 -> ciflow/trunk/160236 2025-09-07T06:25:54.9117902Z * [new tag] ciflow/trunk/160329 -> ciflow/trunk/160329 2025-09-07T06:25:54.9118570Z * [new tag] ciflow/trunk/160480 -> ciflow/trunk/160480 2025-09-07T06:25:54.9119295Z * [new tag] ciflow/trunk/160532 -> ciflow/trunk/160532 2025-09-07T06:25:54.9119989Z * [new tag] ciflow/trunk/160836 -> ciflow/trunk/160836 2025-09-07T06:25:54.9120803Z * [new tag] ciflow/trunk/160843 -> ciflow/trunk/160843 2025-09-07T06:25:54.9121451Z * [new tag] ciflow/trunk/160869 -> ciflow/trunk/160869 2025-09-07T06:25:54.9122370Z * [new tag] ciflow/trunk/160940 -> ciflow/trunk/160940 2025-09-07T06:25:54.9123115Z * [new tag] ciflow/trunk/160943 -> ciflow/trunk/160943 2025-09-07T06:25:54.9124106Z * [new tag] ciflow/trunk/160953 -> ciflow/trunk/160953 2025-09-07T06:25:54.9124895Z * [new tag] ciflow/trunk/161035 -> ciflow/trunk/161035 2025-09-07T06:25:54.9125623Z * [new tag] ciflow/trunk/161178 -> ciflow/trunk/161178 2025-09-07T06:25:54.9126303Z * [new tag] ciflow/trunk/161349 -> ciflow/trunk/161349 2025-09-07T06:25:54.9127056Z * [new tag] ciflow/trunk/161350 -> ciflow/trunk/161350 2025-09-07T06:25:54.9127802Z * [new tag] ciflow/trunk/161351 -> ciflow/trunk/161351 2025-09-07T06:25:54.9128521Z * [new tag] ciflow/trunk/161395 -> ciflow/trunk/161395 2025-09-07T06:25:54.9129222Z * [new tag] ciflow/trunk/161405 -> ciflow/trunk/161405 2025-09-07T06:25:54.9129957Z * [new tag] ciflow/trunk/161406 -> ciflow/trunk/161406 2025-09-07T06:25:54.9130622Z * [new tag] ciflow/trunk/161410 -> ciflow/trunk/161410 2025-09-07T06:25:54.9131326Z * [new tag] ciflow/trunk/161468 -> ciflow/trunk/161468 2025-09-07T06:25:54.9132015Z * [new tag] ciflow/trunk/161499 -> ciflow/trunk/161499 2025-09-07T06:25:54.9133098Z * [new tag] ciflow/trunk/161527 -> ciflow/trunk/161527 2025-09-07T06:25:54.9133712Z * [new tag] ciflow/trunk/161534 -> ciflow/trunk/161534 2025-09-07T06:25:54.9134425Z * [new tag] ciflow/trunk/161591 -> ciflow/trunk/161591 2025-09-07T06:25:54.9135169Z * [new tag] ciflow/trunk/161595 -> ciflow/trunk/161595 2025-09-07T06:25:54.9135881Z * [new tag] ciflow/trunk/161596 -> ciflow/trunk/161596 2025-09-07T06:25:54.9136588Z * [new tag] ciflow/trunk/161633 -> ciflow/trunk/161633 2025-09-07T06:25:54.9137302Z * [new tag] ciflow/trunk/161634 -> ciflow/trunk/161634 2025-09-07T06:25:54.9138004Z * [new tag] ciflow/trunk/161635 -> ciflow/trunk/161635 2025-09-07T06:25:54.9138715Z * [new tag] ciflow/trunk/161667 -> ciflow/trunk/161667 2025-09-07T06:25:54.9139400Z * [new tag] ciflow/trunk/161670 -> ciflow/trunk/161670 2025-09-07T06:25:54.9140299Z * [new tag] ciflow/trunk/161692 -> ciflow/trunk/161692 2025-09-07T06:25:54.9140869Z * [new tag] ciflow/trunk/161693 -> ciflow/trunk/161693 2025-09-07T06:25:54.9141677Z * [new tag] ciflow/trunk/161695 -> ciflow/trunk/161695 2025-09-07T06:25:54.9142339Z * [new tag] ciflow/trunk/161730 -> ciflow/trunk/161730 2025-09-07T06:25:54.9143065Z * [new tag] ciflow/trunk/161744 -> ciflow/trunk/161744 2025-09-07T06:25:54.9143755Z * [new tag] ciflow/trunk/161749 -> ciflow/trunk/161749 2025-09-07T06:25:54.9144511Z * [new tag] ciflow/trunk/161881 -> ciflow/trunk/161881 2025-09-07T06:25:54.9145189Z * [new tag] ciflow/trunk/161924 -> ciflow/trunk/161924 2025-09-07T06:25:54.9146145Z * [new tag] ciflow/trunk/161926 -> ciflow/trunk/161926 2025-09-07T06:25:54.9146757Z * [new tag] ciflow/trunk/161936 -> ciflow/trunk/161936 2025-09-07T06:25:54.9147496Z * [new tag] ciflow/trunk/161952 -> ciflow/trunk/161952 2025-09-07T06:25:54.9148280Z * [new tag] ciflow/trunk/161955 -> ciflow/trunk/161955 2025-09-07T06:25:54.9149390Z * [new tag] ciflow/trunk/161957 -> ciflow/trunk/161957 2025-09-07T06:25:54.9150021Z * [new tag] ciflow/trunk/161959 -> ciflow/trunk/161959 2025-09-07T06:25:54.9150748Z * [new tag] ciflow/trunk/161977 -> ciflow/trunk/161977 2025-09-07T06:25:54.9151483Z * [new tag] ciflow/trunk/161988 -> ciflow/trunk/161988 2025-09-07T06:25:54.9152192Z * [new tag] ciflow/trunk/161994 -> ciflow/trunk/161994 2025-09-07T06:25:54.9153117Z * [new tag] ciflow/trunk/162007 -> ciflow/trunk/162007 2025-09-07T06:25:54.9153850Z * [new tag] ciflow/trunk/162013 -> ciflow/trunk/162013 2025-09-07T06:25:54.9154525Z * [new tag] ciflow/trunk/162017 -> ciflow/trunk/162017 2025-09-07T06:25:54.9155197Z * [new tag] ciflow/trunk/162021 -> ciflow/trunk/162021 2025-09-07T06:25:54.9155937Z * [new tag] ciflow/trunk/162022 -> ciflow/trunk/162022 2025-09-07T06:25:54.9156665Z * [new tag] ciflow/trunk/162040 -> ciflow/trunk/162040 2025-09-07T06:25:54.9157368Z * [new tag] ciflow/trunk/162041 -> ciflow/trunk/162041 2025-09-07T06:25:54.9158187Z * [new tag] ciflow/trunk/162062 -> ciflow/trunk/162062 2025-09-07T06:25:54.9158908Z * [new tag] ciflow/trunk/162066 -> ciflow/trunk/162066 2025-09-07T06:25:54.9159646Z * [new tag] ciflow/trunk/162089 -> ciflow/trunk/162089 2025-09-07T06:25:54.9160370Z * [new tag] ciflow/trunk/162099 -> ciflow/trunk/162099 2025-09-07T06:25:54.9161096Z * [new tag] ciflow/trunk/162104 -> ciflow/trunk/162104 2025-09-07T06:25:54.9161775Z * [new tag] ciflow/trunk/162106 -> ciflow/trunk/162106 2025-09-07T06:25:54.9162499Z * [new tag] ciflow/trunk/162112 -> ciflow/trunk/162112 2025-09-07T06:25:54.9163186Z * [new tag] ciflow/trunk/162119 -> ciflow/trunk/162119 2025-09-07T06:25:54.9164028Z * [new tag] ciflow/trunk/162142 -> ciflow/trunk/162142 2025-09-07T06:25:54.9164835Z * [new tag] ciflow/trunk/162169 -> ciflow/trunk/162169 2025-09-07T06:25:54.9165584Z * [new tag] ciflow/trunk/162183 -> ciflow/trunk/162183 2025-09-07T06:25:54.9166237Z * [new tag] ciflow/trunk/162190 -> ciflow/trunk/162190 2025-09-07T06:25:54.9166895Z * [new tag] ciflow/trunk/162194 -> ciflow/trunk/162194 2025-09-07T06:25:54.9167672Z * [new tag] ciflow/trunk/162200 -> ciflow/trunk/162200 2025-09-07T06:25:54.9168408Z * [new tag] ciflow/trunk/162206 -> ciflow/trunk/162206 2025-09-07T06:25:54.9169127Z * [new tag] ciflow/trunk/162208 -> ciflow/trunk/162208 2025-09-07T06:25:54.9169856Z * [new tag] ciflow/trunk/162222 -> ciflow/trunk/162222 2025-09-07T06:25:54.9170559Z * [new tag] ciflow/trunk/162238 -> ciflow/trunk/162238 2025-09-07T06:25:54.9171261Z * [new tag] ciflow/trunk/162244 -> ciflow/trunk/162244 2025-09-07T06:25:54.9172387Z * [new tag] ciflow/trunk/162267 -> ciflow/trunk/162267 2025-09-07T06:25:54.9173111Z * [new tag] ciflow/trunk/162269 -> ciflow/trunk/162269 2025-09-07T06:25:54.9174025Z * [new tag] ciflow/trunk/162278 -> ciflow/trunk/162278 2025-09-07T06:25:54.9174735Z * [new tag] ciflow/trunk/162286 -> ciflow/trunk/162286 2025-09-07T06:25:54.9175485Z * [new tag] ciflow/trunk/162288 -> ciflow/trunk/162288 2025-09-07T06:25:54.9176151Z * [new tag] ciflow/trunk/162293 -> ciflow/trunk/162293 2025-09-07T06:25:54.9177033Z * [new tag] ciflow/trunk/162310 -> ciflow/trunk/162310 2025-09-07T06:25:54.9177608Z * [new tag] ciflow/trunk/162311 -> ciflow/trunk/162311 2025-09-07T06:25:54.9178309Z * [new tag] ciflow/trunk/162315 -> ciflow/trunk/162315 2025-09-07T06:25:54.9179033Z * [new tag] ciflow/trunk/162325 -> ciflow/trunk/162325 2025-09-07T06:25:54.9179972Z * [new tag] ciflow/trunk/162328 -> ciflow/trunk/162328 2025-09-07T06:25:54.9180635Z * [new tag] ciflow/trunk/162329 -> ciflow/trunk/162329 2025-09-07T06:25:54.9181757Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-09-07T06:25:54.9182518Z * [new tag] ciflow/vllm/162292 -> ciflow/vllm/162292 2025-09-07T06:25:54.9183373Z * [new tag] ciflow/win-arm64/156049 -> ciflow/win-arm64/156049 2025-09-07T06:25:54.9184029Z * [new tag] ciflow/win-arm64/158104 -> ciflow/win-arm64/158104 2025-09-07T06:25:54.9184843Z * [new tag] ciflow/xpu/157699 -> ciflow/xpu/157699 2025-09-07T06:25:54.9185630Z * [new tag] ciflow/xpu/157994 -> ciflow/xpu/157994 2025-09-07T06:25:54.9186465Z * [new tag] ciflow/xpu/159459 -> ciflow/xpu/159459 2025-09-07T06:25:54.9187112Z * [new tag] ciflow/xpu/159718 -> ciflow/xpu/159718 2025-09-07T06:25:54.9187795Z * [new tag] ciflow/xpu/159944 -> ciflow/xpu/159944 2025-09-07T06:25:54.9188541Z * [new tag] ciflow/xpu/160867 -> ciflow/xpu/160867 2025-09-07T06:25:54.9189354Z * [new tag] ciflow/xpu/160938 -> ciflow/xpu/160938 2025-09-07T06:25:54.9190044Z * [new tag] ciflow/xpu/160940 -> ciflow/xpu/160940 2025-09-07T06:25:54.9190716Z * [new tag] ciflow/xpu/160953 -> ciflow/xpu/160953 2025-09-07T06:25:54.9191433Z * [new tag] ciflow/xpu/161045 -> ciflow/xpu/161045 2025-09-07T06:25:54.9192384Z * [new tag] ciflow/xpu/161058 -> ciflow/xpu/161058 2025-09-07T06:25:54.9193316Z * [new tag] ciflow/xpu/161246 -> ciflow/xpu/161246 2025-09-07T06:25:54.9194298Z * [new tag] ciflow/xpu/161397 -> ciflow/xpu/161397 2025-09-07T06:25:54.9195061Z * [new tag] ciflow/xpu/161485 -> ciflow/xpu/161485 2025-09-07T06:25:54.9195809Z * [new tag] ciflow/xpu/161988 -> ciflow/xpu/161988 2025-09-07T06:25:54.9196483Z * [new tag] ciflow/xpu/162062 -> ciflow/xpu/162062 2025-09-07T06:25:54.9197437Z * [new tag] cslpull75 -> cslpull75 2025-09-07T06:25:54.9198113Z * [new tag] cslpull76 -> cslpull76 2025-09-07T06:25:54.9198879Z * [new tag] cslpull77 -> cslpull77 2025-09-07T06:25:54.9199634Z * [new tag] cslpull78 -> cslpull78 2025-09-07T06:25:54.9200715Z * [new tag] cslpull79 -> cslpull79 2025-09-07T06:25:54.9201793Z * [new tag] cslpull80 -> cslpull80 2025-09-07T06:25:54.9202678Z * [new tag] cslpull81 -> cslpull81 2025-09-07T06:25:54.9203425Z * [new tag] cslpull82 -> cslpull82 2025-09-07T06:25:54.9204334Z * [new tag] cslpull83 -> cslpull83 2025-09-07T06:25:54.9205233Z * [new tag] cslpull84 -> cslpull84 2025-09-07T06:25:54.9205909Z * [new tag] cslpull85 -> cslpull85 2025-09-07T06:25:54.9206894Z * [new tag] cslpull86 -> cslpull86 2025-09-07T06:25:54.9207758Z * [new tag] cslpull87 -> cslpull87 2025-09-07T06:25:54.9208601Z * [new tag] cslpull88 -> cslpull88 2025-09-07T06:25:54.9209301Z * [new tag] cslpull89 -> cslpull89 2025-09-07T06:25:54.9209943Z * [new tag] cslpull90 -> cslpull90 2025-09-07T06:25:54.9211287Z * [new tag] cslpull91 -> cslpull91 2025-09-07T06:25:54.9212141Z * [new tag] cslpull92 -> cslpull92 2025-09-07T06:25:54.9213005Z * [new tag] flight_5 -> flight_5 2025-09-07T06:25:54.9213984Z * [new tag] flight_5.1 -> flight_5.1 2025-09-07T06:25:54.9214846Z * [new tag] flight_5.2 -> flight_5.2 2025-09-07T06:25:54.9215577Z * [new tag] flight_5.3 -> flight_5.3 2025-09-07T06:25:54.9216440Z * [new tag] forpull1 -> forpull1 2025-09-07T06:25:54.9217562Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-09-07T06:25:54.9218295Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-09-07T06:25:54.9219243Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-09-07T06:25:54.9220012Z * [new tag] nightly-binary -> nightly-binary 2025-09-07T06:25:54.9220827Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-09-07T06:25:54.9221582Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-09-07T06:25:54.9222979Z * [new tag] trunk/00636e0171e7e733628c408084805442270cf608 -> trunk/00636e0171e7e733628c408084805442270cf608 2025-09-07T06:25:54.9223786Z * [new tag] trunk/019fed39aa6b2dd8c69347378d53423e5efae8d4 -> trunk/019fed39aa6b2dd8c69347378d53423e5efae8d4 2025-09-07T06:25:54.9224958Z * [new tag] trunk/01ab325cc2e0dc221af4d710974e1b9175066544 -> trunk/01ab325cc2e0dc221af4d710974e1b9175066544 2025-09-07T06:25:54.9225929Z * [new tag] trunk/01edcd4df8bf0c7b4cc2d3ec868bd2059eeea83b -> trunk/01edcd4df8bf0c7b4cc2d3ec868bd2059eeea83b 2025-09-07T06:25:54.9226832Z * [new tag] trunk/040d00af048967dde7938d358d7f5988cbd18388 -> trunk/040d00af048967dde7938d358d7f5988cbd18388 2025-09-07T06:25:54.9227707Z * [new tag] trunk/0447f2d99b4351b2ff129dce6eebb371024f73e5 -> trunk/0447f2d99b4351b2ff129dce6eebb371024f73e5 2025-09-07T06:25:54.9228676Z * [new tag] trunk/047603d35bdc70046216384838d6340feab79bf4 -> trunk/047603d35bdc70046216384838d6340feab79bf4 2025-09-07T06:25:54.9229556Z * [new tag] trunk/06da7c0730b3764f178ec3a90dedf4ffa4202d81 -> trunk/06da7c0730b3764f178ec3a90dedf4ffa4202d81 2025-09-07T06:25:54.9230704Z * [new tag] trunk/081cab045472ce045634548cc6c14a4870641e23 -> trunk/081cab045472ce045634548cc6c14a4870641e23 2025-09-07T06:25:54.9231491Z * [new tag] trunk/09587daf8c9f21f5340f73921ce5f23d1a4a4572 -> trunk/09587daf8c9f21f5340f73921ce5f23d1a4a4572 2025-09-07T06:25:54.9232319Z * [new tag] trunk/09be1890d72cc34fc946965dc4a27736bf0ca8c6 -> trunk/09be1890d72cc34fc946965dc4a27736bf0ca8c6 2025-09-07T06:25:54.9233141Z * [new tag] trunk/09d2f1b6315d6d416fbf452793d65795863ebc66 -> trunk/09d2f1b6315d6d416fbf452793d65795863ebc66 2025-09-07T06:25:54.9233973Z * [new tag] trunk/0af70e2353e1dcda83175fd4834ecb7b63e009e0 -> trunk/0af70e2353e1dcda83175fd4834ecb7b63e009e0 2025-09-07T06:25:54.9235542Z * [new tag] trunk/0c0e056a9e20c17271a6144dd32c0c7e3ba26736 -> trunk/0c0e056a9e20c17271a6144dd32c0c7e3ba26736 2025-09-07T06:25:54.9236306Z * [new tag] trunk/0cd6c56bdfa9178ff61be82ce3b178926ddb64a9 -> trunk/0cd6c56bdfa9178ff61be82ce3b178926ddb64a9 2025-09-07T06:25:54.9237152Z * [new tag] trunk/0d421ace32c1605ee8e452ee1eeb03bd243dd96c -> trunk/0d421ace32c1605ee8e452ee1eeb03bd243dd96c 2025-09-07T06:25:54.9238181Z * [new tag] trunk/0d71a9dd5b4b6d1dde58d91c9b71d96bc6a6a171 -> trunk/0d71a9dd5b4b6d1dde58d91c9b71d96bc6a6a171 2025-09-07T06:25:54.9239539Z * [new tag] trunk/0d84ff3b78f55492d3d4708458c92d776274939e -> trunk/0d84ff3b78f55492d3d4708458c92d776274939e 2025-09-07T06:25:54.9240370Z * [new tag] trunk/0f45aaf4414048b17d720d0915ce221a8de8ec63 -> trunk/0f45aaf4414048b17d720d0915ce221a8de8ec63 2025-09-07T06:25:54.9241248Z * [new tag] trunk/0ff8eabf1387de5acd6712a03bda61f1a3dfa27f -> trunk/0ff8eabf1387de5acd6712a03bda61f1a3dfa27f 2025-09-07T06:25:54.9242110Z * [new tag] trunk/104f2680e03d13a4765ca69f905d8f16fc0c822f -> trunk/104f2680e03d13a4765ca69f905d8f16fc0c822f 2025-09-07T06:25:54.9242945Z * [new tag] trunk/12814701555d3e41dfcdf8f9273af5821e322df0 -> trunk/12814701555d3e41dfcdf8f9273af5821e322df0 2025-09-07T06:25:54.9243855Z * [new tag] trunk/13b65196db422bdb394cb482e208c61ed448898c -> trunk/13b65196db422bdb394cb482e208c61ed448898c 2025-09-07T06:25:54.9244742Z * [new tag] trunk/13d66e2a66eceed14b8a8f5a971087df4f688a46 -> trunk/13d66e2a66eceed14b8a8f5a971087df4f688a46 2025-09-07T06:25:54.9245701Z * [new tag] trunk/145a3a7bda15e3963a33eb1b54bba5d4a270b225 -> trunk/145a3a7bda15e3963a33eb1b54bba5d4a270b225 2025-09-07T06:25:54.9246554Z * [new tag] trunk/146371483318e17929daefd37c8e459d9d6d47bb -> trunk/146371483318e17929daefd37c8e459d9d6d47bb 2025-09-07T06:25:54.9247394Z * [new tag] trunk/15c77a8cfd341e74fd124b077492ef2bfa51b339 -> trunk/15c77a8cfd341e74fd124b077492ef2bfa51b339 2025-09-07T06:25:54.9248253Z * [new tag] trunk/17fa8eec4a1e32939ab4d364ee6e75487a79b654 -> trunk/17fa8eec4a1e32939ab4d364ee6e75487a79b654 2025-09-07T06:25:54.9249769Z * [new tag] trunk/190c391a28845a14df26abb228d26aa813efb20c -> trunk/190c391a28845a14df26abb228d26aa813efb20c 2025-09-07T06:25:54.9250574Z * [new tag] trunk/1a588ace4667bde1331fbd8ed957157dca5cee68 -> trunk/1a588ace4667bde1331fbd8ed957157dca5cee68 2025-09-07T06:25:54.9251493Z * [new tag] trunk/1aa7476885e8f6e7b0ec3a5b6383aad9d3f343e7 -> trunk/1aa7476885e8f6e7b0ec3a5b6383aad9d3f343e7 2025-09-07T06:25:54.9252214Z * [new tag] trunk/1aeb421c342c9e9607842f4c87cb46e8e816ee53 -> trunk/1aeb421c342c9e9607842f4c87cb46e8e816ee53 2025-09-07T06:25:54.9253043Z * [new tag] trunk/1c1b28d5b6a942fafe23b2f09302d93c25226d4a -> trunk/1c1b28d5b6a942fafe23b2f09302d93c25226d4a 2025-09-07T06:25:54.9253888Z * [new tag] trunk/1ebd70d0c0d562d3be9abdee2a21906584af7d99 -> trunk/1ebd70d0c0d562d3be9abdee2a21906584af7d99 2025-09-07T06:25:54.9254736Z * [new tag] trunk/1ec2c15914da4ef7bd926ed9aebc8671c75fe965 -> trunk/1ec2c15914da4ef7bd926ed9aebc8671c75fe965 2025-09-07T06:25:54.9255569Z * [new tag] trunk/1f51056bd64e73d1aa81321bc3c098575b1bc78a -> trunk/1f51056bd64e73d1aa81321bc3c098575b1bc78a 2025-09-07T06:25:54.9256469Z * [new tag] trunk/1f820de639c75a1562d3fb03f160439f853ae07b -> trunk/1f820de639c75a1562d3fb03f160439f853ae07b 2025-09-07T06:25:54.9257311Z * [new tag] trunk/204697f0e695d82894c5010fbec664c4391f90cc -> trunk/204697f0e695d82894c5010fbec664c4391f90cc 2025-09-07T06:25:54.9258151Z * [new tag] trunk/20629b1619fe636227d01fc85ba221daa7185a05 -> trunk/20629b1619fe636227d01fc85ba221daa7185a05 2025-09-07T06:25:54.9259044Z * [new tag] trunk/20b47acef845e9c4f71da9429a396d293f50ebe7 -> trunk/20b47acef845e9c4f71da9429a396d293f50ebe7 2025-09-07T06:25:54.9259895Z * [new tag] trunk/20bfb2539d7c5250379648eda35f80b8a7d642dd -> trunk/20bfb2539d7c5250379648eda35f80b8a7d642dd 2025-09-07T06:25:54.9260769Z * [new tag] trunk/21fae99c180d17def562797ea0fb154d8fdf88e3 -> trunk/21fae99c180d17def562797ea0fb154d8fdf88e3 2025-09-07T06:25:54.9261691Z * [new tag] trunk/248355faf53f9f7ba2fd0a367d59600c6d991e7f -> trunk/248355faf53f9f7ba2fd0a367d59600c6d991e7f 2025-09-07T06:25:54.9262465Z * [new tag] trunk/25f4aaed9ec26f39c13862323ff8582006473d23 -> trunk/25f4aaed9ec26f39c13862323ff8582006473d23 2025-09-07T06:25:54.9263388Z * [new tag] trunk/261a84a1764412f8e659c956e3f81997ec3de9d5 -> trunk/261a84a1764412f8e659c956e3f81997ec3de9d5 2025-09-07T06:25:54.9264290Z * [new tag] trunk/28f4ab0737937858730f29f5c4e601e109cf9d5f -> trunk/28f4ab0737937858730f29f5c4e601e109cf9d5f 2025-09-07T06:25:54.9265168Z * [new tag] trunk/291cd11f2d5df6f48d348cce0e4e762f274f4dc4 -> trunk/291cd11f2d5df6f48d348cce0e4e762f274f4dc4 2025-09-07T06:25:54.9266055Z * [new tag] trunk/29280864d941e6108ab57f7298f520c0cf9696e9 -> trunk/29280864d941e6108ab57f7298f520c0cf9696e9 2025-09-07T06:25:54.9267002Z * [new tag] trunk/2a45837e98c63cae9d1a2e2133a727b829e549d5 -> trunk/2a45837e98c63cae9d1a2e2133a727b829e549d5 2025-09-07T06:25:54.9267883Z * [new tag] trunk/2a5c0785e2f975697fd7bdf1411de6e03dcaa1ef -> trunk/2a5c0785e2f975697fd7bdf1411de6e03dcaa1ef 2025-09-07T06:25:54.9268882Z * [new tag] trunk/2b8a83901c58a0858ea9e4ce00055f48e6ed164c -> trunk/2b8a83901c58a0858ea9e4ce00055f48e6ed164c 2025-09-07T06:25:54.9269734Z * [new tag] trunk/2ba65472dd54488a86a50326ea990195fc6732d6 -> trunk/2ba65472dd54488a86a50326ea990195fc6732d6 2025-09-07T06:25:54.9270577Z * [new tag] trunk/2c03f0acc53ed13fe8ebfe809129f25996e009a0 -> trunk/2c03f0acc53ed13fe8ebfe809129f25996e009a0 2025-09-07T06:25:54.9271406Z * [new tag] trunk/2dd529df0092799f68ee7afcf52338276906706a -> trunk/2dd529df0092799f68ee7afcf52338276906706a 2025-09-07T06:25:54.9272283Z * [new tag] trunk/2f6b4b1ad3f82bb3bd984f6e65744ea339ffb8b5 -> trunk/2f6b4b1ad3f82bb3bd984f6e65744ea339ffb8b5 2025-09-07T06:25:54.9275560Z * [new tag] trunk/2fa0520a64ed8aa734a56c4d124958f0b5711ca8 -> trunk/2fa0520a64ed8aa734a56c4d124958f0b5711ca8 2025-09-07T06:25:54.9276133Z * [new tag] trunk/302df2ac5dc4222294c09d48804a2dddb8f4bad8 -> trunk/302df2ac5dc4222294c09d48804a2dddb8f4bad8 2025-09-07T06:25:54.9276631Z * [new tag] trunk/33028597bfa2e0178e28c8cce33cb9b3800cac43 -> trunk/33028597bfa2e0178e28c8cce33cb9b3800cac43 2025-09-07T06:25:54.9277139Z * [new tag] trunk/34aa78274d6770086025a967fa63a86830e08176 -> trunk/34aa78274d6770086025a967fa63a86830e08176 2025-09-07T06:25:54.9277545Z * [new tag] trunk/3559c354ce6a14d11fe29fb12fa2747a2f2af449 -> trunk/3559c354ce6a14d11fe29fb12fa2747a2f2af449 2025-09-07T06:25:54.9277947Z * [new tag] trunk/36d207fcaaede0d1e58a5168084c307b32b6fd8b -> trunk/36d207fcaaede0d1e58a5168084c307b32b6fd8b 2025-09-07T06:25:54.9278347Z * [new tag] trunk/377033757ae5ca524ea842f1b0a5f446ed3d8fe0 -> trunk/377033757ae5ca524ea842f1b0a5f446ed3d8fe0 2025-09-07T06:25:54.9279059Z * [new tag] trunk/3771380f83fcac154a7c89ad679311d8c4818287 -> trunk/3771380f83fcac154a7c89ad679311d8c4818287 2025-09-07T06:25:54.9279934Z * [new tag] trunk/3a207816cc569f78863d86c01f2a3d265350e39f -> trunk/3a207816cc569f78863d86c01f2a3d265350e39f 2025-09-07T06:25:54.9280763Z * [new tag] trunk/3a20a20e7065ec927fdd216d4da3b04f879b3c67 -> trunk/3a20a20e7065ec927fdd216d4da3b04f879b3c67 2025-09-07T06:25:54.9281691Z * [new tag] trunk/3bbc2e3e4f025523eaa5dbff220b3e96bca608d0 -> trunk/3bbc2e3e4f025523eaa5dbff220b3e96bca608d0 2025-09-07T06:25:54.9282542Z * [new tag] trunk/3c0ff1b569c45cfa6935ad8031a9d4cf1551aa3f -> trunk/3c0ff1b569c45cfa6935ad8031a9d4cf1551aa3f 2025-09-07T06:25:54.9283430Z * [new tag] trunk/3c45af079afc92a03b03ddf4f9198902ffcf30cf -> trunk/3c45af079afc92a03b03ddf4f9198902ffcf30cf 2025-09-07T06:25:54.9284378Z * [new tag] trunk/3dde5d7f9bf80dd6623a712bc429e9e4302464b5 -> trunk/3dde5d7f9bf80dd6623a712bc429e9e4302464b5 2025-09-07T06:25:54.9285168Z * [new tag] trunk/403a3a393cda7e60f503f3b04b8805a845dcf45d -> trunk/403a3a393cda7e60f503f3b04b8805a845dcf45d 2025-09-07T06:25:54.9286050Z * [new tag] trunk/420c52ecf36f86d32da0853bfbe074b682b070aa -> trunk/420c52ecf36f86d32da0853bfbe074b682b070aa 2025-09-07T06:25:54.9286913Z * [new tag] trunk/43b7c86a2c0f91320f5c5f4827b111edff06fdb6 -> trunk/43b7c86a2c0f91320f5c5f4827b111edff06fdb6 2025-09-07T06:25:54.9287754Z * [new tag] trunk/451ed931562ec8b46d1f7e6c266a68132a119336 -> trunk/451ed931562ec8b46d1f7e6c266a68132a119336 2025-09-07T06:25:54.9288578Z * [new tag] trunk/480c7391126656154318fabf1d57ebc01e196e63 -> trunk/480c7391126656154318fabf1d57ebc01e196e63 2025-09-07T06:25:54.9289495Z * [new tag] trunk/48bedd753da22634aa94fbafeb731e82025404f3 -> trunk/48bedd753da22634aa94fbafeb731e82025404f3 2025-09-07T06:25:54.9290267Z * [new tag] trunk/494878a11b79071ada0b98f34042d47155be6d1c -> trunk/494878a11b79071ada0b98f34042d47155be6d1c 2025-09-07T06:25:54.9291126Z * [new tag] trunk/4ae57d448c0a7d37e4cfd5c27d977fad2cef4051 -> trunk/4ae57d448c0a7d37e4cfd5c27d977fad2cef4051 2025-09-07T06:25:54.9291964Z * [new tag] trunk/4cdaf8265d86f984254b62052da8c26ef61ef1cf -> trunk/4cdaf8265d86f984254b62052da8c26ef61ef1cf 2025-09-07T06:25:54.9292723Z * [new tag] trunk/4d4abec80f03cd8fdefe1d9cb3a60d3690cd777e -> trunk/4d4abec80f03cd8fdefe1d9cb3a60d3690cd777e 2025-09-07T06:25:54.9293650Z * [new tag] trunk/4e42aa8ffc44b8340eb0eeaf80a2cafc4763a186 -> trunk/4e42aa8ffc44b8340eb0eeaf80a2cafc4763a186 2025-09-07T06:25:54.9294512Z * [new tag] trunk/4f72d932feee0749397fec876dcd43994f50b215 -> trunk/4f72d932feee0749397fec876dcd43994f50b215 2025-09-07T06:25:54.9295885Z * [new tag] trunk/50fc22dedf3c4a27be61fa05551c4f320281b42d -> trunk/50fc22dedf3c4a27be61fa05551c4f320281b42d 2025-09-07T06:25:54.9296755Z * [new tag] trunk/5211f1f908907ffc064b56e43cf8659f7fc22aa9 -> trunk/5211f1f908907ffc064b56e43cf8659f7fc22aa9 2025-09-07T06:25:54.9297663Z * [new tag] trunk/524b78d4f67045b83bb69edc56ab16efe282971c -> trunk/524b78d4f67045b83bb69edc56ab16efe282971c 2025-09-07T06:25:54.9298565Z * [new tag] trunk/54e275e0d81fe1e1ccfa4fb5f2a5a9aaca00ca15 -> trunk/54e275e0d81fe1e1ccfa4fb5f2a5a9aaca00ca15 2025-09-07T06:25:54.9299323Z * [new tag] trunk/5561e45758d59c94605873d5db48ed459c004c3b -> trunk/5561e45758d59c94605873d5db48ed459c004c3b 2025-09-07T06:25:54.9300320Z * [new tag] trunk/57278d45f046d4f89f45d373b1af4dd56934ff24 -> trunk/57278d45f046d4f89f45d373b1af4dd56934ff24 2025-09-07T06:25:54.9301175Z * [new tag] trunk/5927a70934ccf7b70182d364c23245a7dd685503 -> trunk/5927a70934ccf7b70182d364c23245a7dd685503 2025-09-07T06:25:54.9302038Z * [new tag] trunk/5985e28912aeb40b103ebfcf2fd0665eb4a50599 -> trunk/5985e28912aeb40b103ebfcf2fd0665eb4a50599 2025-09-07T06:25:54.9302929Z * [new tag] trunk/5a2da090ed6db88bb657c4e51ec0b310cd08bff6 -> trunk/5a2da090ed6db88bb657c4e51ec0b310cd08bff6 2025-09-07T06:25:54.9303881Z * [new tag] trunk/5c473e9f5ee0ef0fc38e6cf34a95b547f8cdc8d5 -> trunk/5c473e9f5ee0ef0fc38e6cf34a95b547f8cdc8d5 2025-09-07T06:25:54.9304752Z * [new tag] trunk/5c67426d6847667a7c55a2dd01f470fa37238c18 -> trunk/5c67426d6847667a7c55a2dd01f470fa37238c18 2025-09-07T06:25:54.9305601Z * [new tag] trunk/5da573c42c332bc68d4b7946c69f690a876d951a -> trunk/5da573c42c332bc68d4b7946c69f690a876d951a 2025-09-07T06:25:54.9306478Z * [new tag] trunk/5e5870e858f60ff4bf87d03f3592097e934a9580 -> trunk/5e5870e858f60ff4bf87d03f3592097e934a9580 2025-09-07T06:25:54.9307319Z * [new tag] trunk/5f3cbc9442aa55b5afb29f4ac8ca9be569003e84 -> trunk/5f3cbc9442aa55b5afb29f4ac8ca9be569003e84 2025-09-07T06:25:54.9308227Z * [new tag] trunk/600c25e9a17fe56e3dee872be8854db08916ba0c -> trunk/600c25e9a17fe56e3dee872be8854db08916ba0c 2025-09-07T06:25:54.9309024Z * [new tag] trunk/601ae8e4831fc8123fffcfb8fd2e6b6381b42e14 -> trunk/601ae8e4831fc8123fffcfb8fd2e6b6381b42e14 2025-09-07T06:25:54.9309940Z * [new tag] trunk/6087ef41e54c2494b117ffd923faf20f515a6806 -> trunk/6087ef41e54c2494b117ffd923faf20f515a6806 2025-09-07T06:25:54.9310808Z * [new tag] trunk/626cb7df8161dd4ecb4fe43b60f37ce9076f56b1 -> trunk/626cb7df8161dd4ecb4fe43b60f37ce9076f56b1 2025-09-07T06:25:54.9311655Z * [new tag] trunk/62c3f9a97fd3dea7132a93066d32d893ffe101e6 -> trunk/62c3f9a97fd3dea7132a93066d32d893ffe101e6 2025-09-07T06:25:54.9312510Z * [new tag] trunk/63a9c23fe99eacfd09610c36dfe8f01b053c1a35 -> trunk/63a9c23fe99eacfd09610c36dfe8f01b053c1a35 2025-09-07T06:25:54.9313392Z * [new tag] trunk/65985937d97505f648b6ed852c3129f2dd08b251 -> trunk/65985937d97505f648b6ed852c3129f2dd08b251 2025-09-07T06:25:54.9314912Z * [new tag] trunk/66f3b4a682a6153517dd23369fdc3289b6494b07 -> trunk/66f3b4a682a6153517dd23369fdc3289b6494b07 2025-09-07T06:25:54.9315774Z * [new tag] trunk/6737e2c996990024187ba620d2764f3b6f6add2c -> trunk/6737e2c996990024187ba620d2764f3b6f6add2c 2025-09-07T06:25:54.9316390Z * [new tag] trunk/67c31dcd364f10072a55f4a30ffd1151c686283a -> trunk/67c31dcd364f10072a55f4a30ffd1151c686283a 2025-09-07T06:25:54.9317329Z * [new tag] trunk/68738beff73e9c3512e18b4edea811a897ce42db -> trunk/68738beff73e9c3512e18b4edea811a897ce42db 2025-09-07T06:25:54.9318195Z * [new tag] trunk/69a25f68884a168550695fdb1a7c310c54d29536 -> trunk/69a25f68884a168550695fdb1a7c310c54d29536 2025-09-07T06:25:54.9319019Z * [new tag] trunk/6b1900c22f1a07b9519346898d4c71d8a2b0f12f -> trunk/6b1900c22f1a07b9519346898d4c71d8a2b0f12f 2025-09-07T06:25:54.9319876Z * [new tag] trunk/6b8b3ac4403f771bd4a8f9a45d93347304148774 -> trunk/6b8b3ac4403f771bd4a8f9a45d93347304148774 2025-09-07T06:25:54.9320719Z * [new tag] trunk/6f7608d603834d6068b2e7a5d59bec3973b6bb1b -> trunk/6f7608d603834d6068b2e7a5d59bec3973b6bb1b 2025-09-07T06:25:54.9321599Z * [new tag] trunk/70d36e047dfb3488fd6335016711a784d810ebda -> trunk/70d36e047dfb3488fd6335016711a784d810ebda 2025-09-07T06:25:54.9322472Z * [new tag] trunk/71992dd805ff9d6763f77214dfe8b0465e88c87b -> trunk/71992dd805ff9d6763f77214dfe8b0465e88c87b 2025-09-07T06:25:54.9323340Z * [new tag] trunk/734ce8eba9c69381f187359bf0fef1d71d84cd20 -> trunk/734ce8eba9c69381f187359bf0fef1d71d84cd20 2025-09-07T06:25:54.9324273Z * [new tag] trunk/73eb4511fb863a37944342b7e92aae706de603c8 -> trunk/73eb4511fb863a37944342b7e92aae706de603c8 2025-09-07T06:25:54.9325465Z * [new tag] trunk/75bc23cfc345bd4c05e7f97c416c4b3d2d1fa64b -> trunk/75bc23cfc345bd4c05e7f97c416c4b3d2d1fa64b 2025-09-07T06:25:54.9326190Z * [new tag] trunk/771f369448321a387f2018535bc8b8b6e5f12fab -> trunk/771f369448321a387f2018535bc8b8b6e5f12fab 2025-09-07T06:25:54.9327166Z * [new tag] trunk/789d4942127143f2adcb53612c058ce4c9a2cf20 -> trunk/789d4942127143f2adcb53612c058ce4c9a2cf20 2025-09-07T06:25:54.9327870Z * [new tag] trunk/791eff96c85678c950888f9da24650083ee673fe -> trunk/791eff96c85678c950888f9da24650083ee673fe 2025-09-07T06:25:54.9328628Z * [new tag] trunk/793fc12aff1f69fbbf9f4278182fb52bbe350fc9 -> trunk/793fc12aff1f69fbbf9f4278182fb52bbe350fc9 2025-09-07T06:25:54.9329442Z * [new tag] trunk/79fcd5247a9a129eee526a14df30bfc6a22b3f01 -> trunk/79fcd5247a9a129eee526a14df30bfc6a22b3f01 2025-09-07T06:25:54.9330290Z * [new tag] trunk/7f4ff79210eb06924f223ae3a1941ee0e2635348 -> trunk/7f4ff79210eb06924f223ae3a1941ee0e2635348 2025-09-07T06:25:54.9331231Z * [new tag] trunk/8076a185c85112be62be292eb47409c88a585b1c -> trunk/8076a185c85112be62be292eb47409c88a585b1c 2025-09-07T06:25:54.9332082Z * [new tag] trunk/80dd397f1979371a5583fa3d5c7352029522a78d -> trunk/80dd397f1979371a5583fa3d5c7352029522a78d 2025-09-07T06:25:54.9332800Z * [new tag] trunk/8171d6052ec12628eb67e0040839314056014429 -> trunk/8171d6052ec12628eb67e0040839314056014429 2025-09-07T06:25:54.9333706Z * [new tag] trunk/81aeefa657b7ccc26b275c50a9f33b2f056e8071 -> trunk/81aeefa657b7ccc26b275c50a9f33b2f056e8071 2025-09-07T06:25:54.9334548Z * [new tag] trunk/81b7b16618bda250ce55982894a83dc0805eb64c -> trunk/81b7b16618bda250ce55982894a83dc0805eb64c 2025-09-07T06:25:54.9335441Z * [new tag] trunk/827f0d405448de31f79d1089f7d7fceab2f87895 -> trunk/827f0d405448de31f79d1089f7d7fceab2f87895 2025-09-07T06:25:54.9336312Z * [new tag] trunk/82f63c8f6de63c30132a8ac299b6e8c2fd0d3fe8 -> trunk/82f63c8f6de63c30132a8ac299b6e8c2fd0d3fe8 2025-09-07T06:25:54.9337206Z * [new tag] trunk/850e1382a9c56bfde18af09d3e72352d775e9435 -> trunk/850e1382a9c56bfde18af09d3e72352d775e9435 2025-09-07T06:25:54.9338346Z * [new tag] trunk/8678d831c48e616b717bff50f2d03141d2e9f965 -> trunk/8678d831c48e616b717bff50f2d03141d2e9f965 2025-09-07T06:25:54.9339153Z * [new tag] trunk/869cbcc16e489a4f5a14a93d5779b0ea86061c60 -> trunk/869cbcc16e489a4f5a14a93d5779b0ea86061c60 2025-09-07T06:25:54.9340079Z * [new tag] trunk/8703debf669bc2238211bfd039f4ecdd8228b7f7 -> trunk/8703debf669bc2238211bfd039f4ecdd8228b7f7 2025-09-07T06:25:54.9340941Z * [new tag] trunk/874069fbe46e82da5cfa405e6c0deb12e89ff608 -> trunk/874069fbe46e82da5cfa405e6c0deb12e89ff608 2025-09-07T06:25:54.9341894Z * [new tag] trunk/8875d6e394da2fffd04f31b28bf258c94d4776a3 -> trunk/8875d6e394da2fffd04f31b28bf258c94d4776a3 2025-09-07T06:25:54.9342793Z * [new tag] trunk/88d94d17e8c5155451393afa6eb3bab48ab61c16 -> trunk/88d94d17e8c5155451393afa6eb3bab48ab61c16 2025-09-07T06:25:54.9343748Z * [new tag] trunk/890626632def7e0ef95a2d01e87a0e4627824a9f -> trunk/890626632def7e0ef95a2d01e87a0e4627824a9f 2025-09-07T06:25:54.9344687Z * [new tag] trunk/8975cda2520b7b1b5bc3b4d8213edf261fa82570 -> trunk/8975cda2520b7b1b5bc3b4d8213edf261fa82570 2025-09-07T06:25:54.9345564Z * [new tag] trunk/89d41d3f61d04f14730ec26f008a59bef6624610 -> trunk/89d41d3f61d04f14730ec26f008a59bef6624610 2025-09-07T06:25:54.9346839Z * [new tag] trunk/8bb213b6d599ef1273fe52f9b1f6d476056c3a41 -> trunk/8bb213b6d599ef1273fe52f9b1f6d476056c3a41 2025-09-07T06:25:54.9347625Z * [new tag] trunk/8e23a1227b5fb2e39afaa7d57c075a75b640a5af -> trunk/8e23a1227b5fb2e39afaa7d57c075a75b640a5af 2025-09-07T06:25:54.9348739Z * [new tag] trunk/8ec551bb354ab2b85fbbba9d461740a20366d248 -> trunk/8ec551bb354ab2b85fbbba9d461740a20366d248 2025-09-07T06:25:54.9349637Z * [new tag] trunk/8fd3c9ce919c8d5c645fd348bba517e948cbc29d -> trunk/8fd3c9ce919c8d5c645fd348bba517e948cbc29d 2025-09-07T06:25:54.9350772Z * [new tag] trunk/90f50f7e68e120d9574e6e3189e37b4280010ad9 -> trunk/90f50f7e68e120d9574e6e3189e37b4280010ad9 2025-09-07T06:25:54.9351677Z * [new tag] trunk/91f0bcf43fc0bc743350d491ac63b77e92054ac9 -> trunk/91f0bcf43fc0bc743350d491ac63b77e92054ac9 2025-09-07T06:25:54.9352618Z * [new tag] trunk/92576a594b8121f6b0b1b5a3ea16d08792fc68ab -> trunk/92576a594b8121f6b0b1b5a3ea16d08792fc68ab 2025-09-07T06:25:54.9353988Z * [new tag] trunk/92a43025e0baa1f2ce345f28d22913b518a1ab9d -> trunk/92a43025e0baa1f2ce345f28d22913b518a1ab9d 2025-09-07T06:25:54.9354726Z * [new tag] trunk/93fb23d6fae7c4e82c4239a1033e522088742634 -> trunk/93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:25:54.9355621Z * [new tag] trunk/9458d1ac3bd70c2af316a8ba95d2c6c9c1199c9c -> trunk/9458d1ac3bd70c2af316a8ba95d2c6c9c1199c9c 2025-09-07T06:25:54.9356569Z * [new tag] trunk/9480cdc0b61488c89a23c2f64f43b2dcedc8728e -> trunk/9480cdc0b61488c89a23c2f64f43b2dcedc8728e 2025-09-07T06:25:54.9357437Z * [new tag] trunk/9491d289b329e4ba4a9f5f5b1be7960671bb7840 -> trunk/9491d289b329e4ba4a9f5f5b1be7960671bb7840 2025-09-07T06:25:54.9358344Z * [new tag] trunk/9499c8761cd2067feb9877414e818f6fd00290f1 -> trunk/9499c8761cd2067feb9877414e818f6fd00290f1 2025-09-07T06:25:54.9359213Z * [new tag] trunk/95ee0bfea99d3d346d6502b91b497d2b35795504 -> trunk/95ee0bfea99d3d346d6502b91b497d2b35795504 2025-09-07T06:25:54.9360112Z * [new tag] trunk/98374612fc2febd686be20761e56bdc2424bc36a -> trunk/98374612fc2febd686be20761e56bdc2424bc36a 2025-09-07T06:25:54.9361121Z * [new tag] trunk/98efc9e93d8fc61eb53cb91378443617cb550500 -> trunk/98efc9e93d8fc61eb53cb91378443617cb550500 2025-09-07T06:25:54.9362029Z * [new tag] trunk/994f2a5dbcbdc915da39bf6f6ce4d1f5e74835c9 -> trunk/994f2a5dbcbdc915da39bf6f6ce4d1f5e74835c9 2025-09-07T06:25:54.9362905Z * [new tag] trunk/99f356fa58c8d726cef022d8710f5491291158f6 -> trunk/99f356fa58c8d726cef022d8710f5491291158f6 2025-09-07T06:25:54.9363833Z * [new tag] trunk/9a1c5c0a078b94d13ac5c1ae0d754d19fb73bf99 -> trunk/9a1c5c0a078b94d13ac5c1ae0d754d19fb73bf99 2025-09-07T06:25:54.9364750Z * [new tag] trunk/9a665ca3c472384e9d722bddba79e5a7680f1abd -> trunk/9a665ca3c472384e9d722bddba79e5a7680f1abd 2025-09-07T06:25:54.9365763Z * [new tag] trunk/9aedb3cd87b52160872173c177f61053d97bed57 -> trunk/9aedb3cd87b52160872173c177f61053d97bed57 2025-09-07T06:25:54.9366620Z * [new tag] trunk/9b81fe281da41f2421506339d26b027a468902f4 -> trunk/9b81fe281da41f2421506339d26b027a468902f4 2025-09-07T06:25:54.9367536Z * [new tag] trunk/9bdcee01f86e2969cff1140cdecfca13cb51816e -> trunk/9bdcee01f86e2969cff1140cdecfca13cb51816e 2025-09-07T06:25:54.9368450Z * [new tag] trunk/9c03d6be87eedc06e524e202e07a7e776551a839 -> trunk/9c03d6be87eedc06e524e202e07a7e776551a839 2025-09-07T06:25:54.9369352Z * [new tag] trunk/9c957723a0fedd9c637e63e023a613019e2cab60 -> trunk/9c957723a0fedd9c637e63e023a613019e2cab60 2025-09-07T06:25:54.9370190Z * [new tag] trunk/9e5247f51d81735e5f1e65e80588985fa93bccc5 -> trunk/9e5247f51d81735e5f1e65e80588985fa93bccc5 2025-09-07T06:25:54.9371160Z * [new tag] trunk/9eadb37cdd699f7e8e8177a5227bfeb16184ef26 -> trunk/9eadb37cdd699f7e8e8177a5227bfeb16184ef26 2025-09-07T06:25:54.9372018Z * [new tag] trunk/a00cdc1e4159db73c9ffb3f25e93e55877709a29 -> trunk/a00cdc1e4159db73c9ffb3f25e93e55877709a29 2025-09-07T06:25:54.9372933Z * [new tag] trunk/a02ee4a816d11380c6f564c1aba64d56af5ba705 -> trunk/a02ee4a816d11380c6f564c1aba64d56af5ba705 2025-09-07T06:25:54.9373824Z * [new tag] trunk/a3c7f77e50f900721817934120d60c2361b3c40d -> trunk/a3c7f77e50f900721817934120d60c2361b3c40d 2025-09-07T06:25:54.9374992Z * [new tag] trunk/a3d72b09ae12126a2b7d4a63a45ac100a882a802 -> trunk/a3d72b09ae12126a2b7d4a63a45ac100a882a802 2025-09-07T06:25:54.9375807Z * [new tag] trunk/a3e5466002791da609fcb069155d8ee347baee92 -> trunk/a3e5466002791da609fcb069155d8ee347baee92 2025-09-07T06:25:54.9376710Z * [new tag] trunk/a714437093ed196eee28f7de454cf4c41badc098 -> trunk/a714437093ed196eee28f7de454cf4c41badc098 2025-09-07T06:25:54.9377594Z * [new tag] trunk/a75e8cd27098f290de0b7439685d05ce02e91356 -> trunk/a75e8cd27098f290de0b7439685d05ce02e91356 2025-09-07T06:25:54.9378339Z * [new tag] trunk/a8d6943d36c1c2a5f90d3573460695bad4b623ae -> trunk/a8d6943d36c1c2a5f90d3573460695bad4b623ae 2025-09-07T06:25:54.9379241Z * [new tag] trunk/a918bbad6ab20649ff82eefb48417ecbe96bcb34 -> trunk/a918bbad6ab20649ff82eefb48417ecbe96bcb34 2025-09-07T06:25:54.9380268Z * [new tag] trunk/a99d8d39bc842d6ebc3e368b178e4884d24b056e -> trunk/a99d8d39bc842d6ebc3e368b178e4884d24b056e 2025-09-07T06:25:54.9381103Z * [new tag] trunk/aac1a50a191b4102d566c9c1ea22f06d6c2e3f02 -> trunk/aac1a50a191b4102d566c9c1ea22f06d6c2e3f02 2025-09-07T06:25:54.9381957Z * [new tag] trunk/aad96a202244c7d0d120c04ba8db593edd8c0f92 -> trunk/aad96a202244c7d0d120c04ba8db593edd8c0f92 2025-09-07T06:25:54.9382835Z * [new tag] trunk/ab643e4dbbaf7b663d4237514cbf01af9b11565c -> trunk/ab643e4dbbaf7b663d4237514cbf01af9b11565c 2025-09-07T06:25:54.9383784Z * [new tag] trunk/abc447174cd2cf8591edbc70a9f836f9a5779f47 -> trunk/abc447174cd2cf8591edbc70a9f836f9a5779f47 2025-09-07T06:25:54.9384664Z * [new tag] trunk/acece97c3a9dceb63194e314da93fdf37cf15a0d -> trunk/acece97c3a9dceb63194e314da93fdf37cf15a0d 2025-09-07T06:25:54.9385619Z * [new tag] trunk/adae7f66aacf3f248c3101b858cf98d5809119fa -> trunk/adae7f66aacf3f248c3101b858cf98d5809119fa 2025-09-07T06:25:54.9386622Z * [new tag] trunk/ae0edc133e61e3b16caf0b2ee0ff3f33ab72af4c -> trunk/ae0edc133e61e3b16caf0b2ee0ff3f33ab72af4c 2025-09-07T06:25:54.9387468Z * [new tag] trunk/aed33a8fcbd60b052d4559d261390c5797129c6d -> trunk/aed33a8fcbd60b052d4559d261390c5797129c6d 2025-09-07T06:25:54.9388396Z * [new tag] trunk/b04e922712080a3652e438d05e8bb74e0cd2d238 -> trunk/b04e922712080a3652e438d05e8bb74e0cd2d238 2025-09-07T06:25:54.9389755Z * [new tag] trunk/b0a3e58dd71c1a039ac0ef51e5bd8f704f632f6f -> trunk/b0a3e58dd71c1a039ac0ef51e5bd8f704f632f6f 2025-09-07T06:25:54.9390666Z * [new tag] trunk/b16d3f4c8c01d461c2f01064e9ca5fa2b33f5cf1 -> trunk/b16d3f4c8c01d461c2f01064e9ca5fa2b33f5cf1 2025-09-07T06:25:54.9391460Z * [new tag] trunk/b18bb6796f210a183e687d9d64984a5a9d13cf09 -> trunk/b18bb6796f210a183e687d9d64984a5a9d13cf09 2025-09-07T06:25:54.9392362Z * [new tag] trunk/b1bb98ddebdd3e41bf7987372409bdce96ae55de -> trunk/b1bb98ddebdd3e41bf7987372409bdce96ae55de 2025-09-07T06:25:54.9393200Z * [new tag] trunk/b2b4add0e754411372060e1d7b4057a66439172b -> trunk/b2b4add0e754411372060e1d7b4057a66439172b 2025-09-07T06:25:54.9394093Z * [new tag] trunk/b2c7b9ad2dc5a7c0b61febd307761bd5bc2f0f05 -> trunk/b2c7b9ad2dc5a7c0b61febd307761bd5bc2f0f05 2025-09-07T06:25:54.9395101Z * [new tag] trunk/b40d9432be44a6b5974ee62e7d19c3c61c5ece37 -> trunk/b40d9432be44a6b5974ee62e7d19c3c61c5ece37 2025-09-07T06:25:54.9395994Z * [new tag] trunk/b4ad38279b178b7bd14355123c1101e2e853e77b -> trunk/b4ad38279b178b7bd14355123c1101e2e853e77b 2025-09-07T06:25:54.9396908Z * [new tag] trunk/b67c41039835bd9b20b83cd6233e86baaa5f5dde -> trunk/b67c41039835bd9b20b83cd6233e86baaa5f5dde 2025-09-07T06:25:54.9397907Z * [new tag] trunk/b6d0a9ea9056ede4f7024dbf3bd6c43be3aff49c -> trunk/b6d0a9ea9056ede4f7024dbf3bd6c43be3aff49c 2025-09-07T06:25:54.9398832Z * [new tag] trunk/b7dad7dd49448c88d0751fa2e29c70afe985f734 -> trunk/b7dad7dd49448c88d0751fa2e29c70afe985f734 2025-09-07T06:25:54.9399701Z * [new tag] trunk/b7e207ca9f046ddd716076965a0cce403ba99052 -> trunk/b7e207ca9f046ddd716076965a0cce403ba99052 2025-09-07T06:25:54.9400685Z * [new tag] trunk/b919560c4a7010e2d89facee25586269a994746e -> trunk/b919560c4a7010e2d89facee25586269a994746e 2025-09-07T06:25:54.9401618Z * [new tag] trunk/b9ba612f7a968f7b27e121ca8f4d0a4d954f5354 -> trunk/b9ba612f7a968f7b27e121ca8f4d0a4d954f5354 2025-09-07T06:25:54.9402623Z * [new tag] trunk/ba7f546ccccb5e0b36d9070dc25f26a9647f89f8 -> trunk/ba7f546ccccb5e0b36d9070dc25f26a9647f89f8 2025-09-07T06:25:54.9403478Z * [new tag] trunk/bb950284c7e72905994bc25dd436c10e48088d85 -> trunk/bb950284c7e72905994bc25dd436c10e48088d85 2025-09-07T06:25:54.9404530Z * [new tag] trunk/bbedc71fd3267c639c38b4ec25eaa22f973d9c4d -> trunk/bbedc71fd3267c639c38b4ec25eaa22f973d9c4d 2025-09-07T06:25:54.9405381Z * [new tag] trunk/bc4db2c27fce6ff1648bdc5af31ec225d2a31f37 -> trunk/bc4db2c27fce6ff1648bdc5af31ec225d2a31f37 2025-09-07T06:25:54.9406184Z * [new tag] trunk/bc505977fb66677a09c31155c987330fbb18a865 -> trunk/bc505977fb66677a09c31155c987330fbb18a865 2025-09-07T06:25:54.9407077Z * [new tag] trunk/bd39e47feea7326afb5bbb67fcb1e69279239527 -> trunk/bd39e47feea7326afb5bbb67fcb1e69279239527 2025-09-07T06:25:54.9408093Z * [new tag] trunk/be5b03dde96638f25ffd732a4fed7e41b4cf40e1 -> trunk/be5b03dde96638f25ffd732a4fed7e41b4cf40e1 2025-09-07T06:25:54.9408968Z * [new tag] trunk/bffc7dd1f374d8408911cd22c6b3d6df39ded9b3 -> trunk/bffc7dd1f374d8408911cd22c6b3d6df39ded9b3 2025-09-07T06:25:54.9409921Z * [new tag] trunk/c024b1f5a18d5c5aee5cc2acdd4c52b24b93ffcf -> trunk/c024b1f5a18d5c5aee5cc2acdd4c52b24b93ffcf 2025-09-07T06:25:54.9410775Z * [new tag] trunk/c0983e6cc0acf71689e1851d12609e00b3f59371 -> trunk/c0983e6cc0acf71689e1851d12609e00b3f59371 2025-09-07T06:25:54.9411704Z * [new tag] trunk/c10195e723eeeedd099ed8b73eda7184ca618fad -> trunk/c10195e723eeeedd099ed8b73eda7184ca618fad 2025-09-07T06:25:54.9413072Z * [new tag] trunk/c157cf6488ade6a7ee2ce2d25b059e1335630a99 -> trunk/c157cf6488ade6a7ee2ce2d25b059e1335630a99 2025-09-07T06:25:54.9414011Z * [new tag] trunk/c2a30246172fd71d56529907ffd3c27b76b1f3a7 -> trunk/c2a30246172fd71d56529907ffd3c27b76b1f3a7 2025-09-07T06:25:54.9414931Z * [new tag] trunk/c32111149921b48bfef909293f1049e21619ed76 -> trunk/c32111149921b48bfef909293f1049e21619ed76 2025-09-07T06:25:54.9415702Z * [new tag] trunk/c37103234afc832dcad307e9016230810957c9d5 -> trunk/c37103234afc832dcad307e9016230810957c9d5 2025-09-07T06:25:54.9416610Z * [new tag] trunk/c3ceca2995cd35e1376c4b0704669bff1a81e836 -> trunk/c3ceca2995cd35e1376c4b0704669bff1a81e836 2025-09-07T06:25:54.9417489Z * [new tag] trunk/c3d54dea9febb1236d48d19e5d4876a63f2e20fd -> trunk/c3d54dea9febb1236d48d19e5d4876a63f2e20fd 2025-09-07T06:25:54.9418391Z * [new tag] trunk/c465b3d52c5687fe910d35a5c75341b77f821741 -> trunk/c465b3d52c5687fe910d35a5c75341b77f821741 2025-09-07T06:25:54.9419285Z * [new tag] trunk/c5b8a10be5e89396da916d1069ffcb7135f0372b -> trunk/c5b8a10be5e89396da916d1069ffcb7135f0372b 2025-09-07T06:25:54.9420108Z * [new tag] trunk/c7e41071a08f4045bc11ab60ec366d7357d56e30 -> trunk/c7e41071a08f4045bc11ab60ec366d7357d56e30 2025-09-07T06:25:54.9421006Z * [new tag] trunk/c98ddaca6d2e19ca37aff00c4ff0cda1e9a6ff65 -> trunk/c98ddaca6d2e19ca37aff00c4ff0cda1e9a6ff65 2025-09-07T06:25:54.9421916Z * [new tag] trunk/cb1e31362c7b53acf4ac95b9f8878064c184f03b -> trunk/cb1e31362c7b53acf4ac95b9f8878064c184f03b 2025-09-07T06:25:54.9422746Z * [new tag] trunk/cbfb005f7cce79974795b148e265f594f59477c8 -> trunk/cbfb005f7cce79974795b148e265f594f59477c8 2025-09-07T06:25:54.9423758Z * [new tag] trunk/cc5bdd12401bda835291d2f3cb297132ebdbf358 -> trunk/cc5bdd12401bda835291d2f3cb297132ebdbf358 2025-09-07T06:25:54.9424943Z * [new tag] trunk/cd529b686d54bbaa443f5b310140de48422d96c7 -> trunk/cd529b686d54bbaa443f5b310140de48422d96c7 2025-09-07T06:25:54.9425702Z * [new tag] trunk/cec0ff122815582af5302360aff03676558c5c87 -> trunk/cec0ff122815582af5302360aff03676558c5c87 2025-09-07T06:25:54.9426614Z * [new tag] trunk/d11720efdb563d02cf4f7d324311fb15a755268e -> trunk/d11720efdb563d02cf4f7d324311fb15a755268e 2025-09-07T06:25:54.9427493Z * [new tag] trunk/d1706d9128ae24d9048167e80d3fe5196d19035e -> trunk/d1706d9128ae24d9048167e80d3fe5196d19035e 2025-09-07T06:25:54.9428432Z * [new tag] trunk/d1a15abfdcaef138f2d9e93a9f46be44f30b766d -> trunk/d1a15abfdcaef138f2d9e93a9f46be44f30b766d 2025-09-07T06:25:54.9429644Z * [new tag] trunk/d232a95d4a79404ca05c1f52d37fde7339dcdf49 -> trunk/d232a95d4a79404ca05c1f52d37fde7339dcdf49 2025-09-07T06:25:54.9430414Z * [new tag] trunk/d2d4c8e9b2371c9aacfb771d9402ac7427b9778e -> trunk/d2d4c8e9b2371c9aacfb771d9402ac7427b9778e 2025-09-07T06:25:54.9431282Z * [new tag] trunk/d33840c542b387ab08ba49aa6c45aa9567fd9be7 -> trunk/d33840c542b387ab08ba49aa6c45aa9567fd9be7 2025-09-07T06:25:54.9432136Z * [new tag] trunk/d5643e8f3a648a99636bfa1f2a41d54bd3c0d0f1 -> trunk/d5643e8f3a648a99636bfa1f2a41d54bd3c0d0f1 2025-09-07T06:25:54.9432989Z * [new tag] trunk/d5b38410b5b6cf75c7a7389972777a6497926ee7 -> trunk/d5b38410b5b6cf75c7a7389972777a6497926ee7 2025-09-07T06:25:54.9433787Z * [new tag] trunk/d5e0f4202ba14632e4d14862ace096609e763462 -> trunk/d5e0f4202ba14632e4d14862ace096609e763462 2025-09-07T06:25:54.9434704Z * [new tag] trunk/d636c181f9140a7b59be10b36eae23039fc2bb72 -> trunk/d636c181f9140a7b59be10b36eae23039fc2bb72 2025-09-07T06:25:54.9436295Z * [new tag] trunk/d64718503728001a1e78168fd7f2d4ff23e57285 -> trunk/d64718503728001a1e78168fd7f2d4ff23e57285 2025-09-07T06:25:54.9437136Z * [new tag] trunk/d67c29ad22670320d676b02e394274af34e8e643 -> trunk/d67c29ad22670320d676b02e394274af34e8e643 2025-09-07T06:25:54.9438067Z * [new tag] trunk/d6b74568e2c98ce58ecc145b72ac66d4caf7ce95 -> trunk/d6b74568e2c98ce58ecc145b72ac66d4caf7ce95 2025-09-07T06:25:54.9438952Z * [new tag] trunk/d711f27845abd45007ccab6076649ebd896c2661 -> trunk/d711f27845abd45007ccab6076649ebd896c2661 2025-09-07T06:25:54.9439899Z * [new tag] trunk/d9d6dde0f42d4bcc8c97671ac50d5096c7e500ab -> trunk/d9d6dde0f42d4bcc8c97671ac50d5096c7e500ab 2025-09-07T06:25:54.9440813Z * [new tag] trunk/da4db4b33d1fdd046650cf19fdbac581a19bf2f9 -> trunk/da4db4b33d1fdd046650cf19fdbac581a19bf2f9 2025-09-07T06:25:54.9441601Z * [new tag] trunk/dac8a4b91c01c3bbc96f54e621b1ea4ffdbd29d1 -> trunk/dac8a4b91c01c3bbc96f54e621b1ea4ffdbd29d1 2025-09-07T06:25:54.9442589Z * [new tag] trunk/dbec08729fb9848bebed6048c63831b87170d061 -> trunk/dbec08729fb9848bebed6048c63831b87170d061 2025-09-07T06:25:54.9443464Z * [new tag] trunk/dcf385395d838f38c8dca25913578230dd43099a -> trunk/dcf385395d838f38c8dca25913578230dd43099a 2025-09-07T06:25:54.9444407Z * [new tag] trunk/dd2519abe83ec3c40d4797492434e41fe3b47e17 -> trunk/dd2519abe83ec3c40d4797492434e41fe3b47e17 2025-09-07T06:25:54.9445394Z * [new tag] trunk/dec72ea4b006dd0fbcaaaa106ad273d73807ab9d -> trunk/dec72ea4b006dd0fbcaaaa106ad273d73807ab9d 2025-09-07T06:25:54.9446261Z * [new tag] trunk/e0a62b266c021b910ce6dc02a6c9429210487717 -> trunk/e0a62b266c021b910ce6dc02a6c9429210487717 2025-09-07T06:25:54.9447180Z * [new tag] trunk/e19e02c84c9dcc408375e5cae3b0709c18b99228 -> trunk/e19e02c84c9dcc408375e5cae3b0709c18b99228 2025-09-07T06:25:54.9448342Z * [new tag] trunk/e304ea4e69d3a7deeb7e48c7450c214a4c953937 -> trunk/e304ea4e69d3a7deeb7e48c7450c214a4c953937 2025-09-07T06:25:54.9449211Z * [new tag] trunk/e3068cdb446adefb5a875616ba37a60235391439 -> trunk/e3068cdb446adefb5a875616ba37a60235391439 2025-09-07T06:25:54.9450104Z * [new tag] trunk/e381d4b0205d5f126c1de534f867ba776f7c3ee6 -> trunk/e381d4b0205d5f126c1de534f867ba776f7c3ee6 2025-09-07T06:25:54.9451060Z * [new tag] trunk/e4bd0ff4f8981b805df32ea5b3550621965ea4f2 -> trunk/e4bd0ff4f8981b805df32ea5b3550621965ea4f2 2025-09-07T06:25:54.9451809Z * [new tag] trunk/e532c9d4f1cdcbc1ea9628f55b9813e77847bdc7 -> trunk/e532c9d4f1cdcbc1ea9628f55b9813e77847bdc7 2025-09-07T06:25:54.9452657Z * [new tag] trunk/e92cd9415377403b6e90585e764639e2e0b5973b -> trunk/e92cd9415377403b6e90585e764639e2e0b5973b 2025-09-07T06:25:54.9453683Z * [new tag] trunk/e9481b6617b5576b099d8ca5798111592e9ad090 -> trunk/e9481b6617b5576b099d8ca5798111592e9ad090 2025-09-07T06:25:54.9454426Z * [new tag] trunk/ea1883dfd3e42defe37b11202b878bb76defa087 -> trunk/ea1883dfd3e42defe37b11202b878bb76defa087 2025-09-07T06:25:54.9455392Z * [new tag] trunk/eac3d6f04cfbbebe3d470dacd216da7d4b1f95a8 -> trunk/eac3d6f04cfbbebe3d470dacd216da7d4b1f95a8 2025-09-07T06:25:54.9456210Z * [new tag] trunk/eb18d32bda75189494d955aa001ade15f10333de -> trunk/eb18d32bda75189494d955aa001ade15f10333de 2025-09-07T06:25:54.9457004Z * [new tag] trunk/ef3be6726f7ff4b77c22db10cec5b686f9107ea9 -> trunk/ef3be6726f7ff4b77c22db10cec5b686f9107ea9 2025-09-07T06:25:54.9457849Z * [new tag] trunk/ef8aabd42422725026cb4dbf48aafa9efa226a04 -> trunk/ef8aabd42422725026cb4dbf48aafa9efa226a04 2025-09-07T06:25:54.9458978Z * [new tag] trunk/f00445b43eee57e20bb9316fa796ca23bf73373b -> trunk/f00445b43eee57e20bb9316fa796ca23bf73373b 2025-09-07T06:25:54.9459811Z * [new tag] trunk/f0c391102b754e3b145e8c59231d2df563487e37 -> trunk/f0c391102b754e3b145e8c59231d2df563487e37 2025-09-07T06:25:54.9460778Z * [new tag] trunk/f27985b7e796fb66a1b476284ba42d8cb360a751 -> trunk/f27985b7e796fb66a1b476284ba42d8cb360a751 2025-09-07T06:25:54.9461769Z * [new tag] trunk/f36f285953700f971552083a5da9d0ceacb63bbd -> trunk/f36f285953700f971552083a5da9d0ceacb63bbd 2025-09-07T06:25:54.9462689Z * [new tag] trunk/f3cebec39ebc110e1c8b06e741896585f7892dbb -> trunk/f3cebec39ebc110e1c8b06e741896585f7892dbb 2025-09-07T06:25:54.9463455Z * [new tag] trunk/f4c33cd44acac92c0b451a04da20ebe9370e5b0c -> trunk/f4c33cd44acac92c0b451a04da20ebe9370e5b0c 2025-09-07T06:25:54.9464417Z * [new tag] trunk/f612045ce105f008b2b675e2fc870163babeb2e8 -> trunk/f612045ce105f008b2b675e2fc870163babeb2e8 2025-09-07T06:25:54.9465324Z * [new tag] trunk/f8746b878dfc1e9639d42cbde832e9b9e792c86c -> trunk/f8746b878dfc1e9639d42cbde832e9b9e792c86c 2025-09-07T06:25:54.9466152Z * [new tag] trunk/f8ffa9194e26523e5f976d4a824d5cc58922727c -> trunk/f8ffa9194e26523e5f976d4a824d5cc58922727c 2025-09-07T06:25:54.9466997Z * [new tag] trunk/f981a7fa5230b98974291fdde32fe8488bc5d469 -> trunk/f981a7fa5230b98974291fdde32fe8488bc5d469 2025-09-07T06:25:54.9467936Z * [new tag] trunk/fbf3d2027daabbcb44d0af274b139be2a248a4f7 -> trunk/fbf3d2027daabbcb44d0af274b139be2a248a4f7 2025-09-07T06:25:54.9469099Z * [new tag] trunk/fca2601c9d628e1bd2d75c7318cd22c4e8c832aa -> trunk/fca2601c9d628e1bd2d75c7318cd22c4e8c832aa 2025-09-07T06:25:54.9469949Z * [new tag] trunk/fea20775ad96bdca972a1811d7d3372f368614ab -> trunk/fea20775ad96bdca972a1811d7d3372f368614ab 2025-09-07T06:25:54.9470701Z * [new tag] trunk/fefee081642f87419a21dc852f7167d4640443cd -> trunk/fefee081642f87419a21dc852f7167d4640443cd 2025-09-07T06:25:54.9471317Z * [new tag] v0.1.1 -> v0.1.1 2025-09-07T06:25:54.9472353Z * [new tag] v0.1.10 -> v0.1.10 2025-09-07T06:25:54.9473027Z * [new tag] v0.1.11 -> v0.1.11 2025-09-07T06:25:54.9474216Z * [new tag] v0.1.12 -> v0.1.12 2025-09-07T06:25:54.9475657Z * [new tag] v0.1.2 -> v0.1.2 2025-09-07T06:25:54.9476685Z * [new tag] v0.1.3 -> v0.1.3 2025-09-07T06:25:54.9477523Z * [new tag] v0.1.4 -> v0.1.4 2025-09-07T06:25:54.9478406Z * [new tag] v0.1.5 -> v0.1.5 2025-09-07T06:25:54.9479112Z * [new tag] v0.1.6 -> v0.1.6 2025-09-07T06:25:54.9479998Z * [new tag] v0.1.7 -> v0.1.7 2025-09-07T06:25:54.9480694Z * [new tag] v0.1.8 -> v0.1.8 2025-09-07T06:25:54.9481685Z * [new tag] v0.1.9 -> v0.1.9 2025-09-07T06:25:54.9482334Z * [new tag] v0.2.0 -> v0.2.0 2025-09-07T06:25:54.9483307Z * [new tag] v0.3.0 -> v0.3.0 2025-09-07T06:25:54.9484320Z * [new tag] v0.3.1 -> v0.3.1 2025-09-07T06:25:54.9485188Z * [new tag] v0.4.0 -> v0.4.0 2025-09-07T06:25:54.9485912Z * [new tag] v0.4.1 -> v0.4.1 2025-09-07T06:25:54.9486860Z * [new tag] v1.0.0 -> v1.0.0 2025-09-07T06:25:54.9487636Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-09-07T06:25:54.9488561Z * [new tag] v1.0.1 -> v1.0.1 2025-09-07T06:25:54.9489427Z * [new tag] v1.0rc0 -> v1.0rc0 2025-09-07T06:25:54.9490206Z * [new tag] v1.0rc1 -> v1.0rc1 2025-09-07T06:25:54.9490899Z * [new tag] v1.1.0 -> v1.1.0 2025-09-07T06:25:54.9491939Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-09-07T06:25:54.9492958Z * [new tag] v1.10.0 -> v1.10.0 2025-09-07T06:25:54.9493918Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-09-07T06:25:54.9494716Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-09-07T06:25:54.9495346Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-09-07T06:25:54.9496305Z * [new tag] v1.10.1 -> v1.10.1 2025-09-07T06:25:54.9496980Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-09-07T06:25:54.9497602Z * [new tag] v1.10.2 -> v1.10.2 2025-09-07T06:25:54.9498305Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-09-07T06:25:54.9499239Z * [new tag] v1.11.0 -> v1.11.0 2025-09-07T06:25:54.9500271Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-09-07T06:25:54.9501193Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-09-07T06:25:54.9502120Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-09-07T06:25:54.9503024Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-09-07T06:25:54.9503821Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-09-07T06:25:54.9504477Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-09-07T06:25:54.9505138Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-09-07T06:25:54.9506093Z * [new tag] v1.12.0 -> v1.12.0 2025-09-07T06:25:54.9506850Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-09-07T06:25:54.9507814Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-09-07T06:25:54.9508718Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-09-07T06:25:54.9509635Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-09-07T06:25:54.9510388Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-09-07T06:25:54.9511369Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-09-07T06:25:54.9511990Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-09-07T06:25:54.9512614Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-09-07T06:25:54.9513273Z * [new tag] v1.12.1 -> v1.12.1 2025-09-07T06:25:54.9514305Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-09-07T06:25:54.9515178Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-09-07T06:25:54.9516140Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-09-07T06:25:54.9517098Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-09-07T06:25:54.9517564Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-09-07T06:25:54.9518676Z * [new tag] v1.13.0 -> v1.13.0 2025-09-07T06:25:54.9519426Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-09-07T06:25:54.9520331Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-09-07T06:25:54.9521200Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-09-07T06:25:54.9522175Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-09-07T06:25:54.9522787Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-09-07T06:25:54.9523435Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-09-07T06:25:54.9524455Z * [new tag] v1.13.1 -> v1.13.1 2025-09-07T06:25:54.9525099Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-09-07T06:25:54.9526019Z * [new tag] v1.2.0 -> v1.2.0 2025-09-07T06:25:54.9526960Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-09-07T06:25:54.9527728Z * [new tag] v1.3.0 -> v1.3.0 2025-09-07T06:25:54.9528760Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-09-07T06:25:54.9529829Z * [new tag] v1.3.1 -> v1.3.1 2025-09-07T06:25:54.9530559Z * [new tag] v1.4.0 -> v1.4.0 2025-09-07T06:25:54.9531498Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-09-07T06:25:54.9532057Z * [new tag] v1.4.1 -> v1.4.1 2025-09-07T06:25:54.9533081Z * [new tag] v1.5.0 -> v1.5.0 2025-09-07T06:25:54.9534033Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-09-07T06:25:54.9534934Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-09-07T06:25:54.9535901Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-09-07T06:25:54.9536546Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-09-07T06:25:54.9537234Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-09-07T06:25:54.9538265Z * [new tag] v1.5.1 -> v1.5.1 2025-09-07T06:25:54.9538843Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-09-07T06:25:54.9539496Z * [new tag] v1.6.0 -> v1.6.0 2025-09-07T06:25:54.9540448Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-09-07T06:25:54.9541364Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-09-07T06:25:54.9542256Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-09-07T06:25:54.9543108Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-09-07T06:25:54.9544001Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-09-07T06:25:54.9544833Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-09-07T06:25:54.9545389Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-09-07T06:25:54.9546344Z * [new tag] v1.7.0 -> v1.7.0 2025-09-07T06:25:54.9547304Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-09-07T06:25:54.9548242Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-09-07T06:25:54.9549071Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-09-07T06:25:54.9549626Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-09-07T06:25:54.9550598Z * [new tag] v1.7.1 -> v1.7.1 2025-09-07T06:25:54.9551669Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-09-07T06:25:54.9552390Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-09-07T06:25:54.9553082Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-09-07T06:25:54.9554093Z * [new tag] v1.8.0 -> v1.8.0 2025-09-07T06:25:54.9554686Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-09-07T06:25:54.9555675Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-09-07T06:25:54.9556559Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-09-07T06:25:54.9557396Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-09-07T06:25:54.9558027Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-09-07T06:25:54.9558652Z * [new tag] v1.8.1 -> v1.8.1 2025-09-07T06:25:54.9559636Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-09-07T06:25:54.9560167Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-09-07T06:25:54.9560862Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-09-07T06:25:54.9562295Z * [new tag] v1.8.2 -> v1.8.2 2025-09-07T06:25:54.9562822Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-09-07T06:25:54.9563915Z * [new tag] v1.9.0 -> v1.9.0 2025-09-07T06:25:54.9564816Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-09-07T06:25:54.9565807Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-09-07T06:25:54.9566715Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-09-07T06:25:54.9567329Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-09-07T06:25:54.9568291Z * [new tag] v1.9.1 -> v1.9.1 2025-09-07T06:25:54.9569364Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-09-07T06:25:54.9569957Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-09-07T06:25:54.9570976Z * [new tag] v2.0.0 -> v2.0.0 2025-09-07T06:25:54.9571693Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-09-07T06:25:54.9572733Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-09-07T06:25:54.9573582Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-09-07T06:25:54.9574766Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-09-07T06:25:54.9575504Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-09-07T06:25:54.9576185Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-09-07T06:25:54.9577235Z * [new tag] v2.0.1 -> v2.0.1 2025-09-07T06:25:54.9578153Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-09-07T06:25:54.9578701Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-09-07T06:25:54.9579620Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-09-07T06:25:54.9580348Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-09-07T06:25:54.9581784Z * [new tag] v2.1.0 -> v2.1.0 2025-09-07T06:25:54.9582523Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-09-07T06:25:54.9583456Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-09-07T06:25:54.9584835Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-09-07T06:25:54.9585727Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-09-07T06:25:54.9586627Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-09-07T06:25:54.9587301Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-09-07T06:25:54.9588193Z * [new tag] v2.1.1 -> v2.1.1 2025-09-07T06:25:54.9588969Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-09-07T06:25:54.9589867Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-09-07T06:25:54.9590894Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-09-07T06:25:54.9591750Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-09-07T06:25:54.9592529Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-09-07T06:25:54.9593173Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-09-07T06:25:54.9594103Z * [new tag] v2.1.2 -> v2.1.2 2025-09-07T06:25:54.9595015Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-09-07T06:25:54.9595938Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-09-07T06:25:54.9596622Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-09-07T06:25:54.9597519Z * [new tag] v2.2.0 -> v2.2.0 2025-09-07T06:25:54.9598389Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-09-07T06:25:54.9599132Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-09-07T06:25:54.9600084Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-09-07T06:25:54.9600790Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-09-07T06:25:54.9601732Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-09-07T06:25:54.9602491Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-09-07T06:25:54.9603144Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-09-07T06:25:54.9603826Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-09-07T06:25:54.9604964Z * [new tag] v2.2.1 -> v2.2.1 2025-09-07T06:25:54.9605934Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-09-07T06:25:54.9606545Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-09-07T06:25:54.9607134Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-09-07T06:25:54.9607817Z * [new tag] v2.2.2 -> v2.2.2 2025-09-07T06:25:54.9608998Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-09-07T06:25:54.9609565Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-09-07T06:25:54.9610252Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-09-07T06:25:54.9611192Z * [new tag] v2.3.0 -> v2.3.0 2025-09-07T06:25:54.9611944Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-09-07T06:25:54.9612922Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-09-07T06:25:54.9613940Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-09-07T06:25:54.9614536Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-09-07T06:25:54.9615451Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-09-07T06:25:54.9616346Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-09-07T06:25:54.9617120Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-09-07T06:25:54.9618048Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-09-07T06:25:54.9618615Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-09-07T06:25:54.9619661Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-09-07T06:25:54.9620403Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-09-07T06:25:54.9621071Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-09-07T06:25:54.9621798Z * [new tag] v2.3.1 -> v2.3.1 2025-09-07T06:25:54.9622687Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-09-07T06:25:54.9623456Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-09-07T06:25:54.9624476Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-09-07T06:25:54.9625234Z * [new tag] v2.4.0 -> v2.4.0 2025-09-07T06:25:54.9626182Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-09-07T06:25:54.9626962Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-09-07T06:25:54.9627899Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-09-07T06:25:54.9628668Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-09-07T06:25:54.9629698Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-09-07T06:25:54.9630578Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-09-07T06:25:54.9631508Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-09-07T06:25:54.9632280Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-09-07T06:25:54.9633302Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-09-07T06:25:54.9633952Z * [new tag] v2.4.1 -> v2.4.1 2025-09-07T06:25:54.9634970Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-09-07T06:25:54.9635947Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-09-07T06:25:54.9636947Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-09-07T06:25:54.9637799Z * [new tag] v2.5.0 -> v2.5.0 2025-09-07T06:25:54.9639035Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-09-07T06:25:54.9639593Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-09-07T06:25:54.9640688Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-09-07T06:25:54.9641417Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-09-07T06:25:54.9642361Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-09-07T06:25:54.9643281Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-09-07T06:25:54.9644305Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-09-07T06:25:54.9645254Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-09-07T06:25:54.9646102Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-09-07T06:25:54.9647020Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-09-07T06:25:54.9647556Z * [new tag] v2.5.1 -> v2.5.1 2025-09-07T06:25:54.9648302Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-09-07T06:25:54.9648949Z * [new tag] v2.6.0 -> v2.6.0 2025-09-07T06:25:54.9650008Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-09-07T06:25:54.9650984Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-09-07T06:25:54.9651879Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-09-07T06:25:54.9652613Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-09-07T06:25:54.9653854Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-09-07T06:25:54.9654913Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-09-07T06:25:54.9655794Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-09-07T06:25:54.9656776Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-09-07T06:25:54.9657686Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-09-07T06:25:54.9667791Z * [new tag] v2.7.0 -> v2.7.0 2025-09-07T06:25:54.9668094Z * [new tag] v2.7.0-rc1 -> v2.7.0-rc1 2025-09-07T06:25:54.9668255Z * [new tag] v2.7.0-rc10 -> v2.7.0-rc10 2025-09-07T06:25:54.9668406Z * [new tag] v2.7.0-rc2 -> v2.7.0-rc2 2025-09-07T06:25:54.9668572Z * [new tag] v2.7.0-rc3 -> v2.7.0-rc3 2025-09-07T06:25:54.9668717Z * [new tag] v2.7.0-rc4 -> v2.7.0-rc4 2025-09-07T06:25:54.9668872Z * [new tag] v2.7.0-rc5 -> v2.7.0-rc5 2025-09-07T06:25:54.9669014Z * [new tag] v2.7.0-rc6 -> v2.7.0-rc6 2025-09-07T06:25:54.9669152Z * [new tag] v2.7.0-rc7 -> v2.7.0-rc7 2025-09-07T06:25:54.9669304Z * [new tag] v2.7.0-rc8 -> v2.7.0-rc8 2025-09-07T06:25:54.9669450Z * [new tag] v2.7.0-rc9 -> v2.7.0-rc9 2025-09-07T06:25:54.9669606Z * [new tag] v2.7.1 -> v2.7.1 2025-09-07T06:25:54.9669748Z * [new tag] v2.7.1-rc1 -> v2.7.1-rc1 2025-09-07T06:25:54.9670718Z * [new tag] v2.7.1-rc2 -> v2.7.1-rc2 2025-09-07T06:25:54.9671602Z * [new tag] v2.7.1-rc3 -> v2.7.1-rc3 2025-09-07T06:25:54.9672560Z * [new tag] v2.7.1-rc4 -> v2.7.1-rc4 2025-09-07T06:25:54.9673583Z * [new tag] v2.7.1-rc5 -> v2.7.1-rc5 2025-09-07T06:25:54.9674280Z * [new tag] v2.8.0 -> v2.8.0 2025-09-07T06:25:54.9675268Z * [new tag] v2.8.0-rc1 -> v2.8.0-rc1 2025-09-07T06:25:54.9676201Z * [new tag] v2.8.0-rc2 -> v2.8.0-rc2 2025-09-07T06:25:54.9677229Z * [new tag] v2.8.0-rc3 -> v2.8.0-rc3 2025-09-07T06:25:54.9678200Z * [new tag] v2.8.0-rc4 -> v2.8.0-rc4 2025-09-07T06:25:54.9679252Z * [new tag] v2.8.0-rc5 -> v2.8.0-rc5 2025-09-07T06:25:54.9680010Z * [new tag] v2.8.0-rc6 -> v2.8.0-rc6 2025-09-07T06:25:54.9680920Z * [new tag] v2.8.0-rc7 -> v2.8.0-rc7 2025-09-07T06:25:54.9681748Z * [new tag] v2.8.0-rc8 -> v2.8.0-rc8 2025-09-07T06:25:54.9682709Z * [new tag] whc_flight_1 -> whc_flight_1 2025-09-07T06:25:54.9683644Z * [new tag] whc_flight_2 -> whc_flight_2 2025-09-07T06:25:54.9684415Z * [new tag] whc_flight_4 -> whc_flight_4 2025-09-07T06:25:55.0347299Z [command]/usr/bin/git rev-parse --verify --quiet 93fb23d6fae7c4e82c4239a1033e522088742634^{object} 2025-09-07T06:25:55.0374533Z 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:25:55.0378216Z ##[endgroup] 2025-09-07T06:25:55.0378513Z ##[group]Determining the checkout info 2025-09-07T06:25:55.0379503Z ##[endgroup] 2025-09-07T06:25:55.0383231Z [command]/usr/bin/git sparse-checkout disable 2025-09-07T06:25:55.0423890Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-07T06:25:55.0466174Z ##[group]Checking out the ref 2025-09-07T06:25:55.0469209Z [command]/usr/bin/git checkout --progress --force 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:25:56.0757804Z Updating files: 85% (16609/19405) 2025-09-07T06:25:56.0911927Z Updating files: 86% (16689/19405) 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to '93fb23d6fae7c4e82c4239a1033e522088742634'. 2025-09-07T06:25:56.3208506Z 2025-09-07T06:25:56.3208744Z You are in 'detached HEAD' state. You can look around, make experimental 2025-09-07T06:25:56.3209315Z changes and commit them, and you can discard any commits you make in this 2025-09-07T06:25:56.3209872Z state without impacting any branches by switching back to a branch. 2025-09-07T06:25:56.3210239Z 2025-09-07T06:25:56.3210453Z If you want to create a new branch to retain commits you create, you may 2025-09-07T06:25:56.3210974Z do so (now or later) by using -c with the switch command. Example: 2025-09-07T06:25:56.3211275Z 2025-09-07T06:25:56.3211399Z git switch -c 2025-09-07T06:25:56.3211601Z 2025-09-07T06:25:56.3211721Z Or undo this operation with: 2025-09-07T06:25:56.3211913Z 2025-09-07T06:25:56.3212003Z git switch - 2025-09-07T06:25:56.3212148Z 2025-09-07T06:25:56.3212388Z Turn off this advice by setting config variable advice.detachedHead to false 2025-09-07T06:25:56.3212756Z 2025-09-07T06:25:56.3212939Z HEAD is now at 93fb23d6fae Build vLLM nightly wheels (#162000) 2025-09-07T06:25:56.3286163Z ##[endgroup] 2025-09-07T06:25:56.3286620Z ##[group]Setting up auth for fetching submodules 2025-09-07T06:25:56.3292321Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-07T06:25:56.3340209Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-07T06:25:56.3367347Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-07T06:25:56.3394446Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-07T06:25:56.3418385Z ##[endgroup] 2025-09-07T06:25:56.3419007Z ##[group]Fetching submodules 2025-09-07T06:25:56.3422966Z [command]/usr/bin/git submodule sync --recursive 2025-09-07T06:25:56.3732170Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-09-07T06:25:56.4091764Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-09-07T06:25:56.4298601Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-09-07T06:25:56.4301175Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-09-07T06:25:56.4304260Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-09-07T06:25:56.4307428Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-09-07T06:25:56.4311157Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-09-07T06:25:56.4325918Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-09-07T06:25:56.4329378Z Submodule 'third_party/aiter' (https://github.com/ROCm/aiter.git) registered for path 'third_party/aiter' 2025-09-07T06:25:56.4332972Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-09-07T06:25:56.4336966Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-09-07T06:25:56.4340494Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-09-07T06:25:56.4344466Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-09-07T06:25:56.4359387Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-09-07T06:25:56.4363420Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-09-07T06:25:56.4367699Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-09-07T06:25:56.4372691Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-09-07T06:25:56.4377506Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-09-07T06:25:56.4394661Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-09-07T06:25:56.4399445Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:25:56.4403929Z Submodule 'third_party/gloo' (https://github.com/pytorch/gloo) registered for path 'third_party/gloo' 2025-09-07T06:25:56.4408905Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-09-07T06:25:56.4413621Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-09-07T06:25:56.4418609Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-09-07T06:25:56.4435104Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-09-07T06:25:56.4440245Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-09-07T06:25:56.4445549Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-09-07T06:25:56.4450820Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-09-07T06:25:56.4456087Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-09-07T06:25:56.4473407Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-09-07T06:25:56.4479001Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-09-07T06:25:56.4484702Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-09-07T06:25:56.4490578Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-09-07T06:25:56.4496468Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-09-07T06:25:56.4502386Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-09-07T06:25:56.4521043Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-09-07T06:25:56.4527339Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-09-07T06:25:56.4533753Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-09-07T06:25:56.4568140Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-09-07T06:25:56.7050979Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-09-07T06:25:56.7052458Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-09-07T06:25:56.7087382Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-09-07T06:26:06.7335456Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-09-07T06:26:06.7337221Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-09-07T06:26:06.7338959Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-09-07T06:26:06.7340640Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-09-07T06:26:06.7342256Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-09-07T06:26:06.7346038Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-09-07T06:26:06.7347896Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-09-07T06:26:06.7350517Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-09-07T06:26:06.7352394Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-09-07T06:26:06.7354769Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2025-09-07T06:26:06.7356389Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-09-07T06:26:06.7449031Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2025-09-07T06:26:06.7450355Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-09-07T06:26:06.7451679Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2025-09-07T06:26:06.7453002Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-09-07T06:26:06.7454347Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-09-07T06:26:06.7455962Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-09-07T06:26:06.7457350Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2025-09-07T06:26:06.7529387Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2025-09-07T06:26:07.2252034Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-09-07T06:26:07.2253722Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter'... 2025-09-07T06:26:07.2255267Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-09-07T06:26:07.2256776Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-09-07T06:26:07.2272533Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-09-07T06:26:13.9871551Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-09-07T06:26:13.9873656Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-09-07T06:26:13.9875119Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-09-07T06:26:13.9876513Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-09-07T06:26:13.9877881Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-09-07T06:26:14.0872985Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-09-07T06:26:26.4373612Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-09-07T06:26:36.4154308Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-09-07T06:26:36.5155809Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-09-07T06:26:37.8770893Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-09-07T06:26:37.8896569Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-09-07T06:26:37.8991982Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-09-07T06:26:37.9243056Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-09-07T06:26:38.0016615Z Submodule path 'third_party/NVTX': checked out '2942f167cc30c5e3a44a2aecd5b0d9c07ff61a07' 2025-09-07T06:26:38.0588912Z Submodule path 'third_party/VulkanMemoryAllocator': checked out '1d8f600fd424278486eade7ed3e877c99f0846b1' 2025-09-07T06:26:38.7908099Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-09-07T06:26:38.9483850Z Submodule path 'third_party/aiter': checked out '01aae101b9e5e94d6c16a9514c9fb8df99c93150' 2025-09-07T06:26:38.9503802Z Submodule '3rdparty/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:26:38.9530201Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter/3rdparty/composable_kernel'... 2025-09-07T06:26:43.0257440Z Submodule path 'third_party/aiter/3rdparty/composable_kernel': checked out 'cffe8fa2a442ac8e80dd236a1a5d24fe3d7e0cbf' 2025-09-07T06:26:43.0496488Z Submodule path 'third_party/benchmark': checked out '299e5928955cc62af9968370293b916f5130916f' 2025-09-07T06:26:43.3769785Z Submodule path 'third_party/composable_kernel': checked out '7fe50dc3da2069d6645d9deb8c017a876472a977' 2025-09-07T06:26:43.4243518Z Submodule path 'third_party/cpp-httplib': checked out '89c932f313c6437c38f2982869beacc89c2f2246' 2025-09-07T06:26:43.5230927Z Submodule path 'third_party/cpuinfo': checked out '5e3d2445e6a84d9599bee2bf78edbb4d80865e1d' 2025-09-07T06:26:43.5686511Z Submodule path 'third_party/cudnn_frontend': checked out 'f937055efc6d414d11f4c6577e3977fe74f35fb6' 2025-09-07T06:26:44.1999355Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491' 2025-09-07T06:26:44.3482248Z Submodule path 'third_party/fbgemm': checked out '4b39c551efe15e6bbade20565b0ceb2d8ce3352d' 2025-09-07T06:26:44.3503163Z Submodule 'external/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/external/asmjit' 2025-09-07T06:26:44.3505509Z Submodule 'external/composable_kernel' (https://github.com/jwfromm/composable_kernel.git) registered for path 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:26:44.3507988Z Submodule 'external/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:26:44.3510663Z Submodule 'external/cutlass' (https://github.com/jwfromm/cutlass) registered for path 'third_party/fbgemm/external/cutlass' 2025-09-07T06:26:44.3513550Z Submodule 'external/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/external/googletest' 2025-09-07T06:26:44.3516710Z Submodule 'external/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:26:44.3519551Z Submodule 'external/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/fbgemm/external/json' 2025-09-07T06:26:44.3549922Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/asmjit'... 2025-09-07T06:26:46.1951632Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/hipify_torch'... 2025-09-07T06:26:46.1953116Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cpuinfo'... 2025-09-07T06:26:46.2280867Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/composable_kernel'... 2025-09-07T06:26:46.3514458Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/googletest'... 2025-09-07T06:26:46.4515748Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cutlass'... 2025-09-07T06:26:47.2960432Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/json'... 2025-09-07T06:26:58.7699433Z Submodule path 'third_party/fbgemm/external/asmjit': checked out 'a3199e8857792cd10b7589ff5d58343d2c9008ea' 2025-09-07T06:26:59.0319724Z Submodule path 'third_party/fbgemm/external/composable_kernel': checked out 'b1281b8b08d973a7064f864f47eeb30f3e2596e9' 2025-09-07T06:26:59.1324070Z Submodule path 'third_party/fbgemm/external/cpuinfo': checked out '6543fec09b2f04ac4a666882998b534afc9c1349' 2025-09-07T06:26:59.7573694Z Submodule path 'third_party/fbgemm/external/cutlass': checked out '311f3c8e51dc0eb56310cfc6980bf63d0fbd7917' 2025-09-07T06:26:59.8048586Z Submodule path 'third_party/fbgemm/external/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-09-07T06:26:59.8173130Z Submodule path 'third_party/fbgemm/external/hipify_torch': checked out '63b6a7b541fa7f08f8475ca7d74054db36ff2691' 2025-09-07T06:26:59.9271157Z Submodule path 'third_party/fbgemm/external/json': checked out '9cca280a4d0ccf0c08f47a99aa71d1b0e52f8d03' 2025-09-07T06:26:59.9979348Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-09-07T06:27:00.0000881Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:27:00.0002712Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:27:00.0032974Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-09-07T06:27:03.8764237Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-09-07T06:27:04.1190073Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-09-07T06:27:04.6855976Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-09-07T06:27:04.8276350Z Submodule path 'third_party/flatbuffers': checked out 'a2cd1ea3b6d3fee220106b5fed3f7ce8da9eb757' 2025-09-07T06:27:04.8600782Z Submodule path 'third_party/fmt': checked out '40626af88bd7df9a5fb80be7b25ac85b122d6c21' 2025-09-07T06:27:04.9009325Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-09-07T06:27:04.9267890Z Submodule path 'third_party/gloo': checked out 'c7b7b022c124d9643957d9bd55f57ac59fce8fa2' 2025-09-07T06:27:04.9729276Z Submodule path 'third_party/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-09-07T06:27:04.9865099Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3' 2025-09-07T06:27:04.9882676Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-09-07T06:27:04.9908246Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-09-07T06:27:20.4921041Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d' 2025-09-07T06:27:20.5127928Z Submodule path 'third_party/ittapi': checked out 'dec1d23ca65ab069d225dfe40dea14f455170959' 2025-09-07T06:27:20.6011412Z Submodule path 'third_party/kineto': checked out '5e7501833f1021ce6f618572d3baf657b6319658' 2025-09-07T06:27:20.6030367Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:27:20.6032889Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:27:20.6035879Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:27:20.6064873Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-09-07T06:27:21.4634730Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-09-07T06:27:22.3932706Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-09-07T06:27:22.4769270Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-09-07T06:27:22.4787875Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:27:22.4790293Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:27:22.4793012Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:27:22.4795776Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:27:22.4798641Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:27:22.4801739Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:27:22.4804762Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:27:22.4808207Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:27:22.4837897Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-09-07T06:27:24.5499982Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-09-07T06:27:24.5502706Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-09-07T06:27:24.5505001Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-09-07T06:27:24.5507418Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-09-07T06:27:24.5509770Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-09-07T06:27:24.6501521Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-09-07T06:27:24.9188621Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-09-07T06:27:31.5478254Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-09-07T06:27:31.5663037Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-09-07T06:27:31.6042048Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-09-07T06:27:31.6187940Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-09-07T06:27:31.6204453Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:27:31.6232380Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-09-07T06:27:31.9079610Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-09-07T06:27:31.9269213Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-09-07T06:27:31.9686411Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-09-07T06:27:32.0677500Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-09-07T06:27:32.0850862Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-09-07T06:27:32.1195510Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-09-07T06:27:32.1796046Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-09-07T06:27:32.2240419Z Submodule path 'third_party/kleidiai': checked out 'cca02c2f69dd18e1f12647c1c0bdc8cf90e680c7' 2025-09-07T06:27:32.2637305Z Submodule path 'third_party/mimalloc': checked out 'fbd8b99c2b828428947d70fdc046bb55609be93e' 2025-09-07T06:27:32.3748587Z Submodule path 'third_party/nlohmann': checked out '55f93686c01528224f448c19128836e7df245f72' 2025-09-07T06:27:32.7806402Z Submodule path 'third_party/onnx': checked out 'e709452ef2bbc1d113faf678c24e6d3467696e83' 2025-09-07T06:27:32.7846120Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-09-07T06:27:32.7874590Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-09-07T06:27:33.9713831Z Submodule path 'third_party/onnx/third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4' 2025-09-07T06:27:34.0433873Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2025-09-07T06:27:34.0454053Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:27:34.0456760Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:27:34.0459641Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:27:34.0462694Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:27:34.0466690Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:27:34.0469367Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:27:34.0472332Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:27:34.0476040Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:34.0504804Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-09-07T06:27:34.6426851Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-09-07T06:27:34.6429106Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2025-09-07T06:27:34.6431325Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2025-09-07T06:27:34.6433372Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2025-09-07T06:27:34.7428044Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2025-09-07T06:27:35.9700927Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2025-09-07T06:27:45.8744567Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'... 2025-09-07T06:27:45.8942854Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2' 2025-09-07T06:27:45.9622336Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1' 2025-09-07T06:27:45.9799500Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa' 2025-09-07T06:27:46.0866711Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d' 2025-09-07T06:27:46.1134819Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce' 2025-09-07T06:27:46.1298520Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5' 2025-09-07T06:27:46.1463171Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d' 2025-09-07T06:27:46.1479915Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:46.1482296Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:46.1510757Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'... 2025-09-07T06:27:48.5095087Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'... 2025-09-07T06:27:48.7606139Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4' 2025-09-07T06:27:48.8079551Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2025-09-07T06:27:49.2858484Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50' 2025-09-07T06:27:49.2984141Z Submodule path 'third_party/pocketfft': 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2025-09-07T06:27:54.0154981Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url" 2025-09-07T06:27:54.0456245Z Entering 'android/libs/fbjni' 2025-09-07T06:27:54.0509126Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2025-09-07T06:27:54.0524502Z Entering 'third_party/FP16' 2025-09-07T06:27:54.0576611Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2025-09-07T06:27:54.0593190Z Entering 'third_party/FXdiv' 2025-09-07T06:27:54.0644748Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2025-09-07T06:27:54.0661053Z Entering 'third_party/NNPACK' 2025-09-07T06:27:54.0712322Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config remote.origin.url 2025-09-07T06:27:54.3794104Z Entering 'third_party/kleidiai' 2025-09-07T06:27:54.3856039Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kleidiai/config remote.origin.url 2025-09-07T06:27:54.3872278Z Entering 'third_party/mimalloc' 2025-09-07T06:27:54.3926376Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2025-09-07T06:27:54.3942766Z Entering 'third_party/nlohmann' 2025-09-07T06:27:54.3996466Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2025-09-07T06:27:54.4014885Z Entering 'third_party/onnx' 2025-09-07T06:27:54.4069141Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2025-09-07T06:27:54.4104404Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:27:54.4157837Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:27:54.4177799Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:27:54.4233559Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config remote.origin.url 2025-09-07T06:27:54.4252731Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:27:54.4306668Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config remote.origin.url 2025-09-07T06:27:54.4322291Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:27:54.4376445Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:27:54.4392933Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:27:54.4445800Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config remote.origin.url 2025-09-07T06:27:54.4461782Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:27:54.4786604Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config remote.origin.url 2025-09-07T06:27:54.4805574Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:27:54.4859945Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config remote.origin.url 2025-09-07T06:27:54.4876014Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:27:54.4928936Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2025-09-07T06:27:54.5156305Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:54.5209349Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2025-09-07T06:27:54.5245653Z Entering 'third_party/pocketfft' 2025-09-07T06:27:54.5300144Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2025-09-07T06:27:54.5317179Z Entering 'third_party/protobuf' 2025-09-07T06:27:54.5369686Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2025-09-07T06:27:54.5389353Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:27:54.5443333Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2025-09-07T06:27:54.5459612Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:27:54.5513600Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:27:54.5531768Z Entering 'third_party/psimd' 2025-09-07T06:27:54.5585847Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2025-09-07T06:27:54.5602869Z Entering 'third_party/pthreadpool' 2025-09-07T06:27:54.5655575Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2025-09-07T06:27:54.5672004Z Entering 'third_party/pybind11' 2025-09-07T06:27:54.5723482Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:27:54.5740397Z Entering 'third_party/python-peachpy' 2025-09-07T06:27:54.5795038Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2025-09-07T06:27:54.5813452Z Entering 'third_party/sleef' 2025-09-07T06:27:54.5866832Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2025-09-07T06:27:54.5884360Z Entering 'third_party/tensorpipe' 2025-09-07T06:27:54.5937398Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2025-09-07T06:27:54.5953612Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:27:54.6006005Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:27:54.6021548Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:27:54.6074596Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2025-09-07T06:27:54.6090537Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:27:54.6141725Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2025-09-07T06:27:54.6157027Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:27:54.6208592Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:27:54.6223008Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:27:54.6275458Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2025-09-07T06:27:54.6893848Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-07T06:27:54.7196395Z Entering 'android/libs/fbjni' 2025-09-07T06:27:54.7239777Z Entering 'third_party/FP16' 2025-09-07T06:27:54.7281731Z Entering 'third_party/FXdiv' 2025-09-07T06:27:54.7335960Z Entering 'third_party/NNPACK' 2025-09-07T06:27:54.7370238Z Entering 'third_party/NVTX' 2025-09-07T06:27:54.7413250Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:27:54.7455084Z Entering 'third_party/XNNPACK' 2025-09-07T06:27:54.7512436Z Entering 'third_party/aiter' 2025-09-07T06:27:54.7556547Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:27:54.7605863Z Entering 'third_party/benchmark' 2025-09-07T06:27:54.7648590Z Entering 'third_party/composable_kernel' 2025-09-07T06:27:54.7700087Z Entering 'third_party/cpp-httplib' 2025-09-07T06:27:54.7742621Z Entering 'third_party/cpuinfo' 2025-09-07T06:27:54.7785326Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:27:54.7830484Z Entering 'third_party/cutlass' 2025-09-07T06:27:54.7883369Z Entering 'third_party/fbgemm' 2025-09-07T06:27:54.7929900Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:27:54.7970797Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:27:54.8019073Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:27:54.8061057Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:27:54.8113083Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:27:54.8154310Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:27:54.8195380Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:27:54.8240042Z Entering 'third_party/flash-attention' 2025-09-07T06:27:54.8287272Z Entering 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2025-09-07T06:27:54.8911918Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:27:54.8954153Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:27:54.8995683Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:27:54.9038659Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:27:54.9080945Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:27:54.9122082Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:27:54.9166219Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:27:54.9209276Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:27:54.9250727Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:27:54.9294409Z Entering 'third_party/kleidiai' 2025-09-07T06:27:54.9341373Z Entering 'third_party/mimalloc' 2025-09-07T06:27:54.9384852Z Entering 'third_party/nlohmann' 2025-09-07T06:27:54.9428023Z Entering 'third_party/onnx' 2025-09-07T06:27:54.9487985Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:27:54.9532668Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:27:54.9582746Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:27:54.9623715Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:27:54.9664177Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:27:54.9704855Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:27:54.9747119Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:27:54.9788726Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:27:54.9833935Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:27:54.9874266Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:54.9916626Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:54.9959844Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:55.0020913Z Entering 'third_party/pocketfft' 2025-09-07T06:27:55.0065186Z Entering 'third_party/protobuf' 2025-09-07T06:27:55.0110885Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:27:55.0151378Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:27:55.0195301Z Entering 'third_party/psimd' 2025-09-07T06:27:55.0238987Z Entering 'third_party/pthreadpool' 2025-09-07T06:27:55.0282070Z Entering 'third_party/pybind11' 2025-09-07T06:27:55.0326923Z Entering 'third_party/python-peachpy' 2025-09-07T06:27:55.0370815Z Entering 'third_party/sleef' 2025-09-07T06:27:55.0413754Z Entering 'third_party/tensorpipe' 2025-09-07T06:27:55.0455302Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:27:55.0496721Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:27:55.0537432Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:27:55.0579807Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:27:55.0620597Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:27:55.0680170Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-07T06:27:55.0985296Z Entering 'android/libs/fbjni' 2025-09-07T06:27:55.1028722Z Entering 'third_party/FP16' 2025-09-07T06:27:55.1072029Z Entering 'third_party/FXdiv' 2025-09-07T06:27:55.1118381Z Entering 'third_party/NNPACK' 2025-09-07T06:27:55.1160685Z Entering 'third_party/NVTX' 2025-09-07T06:27:55.1204581Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:27:55.1246939Z Entering 'third_party/XNNPACK' 2025-09-07T06:27:55.1303804Z Entering 'third_party/aiter' 2025-09-07T06:27:55.1349458Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:27:55.1403242Z Entering 'third_party/benchmark' 2025-09-07T06:27:55.1446104Z Entering 'third_party/composable_kernel' 2025-09-07T06:27:55.1497375Z Entering 'third_party/cpp-httplib' 2025-09-07T06:27:55.1539547Z Entering 'third_party/cpuinfo' 2025-09-07T06:27:55.1585090Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:27:55.1626971Z Entering 'third_party/cutlass' 2025-09-07T06:27:55.1679973Z Entering 'third_party/fbgemm' 2025-09-07T06:27:55.1724207Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:27:55.1765343Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:27:55.1812764Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:27:55.1855888Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:27:55.1908377Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:27:55.1949611Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:27:55.1990927Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:27:55.2035162Z Entering 'third_party/flash-attention' 2025-09-07T06:27:55.2080116Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:27:55.2126920Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:27:55.2180760Z Entering 'third_party/flatbuffers' 2025-09-07T06:27:55.2226369Z Entering 'third_party/fmt' 2025-09-07T06:27:55.2268941Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:27:55.2313817Z Entering 'third_party/gloo' 2025-09-07T06:27:55.2357046Z Entering 'third_party/googletest' 2025-09-07T06:27:55.2399873Z Entering 'third_party/ideep' 2025-09-07T06:27:55.2442722Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:27:55.2492326Z Entering 'third_party/ittapi' 2025-09-07T06:27:55.2536405Z Entering 'third_party/kineto' 2025-09-07T06:27:55.2579888Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:27:55.2620831Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:27:55.2664144Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:27:55.2706813Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:27:55.2748368Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:27:55.2788017Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:27:55.2831454Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:27:55.2872054Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:27:55.2914061Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:27:55.2958005Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:27:55.3001092Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:27:55.3041754Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:27:55.3084593Z Entering 'third_party/kleidiai' 2025-09-07T06:27:55.3128789Z Entering 'third_party/mimalloc' 2025-09-07T06:27:55.3172720Z Entering 'third_party/nlohmann' 2025-09-07T06:27:55.3218072Z Entering 'third_party/onnx' 2025-09-07T06:27:55.3279503Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:27:55.3323604Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:27:55.3368037Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:27:55.3410893Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:27:55.3452768Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:27:55.3495880Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:27:55.3539912Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:27:55.3582662Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:27:55.3624177Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:27:55.3665837Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:55.3711529Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:55.3756145Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:55.3819665Z Entering 'third_party/pocketfft' 2025-09-07T06:27:55.3865245Z Entering 'third_party/protobuf' 2025-09-07T06:27:55.3911764Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:27:55.3953081Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:27:55.3996742Z Entering 'third_party/psimd' 2025-09-07T06:27:55.4046653Z Entering 'third_party/pthreadpool' 2025-09-07T06:27:55.4088972Z Entering 'third_party/pybind11' 2025-09-07T06:27:55.4131275Z Entering 'third_party/python-peachpy' 2025-09-07T06:27:55.4173697Z Entering 'third_party/sleef' 2025-09-07T06:27:55.4215836Z Entering 'third_party/tensorpipe' 2025-09-07T06:27:55.4258769Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:27:55.4300338Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:27:55.4340640Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:27:55.4381514Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:27:55.4423020Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:27:55.4478722Z ##[endgroup] 2025-09-07T06:27:55.4524916Z [command]/usr/bin/git log -1 --format=%H 2025-09-07T06:27:55.4549240Z 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:27:55.4656533Z ##[group]Run cd "${GITHUB_WORKSPACE}" 2025-09-07T06:27:55.4656916Z cd "${GITHUB_WORKSPACE}" 2025-09-07T06:27:55.4657245Z # Clean stale submodule dirs 2025-09-07T06:27:55.4657585Z if [ -z "${NO_SUDO}" ]; then 2025-09-07T06:27:55.4657986Z  sudo git submodule foreach --recursive git clean -ffdx 2025-09-07T06:27:55.4658384Z else 2025-09-07T06:27:55.4658823Z  git submodule foreach --recursive git clean -ffdx 2025-09-07T06:27:55.4659205Z fi 2025-09-07T06:27:55.4668926Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:55.4669306Z env: 2025-09-07T06:27:55.4669539Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:55.4669809Z NO_SUDO: true 2025-09-07T06:27:55.4670026Z ##[endgroup] 2025-09-07T06:27:55.4996607Z Entering 'android/libs/fbjni' 2025-09-07T06:27:55.5031375Z Entering 'third_party/FP16' 2025-09-07T06:27:55.5062486Z Entering 'third_party/FXdiv' 2025-09-07T06:27:55.5097693Z Entering 'third_party/NNPACK' 2025-09-07T06:27:55.5132596Z Entering 'third_party/NVTX' 2025-09-07T06:27:55.5169616Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:27:55.5202319Z Entering 'third_party/XNNPACK' 2025-09-07T06:27:55.5326906Z Entering 'third_party/aiter' 2025-09-07T06:27:55.5370361Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:27:55.5478340Z Entering 'third_party/benchmark' 2025-09-07T06:27:55.5510622Z Entering 'third_party/composable_kernel' 2025-09-07T06:27:55.5625962Z Entering 'third_party/cpp-httplib' 2025-09-07T06:27:55.5658325Z Entering 'third_party/cpuinfo' 2025-09-07T06:27:55.5693107Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:27:55.5726832Z Entering 'third_party/cutlass' 2025-09-07T06:27:55.5827046Z Entering 'third_party/fbgemm' 2025-09-07T06:27:55.5887295Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:27:55.5919209Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:27:55.6025727Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:27:55.6059971Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:27:55.6153587Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:27:55.6186500Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:27:55.6215460Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:27:55.6260536Z Entering 'third_party/flash-attention' 2025-09-07T06:27:55.6300930Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:27:55.6396858Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:27:55.6483896Z Entering 'third_party/flatbuffers' 2025-09-07T06:27:55.6557136Z Entering 'third_party/fmt' 2025-09-07T06:27:55.6589233Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:27:55.6621572Z Entering 'third_party/gloo' 2025-09-07T06:27:55.6654275Z Entering 'third_party/googletest' 2025-09-07T06:27:55.6687475Z Entering 'third_party/ideep' 2025-09-07T06:27:55.6717641Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:27:55.6799474Z Entering 'third_party/ittapi' 2025-09-07T06:27:55.6835118Z Entering 'third_party/kineto' 2025-09-07T06:27:55.6871428Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:27:55.6906851Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:27:55.6951250Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:27:55.6984318Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:27:55.7016706Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:27:55.7045691Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:27:55.7075535Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:27:55.7106080Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:27:55.7140826Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:27:55.7180613Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:27:55.7213226Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:27:55.7245070Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:27:55.7279021Z Entering 'third_party/kleidiai' 2025-09-07T06:27:55.7318805Z Entering 'third_party/mimalloc' 2025-09-07T06:27:55.7351466Z Entering 'third_party/nlohmann' 2025-09-07T06:27:55.7395830Z Entering 'third_party/onnx' 2025-09-07T06:27:55.7720143Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:27:55.7758570Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:27:55.7814469Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:27:55.7845540Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:27:55.7877941Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:27:55.7906672Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:27:55.7948213Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:27:55.7979056Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:27:55.8010825Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:27:55.8042850Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:55.8088619Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:55.8123763Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:55.8384942Z Entering 'third_party/pocketfft' 2025-09-07T06:27:55.8416843Z Entering 'third_party/protobuf' 2025-09-07T06:27:55.8493742Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:27:55.8525664Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:27:55.8561283Z Entering 'third_party/psimd' 2025-09-07T06:27:55.8592090Z Entering 'third_party/pthreadpool' 2025-09-07T06:27:55.8623017Z Entering 'third_party/pybind11' 2025-09-07T06:27:55.8656784Z Entering 'third_party/python-peachpy' 2025-09-07T06:27:55.8688608Z Entering 'third_party/sleef' 2025-09-07T06:27:55.8722312Z Entering 'third_party/tensorpipe' 2025-09-07T06:27:55.8761303Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:27:55.8794181Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:27:55.8824492Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:27:55.8858492Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:27:55.8887716Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:27:55.9051168Z Prepare all required actions 2025-09-07T06:27:55.9051724Z Getting action download info 2025-09-07T06:27:56.0256296Z ##[group]Run ./.github/actions/setup-linux 2025-09-07T06:27:56.0256775Z env: 2025-09-07T06:27:56.0257073Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:56.0257543Z ##[endgroup] 2025-09-07T06:27:56.0307058Z ##[group]Run set -euo pipefail 2025-09-07T06:27:56.0307446Z set -euo pipefail 2025-09-07T06:27:56.0307752Z function get_ec2_metadata() { 2025-09-07T06:27:56.0308132Z  # Pulled from instance metadata endpoint for EC2 2025-09-07T06:27:56.0308791Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-09-07T06:27:56.0309378Z  category=$1 2025-09-07T06:27:56.0309760Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-09-07T06:27:56.0310206Z  runner_name_str=i-0dd977e7b70f3c8d7 2025-09-07T06:27:56.0310613Z  if [[ -f /.inarc ]]; then 2025-09-07T06:27:56.0310970Z  echo "ARC Runner, no info on ec2 metadata" 2025-09-07T06:27:56.0311360Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-09-07T06:27:56.0311852Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-09-07T06:27:56.0312298Z  else 2025-09-07T06:27:56.0313185Z  curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2025-09-07T06:27:56.0314125Z  fi 2025-09-07T06:27:56.0314335Z } 2025-09-07T06:27:56.0314611Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-09-07T06:27:56.0315051Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-09-07T06:27:56.0315551Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-09-07T06:27:56.0315967Z echo "system info $(uname -a)" 2025-09-07T06:27:56.0322188Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:56.0322575Z env: 2025-09-07T06:27:56.0322797Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:56.0323062Z ##[endgroup] 2025-09-07T06:27:56.0470118Z ami-id: ami-05ffe3c48a9991133 2025-09-07T06:27:56.0571172Z instance-id: i-0dd977e7b70f3c8d7 2025-09-07T06:27:56.0719012Z instance-type: c5.2xlarge 2025-09-07T06:27:56.0730747Z system info Linux ip-10-0-22-62.ec2.internal 6.1.141-155.222.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 17 10:29:47 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-09-07T06:27:56.0761334Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:27:56.0762312Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:27:56.0768557Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:56.0768973Z env: 2025-09-07T06:27:56.0769185Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:56.0769453Z ##[endgroup] 2025-09-07T06:27:56.0828176Z ##[group]Run if systemctl is-active --quiet docker; then 2025-09-07T06:27:56.0828654Z if systemctl is-active --quiet docker; then 2025-09-07T06:27:56.0829058Z  echo "Docker daemon is running..."; 2025-09-07T06:27:56.0829393Z else 2025-09-07T06:27:56.0829868Z  echo "Starting docker daemon..." && sudo systemctl start docker; 2025-09-07T06:27:56.0830301Z fi 2025-09-07T06:27:56.0835698Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:56.0836097Z env: 2025-09-07T06:27:56.0836306Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:56.0836575Z ##[endgroup] 2025-09-07T06:27:56.0939422Z Docker daemon is running... 2025-09-07T06:27:56.1023154Z ##[group]Run nick-fields/retry@v3.0.0 2025-09-07T06:27:56.1023474Z with: 2025-09-07T06:27:56.1023685Z shell: bash 2025-09-07T06:27:56.1024062Z timeout_minutes: 5 2025-09-07T06:27:56.1024317Z max_attempts: 3 2025-09-07T06:27:56.1024566Z retry_wait_seconds: 30 2025-09-07T06:27:56.1026937Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" # For LF Runners we need to make sure we also login to Meta's ECR docker registry too. META_AWS_ACCOUNT_ID=308535385114 if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" fi 2025-09-07T06:27:56.1029434Z polling_interval_seconds: 1 2025-09-07T06:27:56.1029732Z warning_on_retry: true 2025-09-07T06:27:56.1029998Z continue_on_error: false 2025-09-07T06:27:56.1030264Z env: 2025-09-07T06:27:56.1030488Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:56.1030761Z AWS_RETRY_MODE: standard 2025-09-07T06:27:56.1031016Z AWS_MAX_ATTEMPTS: 5 2025-09-07T06:27:56.1031279Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:27:56.1031568Z ##[endgroup] 2025-09-07T06:27:57.2604208Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-07T06:27:57.2605378Z Configure a credential helper to remove this warning. See 2025-09-07T06:27:57.2606057Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-07T06:27:57.2606461Z 2025-09-07T06:27:57.2606579Z Login Succeeded 2025-09-07T06:27:58.1876951Z Command completed after 1 attempt(s). 2025-09-07T06:27:58.1945994Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:27:58.1946822Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:27:58.1947586Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:27:58.1958565Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:58.1959228Z env: 2025-09-07T06:27:58.1959595Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:58.1960042Z ##[endgroup] 2025-09-07T06:27:58.2054214Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T06:27:58.2054791Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T06:27:58.2055242Z # shellcheck disable=SC2046 2025-09-07T06:27:58.2055596Z docker stop $(docker ps -q) || true 2025-09-07T06:27:58.2056016Z # Prune all of the docker images 2025-09-07T06:27:58.2056370Z docker system prune -af 2025-09-07T06:27:58.2061795Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:58.2062184Z env: 2025-09-07T06:27:58.2062391Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:58.2062664Z ##[endgroup] 2025-09-07T06:27:58.2504935Z "docker stop" requires at least 1 argument. 2025-09-07T06:27:58.2505376Z See 'docker stop --help'. 2025-09-07T06:27:58.2505559Z 2025-09-07T06:27:58.2505762Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-09-07T06:27:58.2506040Z 2025-09-07T06:27:58.2506167Z Stop one or more running containers 2025-09-07T06:27:58.2724623Z Total reclaimed space: 0B 2025-09-07T06:27:58.2764938Z ##[group]Run set +e 2025-09-07T06:27:58.2765234Z set +e 2025-09-07T06:27:58.2765478Z set -x 2025-09-07T06:27:58.2765707Z  2025-09-07T06:27:58.2765957Z PT_DOMAIN=download.pytorch.org 2025-09-07T06:27:58.2766543Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-09-07T06:27:58.2767327Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-09-07T06:27:58.2767879Z # one is returned at random 2025-09-07T06:27:58.2768288Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-09-07T06:27:58.2768667Z  2025-09-07T06:27:58.2769076Z if [ -z "${RESOLVED_IP}" ]; then 2025-09-07T06:27:58.2769531Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-09-07T06:27:58.2770081Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-09-07T06:27:58.2770613Z  2025-09-07T06:27:58.2770843Z  if [ -z "${RESOLVED_IP}" ]; then 2025-09-07T06:27:58.2771249Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-09-07T06:27:58.2771626Z  exit 1 2025-09-07T06:27:58.2771868Z  fi 2025-09-07T06:27:58.2772074Z fi 2025-09-07T06:27:58.2772290Z  2025-09-07T06:27:58.2772555Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-09-07T06:27:58.2772932Z  # Clean up any old records first 2025-09-07T06:27:58.2773510Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-09-07T06:27:58.2773884Z fi 2025-09-07T06:27:58.2774102Z  2025-09-07T06:27:58.2774429Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-09-07T06:27:58.2774831Z cat /etc/hosts 2025-09-07T06:27:58.2780917Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:58.2781321Z env: 2025-09-07T06:27:58.2781551Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:58.2781807Z ##[endgroup] 2025-09-07T06:27:58.2805951Z + PT_DOMAIN=download.pytorch.org 2025-09-07T06:27:58.2811871Z ++ dig -4 +short download.pytorch.org 2025-09-07T06:27:58.2812302Z ++ tail -n1 2025-09-07T06:27:58.3411522Z + RESOLVED_IP=13.224.214.35 2025-09-07T06:27:58.3412053Z + '[' -z 13.224.214.35 ']' 2025-09-07T06:27:58.3412509Z + grep -r download.pytorch.org /etc/hosts 2025-09-07T06:27:58.3428229Z + echo '13.224.214.35 download.pytorch.org' 2025-09-07T06:27:58.3428928Z + sudo tee -a /etc/hosts 2025-09-07T06:27:58.7061597Z 13.224.214.35 download.pytorch.org 2025-09-07T06:27:58.7085811Z + cat /etc/hosts 2025-09-07T06:27:58.7094807Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-09-07T06:27:58.7100609Z ::1 localhost6 localhost6.localdomain6 2025-09-07T06:27:58.7101003Z 13.224.214.35 download.pytorch.org 2025-09-07T06:27:58.7280697Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-09-07T06:27:58.7281215Z with: 2025-09-07T06:27:58.7281975Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:58.7282844Z use-custom-docker-registry: true 2025-09-07T06:27:58.7283206Z docker-build-dir: .ci/docker 2025-09-07T06:27:58.7283510Z docker-build-script: ./build.sh 2025-09-07T06:27:58.7283813Z working-directory: . 2025-09-07T06:27:58.7284174Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:58.7284664Z force-push: false 2025-09-07T06:27:58.7284902Z env: 2025-09-07T06:27:58.7285121Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:58.7285387Z ##[endgroup] 2025-09-07T06:27:58.7313250Z ##[group]Run set -ex 2025-09-07T06:27:58.7313571Z set -ex 2025-09-07T06:27:58.7313802Z  2025-09-07T06:27:58.7314251Z # If the docker build directory or the build script doesn't exist, the action will 2025-09-07T06:27:58.7314965Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-09-07T06:27:58.7315543Z # job could then download the pre-built image as usual 2025-09-07T06:27:58.7316254Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-09-07T06:27:58.7316920Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7317260Z else 2025-09-07T06:27:58.7317531Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7317979Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7318400Z  2025-09-07T06:27:58.7318969Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-09-07T06:27:58.7319626Z  exit 0 2025-09-07T06:27:58.7319996Z fi 2025-09-07T06:27:58.7320209Z  2025-09-07T06:27:58.7320559Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-09-07T06:27:58.7321178Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-09-07T06:27:58.7321731Z  # use it as it is, but first let's extract the tag 2025-09-07T06:27:58.7322212Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-09-07T06:27:58.7322736Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7323254Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7323673Z else 2025-09-07T06:27:58.7323950Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-09-07T06:27:58.7324350Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-09-07T06:27:58.7324858Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-09-07T06:27:58.7325217Z  fi 2025-09-07T06:27:58.7325688Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-09-07T06:27:58.7326330Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7326996Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7327716Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7328167Z fi 2025-09-07T06:27:58.7337412Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:58.7337804Z env: 2025-09-07T06:27:58.7338023Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:58.7338303Z REPO_NAME: pytorch 2025-09-07T06:27:58.7339203Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:58.7340055Z DOCKER_BUILD_DIR: .ci/docker 2025-09-07T06:27:58.7340372Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-09-07T06:27:58.7340755Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:58.7341177Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-09-07T06:27:58.7341482Z CUSTOM_TAG_PREFIX: 2025-09-07T06:27:58.7341731Z ##[endgroup] 2025-09-07T06:27:58.7367504Z + [[ -d .ci/docker ]] 2025-09-07T06:27:58.7367787Z + [[ -f .ci/docker/./build.sh ]] 2025-09-07T06:27:58.7368084Z + [[ true == \t\r\u\e ]] 2025-09-07T06:27:58.7368348Z + echo skip=false 2025-09-07T06:27:58.7369384Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2025-09-07T06:27:58.7375810Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:58.7376728Z ++ awk -F '[:,]' '{print $2}' 2025-09-07T06:27:58.7399122Z + DOCKER_TAG=pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:58.7400283Z + echo docker-tag=pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:58.7401515Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:58.7434446Z ##[group]Run set +e 2025-09-07T06:27:58.7434765Z set +e 2025-09-07T06:27:58.7435007Z set -x 2025-09-07T06:27:58.7435237Z  2025-09-07T06:27:58.7435441Z login() { 2025-09-07T06:27:58.7435935Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-07T06:27:58.7436485Z } 2025-09-07T06:27:58.7436698Z  2025-09-07T06:27:58.7436905Z retry () { 2025-09-07T06:27:58.7437183Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-07T06:27:58.7437632Z } 2025-09-07T06:27:58.7437847Z  2025-09-07T06:27:58.7438071Z retry login "${DOCKER_REGISTRY}" 2025-09-07T06:27:58.7438388Z  2025-09-07T06:27:58.7438615Z START_TIME=$(date +%s) 2025-09-07T06:27:58.7438916Z # Wait up to 120 minutes 2025-09-07T06:27:58.7439289Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-09-07T06:27:58.7439791Z  # Check if image already exists, if it does then skip building it 2025-09-07T06:27:58.7440304Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-09-07T06:27:58.7440685Z  exit 0 2025-09-07T06:27:58.7440925Z  fi 2025-09-07T06:27:58.7441132Z  2025-09-07T06:27:58.7441525Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-09-07T06:27:58.7442215Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-09-07T06:27:58.7442904Z  # latter, it will wait for the Docker images to become available before continuing 2025-09-07T06:27:58.7443440Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-09-07T06:27:58.7443844Z  # It's a Docker build job, let's build the image 2025-09-07T06:27:58.7444205Z  break 2025-09-07T06:27:58.7444555Z  else 2025-09-07T06:27:58.7444916Z  # It's a regular build job, wait for the image to become available 2025-09-07T06:27:58.7445337Z  sleep 300 2025-09-07T06:27:58.7445596Z  fi 2025-09-07T06:27:58.7445827Z done 2025-09-07T06:27:58.7446051Z  2025-09-07T06:27:58.7446397Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-09-07T06:27:58.7446986Z # be empty. The default action would be to continue rebuild the image 2025-09-07T06:27:58.7447642Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-09-07T06:27:58.7448126Z  # if we're on the base branch then use the parent commit 2025-09-07T06:27:58.7448542Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-09-07T06:27:58.7448853Z else 2025-09-07T06:27:58.7449190Z  # otherwise we're on a PR, so use the most recent base commit 2025-09-07T06:27:58.7449688Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-09-07T06:27:58.7450060Z fi 2025-09-07T06:27:58.7450259Z  2025-09-07T06:27:58.7450493Z if [[ -z "${MERGE_BASE}" ]]; then 2025-09-07T06:27:58.7450863Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7451203Z  2025-09-07T06:27:58.7451670Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-09-07T06:27:58.7452239Z  exit 0 2025-09-07T06:27:58.7452469Z fi 2025-09-07T06:27:58.7452683Z  2025-09-07T06:27:58.7452997Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-09-07T06:27:58.7453688Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-09-07T06:27:58.7454296Z  exit 1 2025-09-07T06:27:58.7454520Z fi 2025-09-07T06:27:58.7454734Z  2025-09-07T06:27:58.7455092Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-09-07T06:27:58.7455767Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-09-07T06:27:58.7456377Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-09-07T06:27:58.7457082Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-09-07T06:27:58.7457883Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-09-07T06:27:58.7458407Z fi 2025-09-07T06:27:58.7458622Z  2025-09-07T06:27:58.7458897Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:58.7464585Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:58.7464977Z env: 2025-09-07T06:27:58.7465185Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:58.7465468Z DOCKER_BUILD_DIR: .ci/docker 2025-09-07T06:27:58.7465818Z BASE_REVISION: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:27:58.7466693Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:58.7467722Z DOCKER_TAG: pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:58.7468358Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:58.7468758Z DOCKER_PUSH: 2025-09-07T06:27:58.7468992Z ##[endgroup] 2025-09-07T06:27:58.7493283Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:58.7493984Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:58.7496010Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:27:58.7497380Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:59.3249202Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-07T06:27:59.3249836Z Configure a credential helper to remove this warning. See 2025-09-07T06:27:59.3250692Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-07T06:27:59.3251192Z 2025-09-07T06:27:59.3251298Z Login Succeeded 2025-09-07T06:27:59.3278120Z ++ date +%s 2025-09-07T06:27:59.3288137Z + START_TIME=1757226479 2025-09-07T06:27:59.3291004Z ++ date +%s 2025-09-07T06:27:59.3300699Z + [[ 1757219279 -lt 1757226479 ]] 2025-09-07T06:27:59.3301835Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:59.5535480Z { 2025-09-07T06:27:59.5535920Z "schemaVersion": 2, 2025-09-07T06:27:59.5536543Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-09-07T06:27:59.5537056Z "config": { 2025-09-07T06:27:59.5537666Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-09-07T06:27:59.5538318Z "size": 30147, 2025-09-07T06:27:59.5538800Z "digest": "sha256:d68dea278b660b539496dbad92d5230006940a736c3a0dcc39d6f72863a5aaa0" 2025-09-07T06:27:59.5539604Z }, 2025-09-07T06:27:59.5539884Z "layers": [ 2025-09-07T06:27:59.5540188Z { 2025-09-07T06:27:59.5540755Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5541357Z "size": 30448359, 2025-09-07T06:27:59.5541798Z "digest": "sha256:e6fdc8487bfe6d764301ef3634bc6c043841dc3ab05ca14f81e69c0f92562d46" 2025-09-07T06:27:59.5542277Z }, 2025-09-07T06:27:59.5542474Z { 2025-09-07T06:27:59.5542827Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5543263Z "size": 1554, 2025-09-07T06:27:59.5543677Z "digest": "sha256:efc45b9044a6cbae9d1981fa8f749b3b24e14bf1e2227b92e3e19d9f6f73f452" 2025-09-07T06:27:59.5544163Z }, 2025-09-07T06:27:59.5544359Z { 2025-09-07T06:27:59.5544688Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5545102Z "size": 344022769, 2025-09-07T06:27:59.5545533Z "digest": 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"sha256:25069f524b4c945f72a7babca5cc2d9f555e3ec4352030b5bbd67ba8ac4b0f74" 2025-09-07T06:27:59.5650685Z }, 2025-09-07T06:27:59.5650856Z { 2025-09-07T06:27:59.5651179Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5651606Z "size": 1012, 2025-09-07T06:27:59.5652011Z "digest": "sha256:c576014b13e169b13387245f818ac901197b9d33206aacca506b88b26ad9a1fb" 2025-09-07T06:27:59.5652471Z }, 2025-09-07T06:27:59.5652661Z { 2025-09-07T06:27:59.5652992Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5653410Z "size": 724, 2025-09-07T06:27:59.5653819Z "digest": "sha256:11edb6ea0bca3be307ef836b0bd07999ff562bcb7a807f5e6c9f7d4d5f976b5d" 2025-09-07T06:27:59.5654304Z }, 2025-09-07T06:27:59.5654492Z { 2025-09-07T06:27:59.5654816Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5655224Z "size": 135, 2025-09-07T06:27:59.5655631Z "digest": "sha256:cd8bd7e8fa9445552b41b40d0d4b59943007a6e907837d8e14bf638363154f90" 2025-09-07T06:27:59.5656106Z }, 2025-09-07T06:27:59.5656296Z { 2025-09-07T06:27:59.5656609Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5657099Z "size": 32, 2025-09-07T06:27:59.5657519Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:59.5658002Z }, 2025-09-07T06:27:59.5658183Z { 2025-09-07T06:27:59.5658505Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5658927Z "size": 158, 2025-09-07T06:27:59.5659340Z "digest": "sha256:f05371ab6d70bb3df829f342267e16fb3ea0d7102f6ae1c52897cdf720d82a81" 2025-09-07T06:27:59.5659807Z }, 2025-09-07T06:27:59.5659995Z { 2025-09-07T06:27:59.5660320Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5660739Z "size": 1369, 2025-09-07T06:27:59.5661147Z "digest": "sha256:2ea1f5444bf63e47215d2ce9bfc3d2185dd9bf861cc250e34632216f5814b8da" 2025-09-07T06:27:59.5661630Z }, 2025-09-07T06:27:59.5661822Z { 2025-09-07T06:27:59.5662153Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5662575Z "size": 32, 2025-09-07T06:27:59.5662997Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:59.5663484Z }, 2025-09-07T06:27:59.5663678Z { 2025-09-07T06:27:59.5663997Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5664427Z "size": 137, 2025-09-07T06:27:59.5664849Z "digest": "sha256:dc3473b7f9639490137bc3cf54b339baf944c9e1ebf417eb8cb2fa6191480a11" 2025-09-07T06:27:59.5665341Z }, 2025-09-07T06:27:59.5665529Z { 2025-09-07T06:27:59.5665864Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5666298Z "size": 381, 2025-09-07T06:27:59.5666724Z "digest": "sha256:3258a00bf4d8c01ff6239aa16d98c260e208b7fd6fe393c70ef0367a0a44b318" 2025-09-07T06:27:59.5667202Z }, 2025-09-07T06:27:59.5667404Z { 2025-09-07T06:27:59.5667745Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5668185Z "size": 32, 2025-09-07T06:27:59.5668671Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:59.5669170Z }, 2025-09-07T06:27:59.5669368Z { 2025-09-07T06:27:59.5669700Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5670117Z "size": 104, 2025-09-07T06:27:59.5670538Z "digest": "sha256:a5e2ce08f721a164ec5ae3e23221cbd32361ce97a5104e02c9151f9a768d3ac8" 2025-09-07T06:27:59.5671020Z }, 2025-09-07T06:27:59.5671218Z { 2025-09-07T06:27:59.5671532Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5671957Z "size": 409, 2025-09-07T06:27:59.5672367Z "digest": "sha256:0620806a0206044e85ebed03275b53eb24d015f4b21fd4ca32480d1a8150c7cf" 2025-09-07T06:27:59.5672843Z }, 2025-09-07T06:27:59.5673027Z { 2025-09-07T06:27:59.5673593Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5674029Z "size": 32, 2025-09-07T06:27:59.5674455Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:59.5674933Z }, 2025-09-07T06:27:59.5675130Z { 2025-09-07T06:27:59.5675460Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5675885Z "size": 109, 2025-09-07T06:27:59.5676283Z "digest": "sha256:86243afc698eb6983eb8ab0e82e3172878b440c78788227107d0221c83d5876e" 2025-09-07T06:27:59.5676760Z }, 2025-09-07T06:27:59.5676961Z { 2025-09-07T06:27:59.5677293Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5677704Z "size": 1897, 2025-09-07T06:27:59.5678123Z "digest": "sha256:81d56f8c0e9e96289a7250e45d9cc29663b554c56251c1fee391de9bebf0c201" 2025-09-07T06:27:59.5678602Z }, 2025-09-07T06:27:59.5678797Z { 2025-09-07T06:27:59.5679114Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5679541Z "size": 243408560, 2025-09-07T06:27:59.5679985Z "digest": "sha256:583d3e85f6ec4fbf3276a0e7c69498705dc09173a39bbcebe5a1b8fd13d7e225" 2025-09-07T06:27:59.5680469Z }, 2025-09-07T06:27:59.5680800Z { 2025-09-07T06:27:59.5681136Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5681566Z "size": 106, 2025-09-07T06:27:59.5681990Z "digest": "sha256:1d2c6772b50878424206ff920f681eb7943f9e7eedb6e31fdfd6fa6a38de8e33" 2025-09-07T06:27:59.5682480Z }, 2025-09-07T06:27:59.5682665Z { 2025-09-07T06:27:59.5683001Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5683430Z "size": 164, 2025-09-07T06:27:59.5683848Z "digest": "sha256:2c149ad240dfccc069a636e2b6bf13397ae1918c01381975dd069948e5ea471d" 2025-09-07T06:27:59.5684318Z }, 2025-09-07T06:27:59.5684579Z { 2025-09-07T06:27:59.5684919Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5685347Z "size": 7943, 2025-09-07T06:27:59.5685755Z "digest": "sha256:77d8ceec47985080ac194cf4eeeafaf70595896cc4bf664906c20422325d5ddd" 2025-09-07T06:27:59.5686248Z }, 2025-09-07T06:27:59.5686440Z { 2025-09-07T06:27:59.5686776Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5687189Z "size": 8074, 2025-09-07T06:27:59.5687596Z "digest": "sha256:2be2d278705b612946c28177f8684dc07e44a27248c228732a50c62716acfe4b" 2025-09-07T06:27:59.5688067Z }, 2025-09-07T06:27:59.5688259Z { 2025-09-07T06:27:59.5688577Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5688999Z "size": 302, 2025-09-07T06:27:59.5689407Z "digest": "sha256:ca8d286566c546411a1541f9e11d22786fae23a1d8004f8e69e283ce89ad2916" 2025-09-07T06:27:59.5689884Z }, 2025-09-07T06:27:59.5690062Z { 2025-09-07T06:27:59.5690385Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5690807Z "size": 32, 2025-09-07T06:27:59.5691226Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:59.5691699Z }, 2025-09-07T06:27:59.5691899Z { 2025-09-07T06:27:59.5692333Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5692773Z "size": 108, 2025-09-07T06:27:59.5693176Z "digest": "sha256:7461eb1803b5c82f085c3d8caf6b92b6e01c24e55edc778d53c6bb4992fada35" 2025-09-07T06:27:59.5693659Z }, 2025-09-07T06:27:59.5693856Z { 2025-09-07T06:27:59.5694190Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5694700Z "size": 54145653, 2025-09-07T06:27:59.5695140Z "digest": "sha256:08bab7160595f46d42c82fd0253b4294dacaefe59cdba6772e375da23e62c0ed" 2025-09-07T06:27:59.5695628Z }, 2025-09-07T06:27:59.5695826Z { 2025-09-07T06:27:59.5696142Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:59.5696571Z "size": 32, 2025-09-07T06:27:59.5696987Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:59.5697475Z } 2025-09-07T06:27:59.5697655Z ] 2025-09-07T06:27:59.5697849Z } 2025-09-07T06:27:59.5733833Z ##[group]Run set -eux 2025-09-07T06:27:59.5734129Z set -eux 2025-09-07T06:27:59.5734572Z # It's ok if this steps fails, it would then be an anonymous user like what we used to have 2025-09-07T06:27:59.5735767Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin || true 2025-09-07T06:27:59.5743364Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:59.5743744Z env: 2025-09-07T06:27:59.5743969Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:59.5744240Z ##[endgroup] 2025-09-07T06:27:59.5772828Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-09-07T06:27:59.5774037Z + jq --raw-output .SecretString 2025-09-07T06:27:59.5774988Z + jq -r .docker_hub_readonly_token 2025-09-07T06:27:59.5776372Z + docker login --username pytorchbot --password-stdin 2025-09-07T06:28:00.1670846Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-07T06:28:00.1672097Z Configure a credential helper to remove this warning. See 2025-09-07T06:28:00.1672988Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-07T06:28:00.1673761Z 2025-09-07T06:28:00.1673922Z Login Succeeded 2025-09-07T06:28:00.1761656Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*:} 2025-09-07T06:28:00.1762048Z tag=${ECR_DOCKER_IMAGE##*:} 2025-09-07T06:28:00.1762448Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-09-07T06:28:00.1769063Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:28:00.1769465Z env: 2025-09-07T06:28:00.1769697Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:28:00.1770500Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:00.1771312Z ##[endgroup] 2025-09-07T06:28:00.1799078Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:00.1853692Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-09-07T06:28:00.1854152Z with: 2025-09-07T06:28:00.1854884Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:00.1855789Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:28:00.1856196Z env: 2025-09-07T06:28:00.1856415Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:28:00.1856681Z ##[endgroup] 2025-09-07T06:28:00.1881572Z ##[group]Run set -x 2025-09-07T06:28:00.1881855Z set -x 2025-09-07T06:28:00.1882090Z set +e 2025-09-07T06:28:00.1882319Z  2025-09-07T06:28:00.1882522Z login() { 2025-09-07T06:28:00.1883017Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-07T06:28:00.1883553Z } 2025-09-07T06:28:00.1883781Z  2025-09-07T06:28:00.1884037Z retry () { 2025-09-07T06:28:00.1884316Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-07T06:28:00.1884724Z } 2025-09-07T06:28:00.1884938Z  2025-09-07T06:28:00.1885183Z retry login "${DOCKER_REGISTRY}" 2025-09-07T06:28:00.1885503Z  2025-09-07T06:28:00.1885995Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-09-07T06:28:00.1886686Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-09-07T06:28:00.1887069Z  2025-09-07T06:28:00.1887286Z set -e 2025-09-07T06:28:00.1887629Z # ignore output since only exit code is used for conditional 2025-09-07T06:28:00.1888137Z # only pull docker image if it's not available locally 2025-09-07T06:28:00.1888701Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-09-07T06:28:00.1889229Z  retry docker pull "${DOCKER_IMAGE}" 2025-09-07T06:28:00.1889557Z fi 2025-09-07T06:28:00.1895242Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:28:00.1895638Z env: 2025-09-07T06:28:00.1895862Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:28:00.1896652Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:00.1897551Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:28:00.1897959Z ##[endgroup] 2025-09-07T06:28:00.1921344Z + set +e 2025-09-07T06:28:00.1921937Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:28:00.1922414Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:28:00.1925591Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:28:00.1926530Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:28:00.7505170Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-07T06:28:00.7506210Z Configure a credential helper to remove this warning. See 2025-09-07T06:28:00.7507211Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-07T06:28:00.7507679Z 2025-09-07T06:28:00.7507782Z Login Succeeded 2025-09-07T06:28:00.7526876Z ++ docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:00.7527899Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-09-07T06:28:00.9630899Z + IMAGE_SIZE=4409.002216339111 2025-09-07T06:28:00.9631321Z + echo 'Compressed size of image in MB: 4409.002216339111' 2025-09-07T06:28:00.9631709Z + set -e 2025-09-07T06:28:00.9631982Z Compressed size of image in MB: 4409.002216339111 2025-09-07T06:28:00.9633215Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:00.9771933Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:00.9773459Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:01.2346214Z pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77: Pulling from pytorch/ci-image 2025-09-07T06:28:01.2347516Z e6fdc8487bfe: Pulling fs layer 2025-09-07T06:28:01.2347954Z efc45b9044a6: Pulling fs layer 2025-09-07T06:28:01.2348555Z fad5f8e61058: Pulling fs layer 2025-09-07T06:28:01.2349049Z c0da146487b6: Pulling fs layer 2025-09-07T06:28:01.2349539Z 67db20671424: Pulling fs layer 2025-09-07T06:28:01.2350009Z 673cf5ffa968: Pulling fs layer 2025-09-07T06:28:01.2350511Z 41f32a933100: Pulling fs layer 2025-09-07T06:28:01.2351035Z ed25a020f194: Pulling fs layer 2025-09-07T06:28:01.2351384Z d242bd81fee3: Pulling fs layer 2025-09-07T06:28:01.2351790Z 8c15b80c2064: Pulling fs layer 2025-09-07T06:28:01.2352165Z 4d141034e9db: Pulling fs layer 2025-09-07T06:28:01.2352442Z ed25a020f194: Waiting 2025-09-07T06:28:01.2352701Z 11edb6ea0bca: Pulling fs layer 2025-09-07T06:28:01.2352974Z a51285558384: Pulling fs layer 2025-09-07T06:28:01.2353248Z d242bd81fee3: Waiting 2025-09-07T06:28:01.2353491Z 41f32a933100: Waiting 2025-09-07T06:28:01.2353745Z f3371e22a442: Pulling fs layer 2025-09-07T06:28:01.2354007Z 8c15b80c2064: Waiting 2025-09-07T06:28:01.2354245Z 67db20671424: Waiting 2025-09-07T06:28:01.2354494Z 4f4fb700ef54: Pulling fs layer 2025-09-07T06:28:01.2354783Z 829c85269cfc: Pulling fs layer 2025-09-07T06:28:01.2355058Z a0228ccab2ac: Pulling fs layer 2025-09-07T06:28:01.2355339Z 673cf5ffa968: Waiting 2025-09-07T06:28:01.2355584Z 11edb6ea0bca: Waiting 2025-09-07T06:28:01.2355841Z 1c71c7358f5b: Pulling fs layer 2025-09-07T06:28:01.2356105Z a51285558384: Waiting 2025-09-07T06:28:01.2356353Z f3371e22a442: Waiting 2025-09-07T06:28:01.2356592Z 4f4fb700ef54: Waiting 2025-09-07T06:28:01.2356850Z 059f21cc5727: Pulling fs layer 2025-09-07T06:28:01.2357113Z c0da146487b6: Waiting 2025-09-07T06:28:01.2357350Z 829c85269cfc: Waiting 2025-09-07T06:28:01.2357599Z 7f8e86c840b7: Pulling fs layer 2025-09-07T06:28:01.2357887Z 0f520396671f: Pulling fs layer 2025-09-07T06:28:01.2358152Z a0228ccab2ac: Waiting 2025-09-07T06:28:01.2358394Z 1c71c7358f5b: Waiting 2025-09-07T06:28:01.2358632Z 059f21cc5727: Waiting 2025-09-07T06:28:01.2358885Z edeb483a9b38: Pulling fs layer 2025-09-07T06:28:01.2359149Z 4d141034e9db: Waiting 2025-09-07T06:28:01.2359391Z 7f8e86c840b7: Waiting 2025-09-07T06:28:01.2359729Z 8608e3e91e31: Pulling fs layer 2025-09-07T06:28:01.2360106Z 0f520396671f: Waiting 2025-09-07T06:28:01.2360434Z 809a30f3be95: Pulling fs layer 2025-09-07T06:28:01.2360715Z edeb483a9b38: Waiting 2025-09-07T06:28:01.2361056Z 90710e19b430: Pulling fs layer 2025-09-07T06:28:01.2361831Z 8608e3e91e31: Waiting 2025-09-07T06:28:01.2362270Z 809a30f3be95: Waiting 2025-09-07T06:28:01.2362700Z a65dff48674f: Pulling fs layer 2025-09-07T06:28:01.2363137Z 90710e19b430: Waiting 2025-09-07T06:28:01.2373647Z 720fb67e397f: Pulling fs layer 2025-09-07T06:28:01.2373945Z c469b381684d: Pulling fs layer 2025-09-07T06:28:01.2374214Z c98637e362e4: Pulling fs layer 2025-09-07T06:28:01.2374480Z 7988968c85d3: Pulling fs layer 2025-09-07T06:28:01.2374755Z a65dff48674f: Waiting 2025-09-07T06:28:01.2374998Z c469b381684d: Waiting 2025-09-07T06:28:01.2375226Z 720fb67e397f: Waiting 2025-09-07T06:28:01.2375482Z 7741a1f97c0d: Pulling fs layer 2025-09-07T06:28:01.2375772Z aa3590d8b0d4: Pulling fs layer 2025-09-07T06:28:01.2376050Z c98637e362e4: Waiting 2025-09-07T06:28:01.2376317Z 91927b708b6a: Pulling fs layer 2025-09-07T06:28:01.2376591Z 7988968c85d3: Waiting 2025-09-07T06:28:01.2376849Z ed7eb4cd91c6: Pulling fs layer 2025-09-07T06:28:01.2377189Z aa3590d8b0d4: Waiting 2025-09-07T06:28:01.2377430Z 7f422bd7611b: Pulling fs layer 2025-09-07T06:28:01.2377933Z 91927b708b6a: Waiting 2025-09-07T06:28:01.2378191Z 8e1167399aca: Pulling fs layer 2025-09-07T06:28:01.2378469Z 7741a1f97c0d: Waiting 2025-09-07T06:28:01.2378697Z 7f422bd7611b: Waiting 2025-09-07T06:28:01.2378953Z d157c63b23dc: Pulling fs layer 2025-09-07T06:28:01.2379236Z ed7eb4cd91c6: Waiting 2025-09-07T06:28:01.2379501Z ec0c7fb1e287: Pulling fs layer 2025-09-07T06:28:01.2379764Z 8e1167399aca: Waiting 2025-09-07T06:28:01.2380018Z 97d08cfe0fea: Pulling fs layer 2025-09-07T06:28:01.2380292Z d157c63b23dc: Waiting 2025-09-07T06:28:01.2380544Z 9e37f0a1d024: Pulling fs layer 2025-09-07T06:28:01.2380805Z ec0c7fb1e287: Waiting 2025-09-07T06:28:01.2381045Z 97d08cfe0fea: Waiting 2025-09-07T06:28:01.2381295Z 6261a3bd7fa1: Pulling fs layer 2025-09-07T06:28:01.2381580Z 7025d6c8fff9: Pulling fs layer 2025-09-07T06:28:01.2381841Z 9e37f0a1d024: Waiting 2025-09-07T06:28:01.2382079Z 6261a3bd7fa1: Waiting 2025-09-07T06:28:01.2382318Z 7025d6c8fff9: Waiting 2025-09-07T06:28:01.2382581Z a3f895aec88a: Pulling fs layer 2025-09-07T06:28:01.2383032Z 25069f524b4c: Pulling fs layer 2025-09-07T06:28:01.2383317Z c576014b13e1: Pulling fs layer 2025-09-07T06:28:01.2383590Z 25069f524b4c: Waiting 2025-09-07T06:28:01.2383836Z a3f895aec88a: Waiting 2025-09-07T06:28:01.2384085Z cd8bd7e8fa94: Pulling fs layer 2025-09-07T06:28:01.2384373Z f05371ab6d70: Pulling fs layer 2025-09-07T06:28:01.2384643Z c576014b13e1: Waiting 2025-09-07T06:28:01.2384899Z 2ea1f5444bf6: Pulling fs layer 2025-09-07T06:28:01.2385165Z cd8bd7e8fa94: Waiting 2025-09-07T06:28:01.2385498Z f05371ab6d70: Waiting 2025-09-07T06:28:01.2385903Z dc3473b7f963: Pulling fs layer 2025-09-07T06:28:01.2386367Z 3258a00bf4d8: Pulling fs layer 2025-09-07T06:28:01.2386805Z dc3473b7f963: Waiting 2025-09-07T06:28:01.2387218Z a5e2ce08f721: Pulling fs layer 2025-09-07T06:28:01.2387675Z 0620806a0206: Pulling fs layer 2025-09-07T06:28:01.2388127Z 2ea1f5444bf6: Waiting 2025-09-07T06:28:01.2388607Z 0620806a0206: Waiting 2025-09-07T06:28:01.2388971Z 86243afc698e: Pulling fs layer 2025-09-07T06:28:01.2389324Z a5e2ce08f721: Waiting 2025-09-07T06:28:01.2389701Z 81d56f8c0e9e: Pulling fs layer 2025-09-07T06:28:01.2390185Z 86243afc698e: Waiting 2025-09-07T06:28:01.2390554Z 583d3e85f6ec: Pulling fs layer 2025-09-07T06:28:01.2390839Z 3258a00bf4d8: Waiting 2025-09-07T06:28:01.2391116Z 1d2c6772b508: Pulling fs layer 2025-09-07T06:28:01.2391407Z 2c149ad240df: Pulling fs layer 2025-09-07T06:28:01.2391685Z 583d3e85f6ec: Waiting 2025-09-07T06:28:01.2391940Z 77d8ceec4798: Pulling fs layer 2025-09-07T06:28:01.2392214Z 2be2d278705b: Pulling fs layer 2025-09-07T06:28:01.2392497Z ca8d286566c5: Pulling fs layer 2025-09-07T06:28:01.2392772Z 2c149ad240df: Waiting 2025-09-07T06:28:01.2393011Z 77d8ceec4798: Waiting 2025-09-07T06:28:01.2393236Z 2be2d278705b: Waiting 2025-09-07T06:28:01.2393475Z ca8d286566c5: Waiting 2025-09-07T06:28:01.2393723Z 7461eb1803b5: Pulling fs layer 2025-09-07T06:28:01.2393996Z 1d2c6772b508: Waiting 2025-09-07T06:28:01.2394232Z 08bab7160595: Pulling fs layer 2025-09-07T06:28:01.2394688Z 7461eb1803b5: Waiting 2025-09-07T06:28:01.2394939Z 81d56f8c0e9e: Waiting 2025-09-07T06:28:01.2395181Z 08bab7160595: Waiting 2025-09-07T06:28:01.3223314Z efc45b9044a6: Verifying Checksum 2025-09-07T06:28:01.3223743Z efc45b9044a6: Download complete 2025-09-07T06:28:01.4005733Z c0da146487b6: Verifying Checksum 2025-09-07T06:28:01.4006164Z c0da146487b6: Download complete 2025-09-07T06:28:01.5927900Z e6fdc8487bfe: Download complete 2025-09-07T06:28:01.6808875Z 673cf5ffa968: Verifying Checksum 2025-09-07T06:28:01.6810029Z 673cf5ffa968: Download complete 2025-09-07T06:28:01.7622864Z 41f32a933100: Verifying Checksum 2025-09-07T06:28:01.7623460Z 41f32a933100: Download complete 2025-09-07T06:28:01.8542151Z ed25a020f194: Verifying Checksum 2025-09-07T06:28:01.9377323Z d242bd81fee3: Verifying Checksum 2025-09-07T06:28:01.9378246Z d242bd81fee3: Download complete 2025-09-07T06:28:02.0303361Z 8c15b80c2064: Verifying Checksum 2025-09-07T06:28:02.0304010Z 8c15b80c2064: Download complete 2025-09-07T06:28:02.1006455Z 67db20671424: Verifying Checksum 2025-09-07T06:28:02.1007232Z 67db20671424: Download complete 2025-09-07T06:28:02.1397482Z 4d141034e9db: Download complete 2025-09-07T06:28:02.1955561Z 11edb6ea0bca: Verifying Checksum 2025-09-07T06:28:02.1956147Z 11edb6ea0bca: Download complete 2025-09-07T06:28:02.2161325Z a51285558384: Verifying Checksum 2025-09-07T06:28:02.2233829Z a51285558384: Download complete 2025-09-07T06:28:02.2234403Z 4f4fb700ef54: Verifying Checksum 2025-09-07T06:28:02.2235390Z 4f4fb700ef54: Download complete 2025-09-07T06:28:02.2922322Z 829c85269cfc: Verifying Checksum 2025-09-07T06:28:02.2922941Z 829c85269cfc: Download complete 2025-09-07T06:28:02.3862347Z a0228ccab2ac: Download complete 2025-09-07T06:28:02.4784653Z 1c71c7358f5b: Download complete 2025-09-07T06:28:02.5646352Z e6fdc8487bfe: Pull complete 2025-09-07T06:28:02.5773688Z 059f21cc5727: Verifying Checksum 2025-09-07T06:28:02.5774593Z 059f21cc5727: Download complete 2025-09-07T06:28:02.5869562Z efc45b9044a6: Pull complete 2025-09-07T06:28:02.6716563Z 7f8e86c840b7: Verifying Checksum 2025-09-07T06:28:02.6717127Z 7f8e86c840b7: Download complete 2025-09-07T06:28:02.7626301Z 0f520396671f: Download complete 2025-09-07T06:28:02.8217983Z edeb483a9b38: Verifying Checksum 2025-09-07T06:28:02.8218367Z edeb483a9b38: Download complete 2025-09-07T06:28:02.9070895Z 8608e3e91e31: Download complete 2025-09-07T06:28:03.0027814Z 809a30f3be95: Verifying Checksum 2025-09-07T06:28:03.0028219Z 809a30f3be95: Download complete 2025-09-07T06:28:03.0998234Z 90710e19b430: Verifying Checksum 2025-09-07T06:28:03.0999005Z 90710e19b430: Download complete 2025-09-07T06:28:03.1973209Z a65dff48674f: Verifying Checksum 2025-09-07T06:28:03.1973998Z a65dff48674f: Download complete 2025-09-07T06:28:03.2905501Z 720fb67e397f: Download complete 2025-09-07T06:28:04.7369906Z fad5f8e61058: Verifying Checksum 2025-09-07T06:28:04.7370548Z fad5f8e61058: Download complete 2025-09-07T06:28:04.7983412Z c98637e362e4: Download complete 2025-09-07T06:28:04.8912797Z 7988968c85d3: Verifying Checksum 2025-09-07T06:28:04.8913388Z 7988968c85d3: Download complete 2025-09-07T06:28:04.9450515Z 7741a1f97c0d: Verifying Checksum 2025-09-07T06:28:04.9451184Z 7741a1f97c0d: Download complete 2025-09-07T06:28:05.0365650Z aa3590d8b0d4: Verifying Checksum 2025-09-07T06:28:05.0366337Z aa3590d8b0d4: Download complete 2025-09-07T06:28:05.2811017Z 91927b708b6a: Verifying Checksum 2025-09-07T06:28:05.2811928Z 91927b708b6a: Download complete 2025-09-07T06:28:05.3771885Z ed7eb4cd91c6: Verifying Checksum 2025-09-07T06:28:05.3772451Z ed7eb4cd91c6: Download complete 2025-09-07T06:28:05.4682703Z 7f422bd7611b: Download complete 2025-09-07T06:28:05.5410577Z 8e1167399aca: Download complete 2025-09-07T06:28:05.6215076Z d157c63b23dc: Download complete 2025-09-07T06:28:05.6897672Z ec0c7fb1e287: Download complete 2025-09-07T06:28:05.7618193Z 97d08cfe0fea: Verifying Checksum 2025-09-07T06:28:05.7618810Z 97d08cfe0fea: Download complete 2025-09-07T06:28:05.8327417Z 9e37f0a1d024: Download complete 2025-09-07T06:28:05.9122226Z 6261a3bd7fa1: Verifying Checksum 2025-09-07T06:28:05.9123422Z 6261a3bd7fa1: Download complete 2025-09-07T06:28:06.0012074Z 7025d6c8fff9: Verifying Checksum 2025-09-07T06:28:06.0012543Z 7025d6c8fff9: Download complete 2025-09-07T06:28:06.0887373Z a3f895aec88a: Verifying Checksum 2025-09-07T06:28:06.0887930Z a3f895aec88a: Download complete 2025-09-07T06:28:06.1481719Z 25069f524b4c: Download complete 2025-09-07T06:28:06.2250274Z c576014b13e1: Verifying Checksum 2025-09-07T06:28:06.2250735Z c576014b13e1: Download complete 2025-09-07T06:28:06.2925818Z cd8bd7e8fa94: Verifying Checksum 2025-09-07T06:28:06.2926410Z cd8bd7e8fa94: Download complete 2025-09-07T06:28:06.3538639Z f05371ab6d70: Download complete 2025-09-07T06:28:06.4231868Z 2ea1f5444bf6: Download complete 2025-09-07T06:28:06.4872696Z dc3473b7f963: Verifying Checksum 2025-09-07T06:28:06.4873187Z dc3473b7f963: Download complete 2025-09-07T06:28:06.5735683Z 3258a00bf4d8: Verifying Checksum 2025-09-07T06:28:06.5736200Z 3258a00bf4d8: Download complete 2025-09-07T06:28:06.6546677Z a5e2ce08f721: Verifying Checksum 2025-09-07T06:28:06.6547584Z a5e2ce08f721: Download complete 2025-09-07T06:28:06.7440546Z 0620806a0206: Verifying Checksum 2025-09-07T06:28:06.7441114Z 0620806a0206: Download complete 2025-09-07T06:28:06.8331651Z 86243afc698e: Verifying Checksum 2025-09-07T06:28:06.9395828Z 86243afc698e: Download complete 2025-09-07T06:28:06.9396443Z 81d56f8c0e9e: Verifying Checksum 2025-09-07T06:28:06.9396990Z 81d56f8c0e9e: Download complete 2025-09-07T06:28:07.8856620Z c469b381684d: Verifying Checksum 2025-09-07T06:28:07.8857001Z c469b381684d: Download complete 2025-09-07T06:28:07.9636614Z 1d2c6772b508: Verifying Checksum 2025-09-07T06:28:07.9637087Z 1d2c6772b508: Download complete 2025-09-07T06:28:08.0445733Z 2c149ad240df: Verifying Checksum 2025-09-07T06:28:08.0446367Z 2c149ad240df: Download complete 2025-09-07T06:28:08.1196935Z 77d8ceec4798: Download complete 2025-09-07T06:28:08.1852932Z 2be2d278705b: Download complete 2025-09-07T06:28:08.2639297Z ca8d286566c5: Verifying Checksum 2025-09-07T06:28:08.2639742Z ca8d286566c5: Download complete 2025-09-07T06:28:08.3517165Z 7461eb1803b5: Verifying Checksum 2025-09-07T06:28:08.3517567Z 7461eb1803b5: Download complete 2025-09-07T06:28:08.9988500Z 08bab7160595: Verifying Checksum 2025-09-07T06:28:08.9989044Z 08bab7160595: Download complete 2025-09-07T06:28:09.4150902Z 583d3e85f6ec: Verifying Checksum 2025-09-07T06:28:09.4151292Z 583d3e85f6ec: Download complete 2025-09-07T06:28:13.7292132Z fad5f8e61058: Pull complete 2025-09-07T06:28:13.8615983Z c0da146487b6: Pull complete 2025-09-07T06:28:15.1965366Z 67db20671424: Pull complete 2025-09-07T06:28:15.3474876Z 673cf5ffa968: Pull complete 2025-09-07T06:28:15.4578855Z 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2c149ad240df: Pull complete 2025-09-07T06:29:52.7715259Z 77d8ceec4798: Pull complete 2025-09-07T06:29:52.8669703Z 2be2d278705b: Pull complete 2025-09-07T06:29:52.9104729Z ca8d286566c5: Pull complete 2025-09-07T06:29:53.0634580Z 7461eb1803b5: Pull complete 2025-09-07T06:29:54.9865236Z 08bab7160595: Pull complete 2025-09-07T06:29:55.0794426Z Digest: sha256:91b756ef2bd4983cfdb00ea40fdbe492b4a29b8a15826aa577c257c337fe768e 2025-09-07T06:29:55.1040895Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:29:55.1273584Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:29:55.1315786Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:29:55.1316832Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:29:55.1324872Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:55.1325272Z env: 2025-09-07T06:29:55.1325501Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:55.1325779Z ##[endgroup] 2025-09-07T06:29:55.1495339Z Prepare all required actions 2025-09-07T06:29:55.1572684Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-09-07T06:29:55.1573056Z with: 2025-09-07T06:29:55.1573911Z github-token: *** 2025-09-07T06:29:55.1574160Z env: 2025-09-07T06:29:55.1574383Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:55.1574639Z ##[endgroup] 2025-09-07T06:29:55.1659828Z ##[group]Run set -eux 2025-09-07T06:29:55.1660101Z set -eux 2025-09-07T06:29:55.1660579Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-09-07T06:29:55.1666968Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:55.1667356Z env: 2025-09-07T06:29:55.1667579Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:55.1668030Z GITHUB_TOKEN: *** 2025-09-07T06:29:55.1668276Z ##[endgroup] 2025-09-07T06:29:55.1693353Z + python3 .github/scripts/get_workflow_job_id.py 17524754568 i-0dd977e7b70f3c8d7 2025-09-07T06:29:56.7769584Z Setting output job-id=49774041707 2025-09-07T06:29:56.7852907Z Setting output job-name=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:29:56.7887423Z ##[group]Run python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-09-07T06:29:56.7888199Z python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-09-07T06:29:56.7889196Z python3 -m tools.stats.monitor --log-interval "$MONITOR_LOG_INTERVAL" --data-collect-interval "$MONITOR_DATA_COLLECT_INTERVAL" > usage_log.txt 2>&1 & 2025-09-07T06:29:56.7890085Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:29:56.7896763Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:56.7897156Z env: 2025-09-07T06:29:56.7897381Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:56.7897650Z JOB_ID: 49774041707 2025-09-07T06:29:56.7898057Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:29:56.7898544Z WORKFLOW_NAME: pull 2025-09-07T06:29:56.7898807Z WORKFLOW_RUN_ID: 17524754568 2025-09-07T06:29:56.7899149Z MONITOR_LOG_INTERVAL: 5 2025-09-07T06:29:56.7899440Z MONITOR_DATA_COLLECT_INTERVAL: 1 2025-09-07T06:29:56.7899746Z ##[endgroup] 2025-09-07T06:29:57.6527259Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T06:29:58.1739952Z Collecting psutil==5.9.8 2025-09-07T06:29:58.2070854Z Downloading psutil-5.9.8-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (288 kB) 2025-09-07T06:29:58.3702221Z Collecting dataclasses_json==0.6.7 2025-09-07T06:29:58.3792886Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-09-07T06:29:58.4375329Z Collecting nvidia-ml-py==11.525.84 2025-09-07T06:29:58.4469629Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2025-09-07T06:29:58.5861121Z Collecting marshmallow<4.0.0,>=3.18.0 2025-09-07T06:29:58.5952718Z Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB) 2025-09-07T06:29:58.6569841Z Collecting typing-inspect<1,>=0.4.0 2025-09-07T06:29:58.6659643Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-09-07T06:29:58.7666558Z Collecting packaging>=17.0 2025-09-07T06:29:58.7759154Z Downloading packaging-25.0-py3-none-any.whl (66 kB) 2025-09-07T06:29:58.8419963Z Collecting mypy-extensions>=0.3.0 2025-09-07T06:29:58.8510068Z Downloading mypy_extensions-1.1.0-py3-none-any.whl (5.0 kB) 2025-09-07T06:29:58.9237961Z Collecting typing-extensions>=3.7.4 2025-09-07T06:29:58.9328686Z Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB) 2025-09-07T06:29:59.1759641Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json 2025-09-07T06:29:59.7651400Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.1.0 nvidia-ml-py-11.525.84 packaging-25.0 psutil-5.9.8 typing-extensions-4.15.0 typing-inspect-0.9.0 2025-09-07T06:30:00.0431954Z Prepare all required actions 2025-09-07T06:30:00.0432401Z Getting action download info 2025-09-07T06:30:00.1799453Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-09-07T06:30:00.3983374Z Download action repository 'actions/download-artifact@v4' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-09-07T06:30:00.7460918Z ##[group]Run ./.github/actions/download-build-artifacts 2025-09-07T06:30:00.7461462Z with: 2025-09-07T06:30:00.7461708Z name: linux-jammy-py3.13-clang12 2025-09-07T06:30:00.7462042Z s3-bucket: gha-artifacts 2025-09-07T06:30:00.7462298Z env: 2025-09-07T06:30:00.7462522Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:00.7462793Z ##[endgroup] 2025-09-07T06:30:00.7507226Z ##[group]Run seemethere/download-artifact-s3@v4 2025-09-07T06:30:00.7507589Z with: 2025-09-07T06:30:00.7507825Z name: linux-jammy-py3.13-clang12 2025-09-07T06:30:00.7508138Z s3-bucket: gha-artifacts 2025-09-07T06:30:00.7508456Z region: us-east-1 2025-09-07T06:30:00.7508677Z env: 2025-09-07T06:30:00.7508896Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:00.7509313Z ##[endgroup] 2025-09-07T06:30:01.2326305Z (node:42843) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-09-07T06:30:01.2326833Z 2025-09-07T06:30:01.2327036Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-09-07T06:30:01.2327611Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-09-07T06:30:01.2328189Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-09-07T06:30:01.4275441Z Found 1 objects with prefix pytorch/pytorch/17524754568/linux-jammy-py3.13-clang12/ 2025-09-07T06:30:01.4276651Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-09-07T06:30:06.0798709Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-09-07T06:30:06.0805580Z Artifact download has finished successfully 2025-09-07T06:30:06.0984716Z ##[group]Run unzip -o artifacts.zip 2025-09-07T06:30:06.0985105Z unzip -o artifacts.zip 2025-09-07T06:30:06.0991709Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:06.0992124Z env: 2025-09-07T06:30:06.0992380Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:06.0992679Z ##[endgroup] 2025-09-07T06:30:06.1049308Z Archive: artifacts.zip 2025-09-07T06:30:06.1050401Z creating: dist/ 2025-09-07T06:30:07.2524253Z inflating: dist/torch-2.9.0a0+git93fb23d-cp313-cp313-linux_x86_64.whl 2025-09-07T06:30:07.2640899Z inflating: dist/.ninja_log 2025-09-07T06:30:07.2641347Z creating: build/custom_test_artifacts/ 2025-09-07T06:30:07.2642155Z creating: build/custom_test_artifacts/custom-op-build/ 2025-09-07T06:30:07.2642699Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2025-09-07T06:30:07.2643314Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/pkgRedirects/ 2025-09-07T06:30:07.2645451Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeConfigureLog.yaml 2025-09-07T06:30:07.2646184Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/ 2025-09-07T06:30:07.2646864Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/CMakeSystem.cmake 2025-09-07T06:30:07.2647587Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/CompilerIdC/ 2025-09-07T06:30:07.2648300Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/CompilerIdC/tmp/ 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build/bin/broadcast_test 2025-09-07T06:30:12.8542346Z inflating: build/bin/cpu_allocator_test 2025-09-07T06:30:12.8597670Z inflating: build/bin/cpu_generator_test 2025-09-07T06:30:12.8649422Z inflating: build/bin/cpu_profiling_allocator_test 2025-09-07T06:30:12.8739258Z inflating: build/bin/cpu_rng_test 2025-09-07T06:30:12.8789506Z inflating: build/bin/dlconvertor_test 2025-09-07T06:30:12.8846369Z inflating: build/bin/extension_backend_test 2025-09-07T06:30:12.8898084Z inflating: build/bin/half_test 2025-09-07T06:30:12.8993170Z inflating: build/bin/ivalue_test 2025-09-07T06:30:12.9042231Z inflating: build/bin/lazy_tensor_test 2025-09-07T06:30:12.9094334Z inflating: build/bin/math_kernel_test 2025-09-07T06:30:12.9146276Z inflating: build/bin/memory_format_test 2025-09-07T06:30:12.9197790Z inflating: build/bin/memory_overlapping_test 2025-09-07T06:30:12.9249744Z inflating: build/bin/mobile_memory_cleanup 2025-09-07T06:30:12.9305331Z inflating: build/bin/native_test 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build/bin/type_ptr_test 2025-09-07T06:30:13.0185277Z inflating: build/bin/type_test 2025-09-07T06:30:13.0236017Z inflating: build/bin/undefined_tensor_test 2025-09-07T06:30:13.0285054Z inflating: build/bin/verify_api_visibility 2025-09-07T06:30:13.0356859Z inflating: build/bin/legacy_vmap_test 2025-09-07T06:30:13.0407283Z inflating: build/bin/weakref_test 2025-09-07T06:30:13.0457711Z inflating: build/bin/wrapdim_test 2025-09-07T06:30:13.0508283Z inflating: build/bin/xla_tensor_test 2025-09-07T06:30:13.0565959Z inflating: build/bin/IListRef_test 2025-09-07T06:30:13.0661788Z inflating: build/bin/List_test 2025-09-07T06:30:13.0724303Z inflating: build/bin/KernelFunction_test 2025-09-07T06:30:13.0850373Z inflating: build/bin/kernel_function_legacy_test 2025-09-07T06:30:13.0944711Z inflating: build/bin/kernel_function_test 2025-09-07T06:30:13.1071747Z inflating: build/bin/kernel_lambda_legacy_test 2025-09-07T06:30:13.1172606Z inflating: build/bin/kernel_lambda_test 2025-09-07T06:30:13.1231954Z inflating: build/bin/kernel_stackbased_test 2025-09-07T06:30:13.1327504Z inflating: build/bin/make_boxed_from_unboxed_functor_test 2025-09-07T06:30:13.1377174Z inflating: build/bin/CppSignature_test 2025-09-07T06:30:13.1430391Z inflating: build/bin/backend_fallback_test 2025-09-07T06:30:13.1478735Z inflating: build/bin/op_allowlist_test 2025-09-07T06:30:13.1780274Z inflating: build/bin/op_registration_test 2025-09-07T06:30:13.1842614Z inflating: build/bin/inline_container_test 2025-09-07T06:30:13.2165777Z inflating: build/bin/test_nativert 2025-09-07T06:30:13.2218422Z inflating: build/bin/HashStoreTest 2025-09-07T06:30:13.2284248Z inflating: build/bin/ProcessGroupGlooTest 2025-09-07T06:30:13.2287659Z inflating: build/bin/example_allreduce 2025-09-07T06:30:13.3337572Z inflating: build/bin/test_jit 2025-09-07T06:30:13.3391458Z inflating: build/bin/test_dist_autograd 2025-09-07T06:30:13.3457542Z inflating: build/bin/test_cpp_rpc 2025-09-07T06:30:13.3460178Z inflating: build/bin/parallel_benchmark 2025-09-07T06:30:13.4726990Z inflating: build/bin/test_api 2025-09-07T06:30:13.5041033Z inflating: build/bin/test_lazy 2025-09-07T06:30:13.5045631Z inflating: build/bin/torch_shm_manager 2025-09-07T06:30:13.5046260Z creating: .additional_ci_files/ 2025-09-07T06:30:13.5133830Z inflating: .additional_ci_files/test-times.json 2025-09-07T06:30:13.5464521Z inflating: .additional_ci_files/test-class-times.json 2025-09-07T06:30:13.5491209Z ##[group]Run rm artifacts.zip 2025-09-07T06:30:13.5491561Z rm artifacts.zip 2025-09-07T06:30:13.5498441Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:13.5498861Z env: 2025-09-07T06:30:13.5499095Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:13.5499373Z ##[endgroup] 2025-09-07T06:30:13.5971131Z ##[group]Run df -H 2025-09-07T06:30:13.5971409Z df -H 2025-09-07T06:30:13.5977702Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:13.5978083Z env: 2025-09-07T06:30:13.5978308Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:13.5978578Z ##[endgroup] 2025-09-07T06:30:13.6197837Z Filesystem Size Used Avail Use% Mounted on 2025-09-07T06:30:13.6198270Z devtmpfs 4.2M 0 4.2M 0% /dev 2025-09-07T06:30:13.6198607Z tmpfs 8.2G 0 8.2G 0% /dev/shm 2025-09-07T06:30:13.6198949Z tmpfs 3.3G 488k 3.3G 1% /run 2025-09-07T06:30:13.6199282Z /dev/nvme0n1p1 161G 27G 135G 17% / 2025-09-07T06:30:13.6199636Z tmpfs 8.2G 13k 8.2G 1% /tmp 2025-09-07T06:30:13.6199979Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2025-09-07T06:30:13.6290538Z Prepare all required actions 2025-09-07T06:30:13.6291337Z Getting action download info 2025-09-07T06:30:13.8020036Z ##[group]Run ./.github/actions/download-td-artifacts 2025-09-07T06:30:13.8020414Z with: 2025-09-07T06:30:13.8020630Z env: 2025-09-07T06:30:13.8020853Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:13.8021114Z ##[endgroup] 2025-09-07T06:30:13.8077234Z ##[group]Run seemethere/download-artifact-s3@v4 2025-09-07T06:30:13.8077599Z with: 2025-09-07T06:30:13.8077804Z name: td_results 2025-09-07T06:30:13.8078054Z s3-bucket: gha-artifacts 2025-09-07T06:30:13.8078328Z region: us-east-1 2025-09-07T06:30:13.8078559Z env: 2025-09-07T06:30:13.8078763Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:13.8079028Z ##[endgroup] 2025-09-07T06:30:14.2710582Z (node:42861) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-09-07T06:30:14.2711107Z 2025-09-07T06:30:14.2711383Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-09-07T06:30:14.2711959Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-09-07T06:30:14.2712535Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-09-07T06:30:14.3799352Z Found 1 objects with prefix pytorch/pytorch/17524754568/td_results/ 2025-09-07T06:30:14.3800032Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-09-07T06:30:14.4400474Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-09-07T06:30:14.4405552Z Artifact download has finished successfully 2025-09-07T06:30:14.5375295Z ##[group]Run mkdir -p .additional_ci_files 2025-09-07T06:30:14.5375692Z mkdir -p .additional_ci_files 2025-09-07T06:30:14.5376147Z mv td_results.json .additional_ci_files/td_results.json || true 2025-09-07T06:30:14.5382328Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:14.5382711Z env: 2025-09-07T06:30:14.5382960Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:14.5383373Z ##[endgroup] 2025-09-07T06:30:14.5654602Z ##[group]Run .github/scripts/parse_ref.py 2025-09-07T06:30:14.5655001Z .github/scripts/parse_ref.py 2025-09-07T06:30:14.5660972Z shell: /usr/bin/bash -e {0} 2025-09-07T06:30:14.5661263Z env: 2025-09-07T06:30:14.5661488Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:14.5661744Z ##[endgroup] 2025-09-07T06:30:14.6153546Z Setting output branch=main 2025-09-07T06:30:14.6303206Z Prepare all required actions 2025-09-07T06:30:14.6303627Z Getting action download info 2025-09-07T06:30:14.7577631Z ##[group]Run ./.github/actions/filter-test-configs 2025-09-07T06:30:14.7578008Z with: 2025-09-07T06:30:14.7578455Z github-token: *** 2025-09-07T06:30:14.7581524Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-09-07T06:30:14.7584637Z job-name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:30:14.7585119Z env: 2025-09-07T06:30:14.7585325Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:14.7585594Z ##[endgroup] 2025-09-07T06:30:14.7756200Z ##[group]Run nick-fields/retry@v3.0.0 2025-09-07T06:30:14.7756527Z with: 2025-09-07T06:30:14.7756729Z shell: bash 2025-09-07T06:30:14.7756981Z timeout_minutes: 10 2025-09-07T06:30:14.7757282Z max_attempts: 5 2025-09-07T06:30:14.7757525Z retry_wait_seconds: 30 2025-09-07T06:30:14.7758331Z 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==6.0.2 2025-09-07T06:30:14.7759205Z polling_interval_seconds: 1 2025-09-07T06:30:14.7759497Z warning_on_retry: true 2025-09-07T06:30:14.7759768Z continue_on_error: false 2025-09-07T06:30:14.7760080Z env: 2025-09-07T06:30:14.7760281Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:14.7760769Z GITHUB_TOKEN: *** 2025-09-07T06:30:14.7761012Z ##[endgroup] 2025-09-07T06:30:14.9554041Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.2 2025-09-07T06:30:15.1989116Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T06:30:15.3312883Z Collecting requests==2.27.1 2025-09-07T06:30:15.3473927Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-09-07T06:30:15.5559239Z Collecting pyyaml==6.0.2 2025-09-07T06:30:15.5600119Z Downloading PyYAML-6.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (737 kB) 2025-09-07T06:30:15.6184790Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-09-07T06:30:15.9991846Z Collecting charset-normalizer~=2.0.0 2025-09-07T06:30:16.0032025Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-09-07T06:30:16.0204006Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-09-07T06:30:16.0885935Z Collecting certifi>=2017.4.17 2025-09-07T06:30:16.0926995Z Downloading certifi-2025.8.3-py3-none-any.whl (161 kB) 2025-09-07T06:30:16.1952563Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2025-09-07T06:30:16.3123340Z Successfully installed certifi-2025.8.3 charset-normalizer-2.0.12 pyyaml-6.0.2 requests-2.27.1 2025-09-07T06:30:16.8534340Z Command completed after 1 attempt(s). 2025-09-07T06:30:16.8591475Z ##[group]Run set -x 2025-09-07T06:30:16.8591756Z set -x 2025-09-07T06:30:16.8591985Z  2025-09-07T06:30:16.8592364Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-09-07T06:30:16.8592858Z # in runner workspace 2025-09-07T06:30:16.8593448Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-09-07T06:30:16.8599507Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:16.8599893Z env: 2025-09-07T06:30:16.8600102Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:16.8600369Z ##[endgroup] 2025-09-07T06:30:16.8625767Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-09-07T06:30:16.8806712Z Setting output branch=main 2025-09-07T06:30:16.8866146Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-09-07T06:30:16.8866606Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-09-07T06:30:16.8881081Z echo "Job name: ${JOB_NAME}" 2025-09-07T06:30:16.8881401Z  2025-09-07T06:30:16.8881854Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-09-07T06:30:16.8882384Z # in runner workspace 2025-09-07T06:30:16.8882846Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-09-07T06:30:16.8883352Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-09-07T06:30:16.8883717Z  --job-name "${JOB_NAME}" \ 2025-09-07T06:30:16.8886762Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]}" \ 2025-09-07T06:30:16.8889761Z  --selected-test-configs "" \ 2025-09-07T06:30:16.8890095Z  --pr-number "${PR_NUMBER}" \ 2025-09-07T06:30:16.8890418Z  --tag "${TAG}" \ 2025-09-07T06:30:16.8890717Z  --event-name "${EVENT_NAME}" \ 2025-09-07T06:30:16.8891048Z  --schedule "${SCHEDULE}" \ 2025-09-07T06:30:16.8891352Z  --branch "${HEAD_BRANCH}" 2025-09-07T06:30:16.8897302Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:16.8897698Z env: 2025-09-07T06:30:16.8897926Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:16.8898493Z GITHUB_TOKEN: *** 2025-09-07T06:30:16.8898897Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:30:16.8899379Z PR_NUMBER: 2025-09-07T06:30:16.8899604Z TAG: 2025-09-07T06:30:16.8899816Z EVENT_NAME: push 2025-09-07T06:30:16.8900048Z SCHEDULE: 2025-09-07T06:30:16.8900268Z HEAD_BRANCH: main 2025-09-07T06:30:16.8900505Z ##[endgroup] 2025-09-07T06:30:16.8925332Z Workflow: pull 2025-09-07T06:30:16.8926048Z Job name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:30:17.0694749Z Setting output keep-going=True 2025-09-07T06:30:17.0695152Z Setting output ci-verbose-test-logs=False 2025-09-07T06:30:17.0695504Z Setting output ci-test-showlocals=False 2025-09-07T06:30:17.0695855Z Setting output ci-no-test-timeout=False 2025-09-07T06:30:17.0696187Z Setting output ci-no-td=False 2025-09-07T06:30:17.0696724Z Setting output ci-td-distributed=False 2025-09-07T06:30:17.0697052Z Setting output is-unstable=False 2025-09-07T06:30:17.0697374Z Setting output reenabled-issues= 2025-09-07T06:30:17.0700522Z Setting output test-matrix={"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-09-07T06:30:17.0703567Z Setting output is-test-matrix-empty=False 2025-09-07T06:30:17.0848086Z ##[group]Run echo "Filtered matrix:" 2025-09-07T06:30:17.0848470Z echo "Filtered matrix:" 2025-09-07T06:30:17.0851432Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]}" 2025-09-07T06:30:17.0854355Z  2025-09-07T06:30:17.0854566Z echo 2025-09-07T06:30:17.0854851Z echo "Is the current job unstable? False" 2025-09-07T06:30:17.0855184Z  2025-09-07T06:30:17.0855394Z echo 2025-09-07T06:30:17.0855659Z echo "Is keep-going label set? True" 2025-09-07T06:30:17.0855988Z  2025-09-07T06:30:17.0856186Z echo 2025-09-07T06:30:17.0856428Z echo "Reenabled issues? " 2025-09-07T06:30:17.0862376Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:17.0862764Z env: 2025-09-07T06:30:17.0862976Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:17.0863244Z ##[endgroup] 2025-09-07T06:30:17.0887797Z Filtered matrix: 2025-09-07T06:30:17.0892008Z {include: [{config: default, shard: 1, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: linux.4xlarge}, {config: crossref, shard: 1, num_shards: 2, runner: linux.2xlarge}, {config: crossref, shard: 2, num_shards: 2, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: linux.2xlarge}, {config: einops, shard: 1, num_shards: 1, runner: linux.2xlarge}]} 2025-09-07T06:30:17.0894732Z 2025-09-07T06:30:17.0894870Z Is the current job unstable? False 2025-09-07T06:30:17.0895075Z 2025-09-07T06:30:17.0895192Z Is keep-going label set? True 2025-09-07T06:30:17.0895385Z 2025-09-07T06:30:17.0895478Z Reenabled issues? 2025-09-07T06:30:17.0939967Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-09-07T06:30:17.0940521Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-09-07T06:30:17.0946435Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:17.0946825Z env: 2025-09-07T06:30:17.0947048Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:17.0947321Z JOB_TIMEOUT: 240 2025-09-07T06:30:17.0947551Z ##[endgroup] 2025-09-07T06:30:17.1003219Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:30:17.1003789Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:30:17.1004364Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:30:17.1010126Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:30:17.1010516Z env: 2025-09-07T06:30:17.1010739Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:17.1011008Z ##[endgroup] 2025-09-07T06:30:17.1119321Z ##[group]Run set -x 2025-09-07T06:30:17.1119708Z set -x 2025-09-07T06:30:17.1119929Z  2025-09-07T06:30:17.1120191Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-09-07T06:30:17.1120606Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-09-07T06:30:17.1121028Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-09-07T06:30:17.1121395Z  TEST_COMMAND=.ci/onnx/test.sh 2025-09-07T06:30:17.1121707Z else 2025-09-07T06:30:17.1121968Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-09-07T06:30:17.1122290Z fi 2025-09-07T06:30:17.1122490Z  2025-09-07T06:30:17.1122761Z # Leaving 1GB for the runner and other things 2025-09-07T06:30:17.1123364Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-09-07T06:30:17.1124398Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-09-07T06:30:17.1125137Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-09-07T06:30:17.1125693Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-09-07T06:30:17.1126130Z  2025-09-07T06:30:17.1126406Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-09-07T06:30:17.1126753Z  SHM_OPTS= 2025-09-07T06:30:17.1127010Z  JENKINS_USER= 2025-09-07T06:30:17.1127373Z  # ensure that docker container cleanly exits in 12 hours 2025-09-07T06:30:17.1127871Z  # if for some reason cleanup action doesn't stop container 2025-09-07T06:30:17.1128279Z  # when job is cancelled 2025-09-07T06:30:17.1128620Z  DOCKER_SHELL_CMD="sleep 12h" 2025-09-07T06:30:17.1128919Z else 2025-09-07T06:30:17.1129184Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-09-07T06:30:17.1129537Z  JENKINS_USER="--user jenkins" 2025-09-07T06:30:17.1129870Z  DOCKER_SHELL_CMD= 2025-09-07T06:30:17.1130134Z fi 2025-09-07T06:30:17.1130351Z  2025-09-07T06:30:17.1130710Z # detached container should get cleaned up by teardown_ec2_linux 2025-09-07T06:30:17.1131270Z # TODO: Stop building test binaries as part of the build phase 2025-09-07T06:30:17.1131909Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-09-07T06:30:17.1132460Z # shellcheck disable=SC2086,SC2090 2025-09-07T06:30:17.1132818Z container_name=$(docker run \ 2025-09-07T06:30:17.1133142Z  ${GPU_FLAG:-} \ 2025-09-07T06:30:17.1133462Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-09-07T06:30:17.1133813Z  -e BUILD_ENVIRONMENT \ 2025-09-07T06:30:17.1134123Z  -e PR_NUMBER \ 2025-09-07T06:30:17.1134409Z  -e GITHUB_ACTIONS \ 2025-09-07T06:30:17.1134713Z  -e GITHUB_REPOSITORY \ 2025-09-07T06:30:17.1135015Z  -e GITHUB_WORKFLOW \ 2025-09-07T06:30:17.1135462Z  -e GITHUB_JOB \ 2025-09-07T06:30:17.1135746Z  -e GITHUB_RUN_ID \ 2025-09-07T06:30:17.1136039Z  -e GITHUB_RUN_NUMBER \ 2025-09-07T06:30:17.1136332Z  -e GITHUB_RUN_ATTEMPT \ 2025-09-07T06:30:17.1136637Z  -e JOB_ID \ 2025-09-07T06:30:17.1136898Z  -e JOB_NAME \ 2025-09-07T06:30:17.1137167Z  -e BASE_SHA \ 2025-09-07T06:30:17.1137417Z  -e BRANCH \ 2025-09-07T06:30:17.1137673Z  -e SHA1 \ 2025-09-07T06:30:17.1137930Z  -e AWS_DEFAULT_REGION \ 2025-09-07T06:30:17.1138234Z  -e IN_WHEEL_TEST \ 2025-09-07T06:30:17.1138513Z  -e SHARD_NUMBER \ 2025-09-07T06:30:17.1138803Z  -e TEST_CONFIG \ 2025-09-07T06:30:17.1139090Z  -e NUM_TEST_SHARDS \ 2025-09-07T06:30:17.1139394Z  -e REENABLED_ISSUES \ 2025-09-07T06:30:17.1139697Z  -e CONTINUE_THROUGH_ERROR \ 2025-09-07T06:30:17.1140113Z  -e VERBOSE_TEST_LOGS \ 2025-09-07T06:30:17.1140425Z  -e TEST_SHOWLOCALS \ 2025-09-07T06:30:17.1140723Z  -e NO_TEST_TIMEOUT \ 2025-09-07T06:30:17.1141010Z  -e NO_TD \ 2025-09-07T06:30:17.1141258Z  -e TD_DISTRIBUTED \ 2025-09-07T06:30:17.1141551Z  -e PR_LABELS \ 2025-09-07T06:30:17.1141859Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-09-07T06:30:17.1142204Z  -e SCCACHE_BUCKET \ 2025-09-07T06:30:17.1142482Z  -e SCCACHE_REGION \ 2025-09-07T06:30:17.1142770Z  -e XLA_CUDA \ 2025-09-07T06:30:17.1143068Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-09-07T06:30:17.1143443Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-09-07T06:30:17.1143818Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-09-07T06:30:17.1144203Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-09-07T06:30:17.1144559Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-09-07T06:30:17.1144906Z  -e VLLM_TEST_HUGGING_FACE_TOKEN \ 2025-09-07T06:30:17.1145253Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-09-07T06:30:17.1145586Z  -e DASHBOARD_TAG \ 2025-09-07T06:30:17.1145886Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-09-07T06:30:17.1146258Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-09-07T06:30:17.1146696Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-09-07T06:30:17.1147114Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-09-07T06:30:17.1147524Z  --security-opt seccomp=unconfined \ 2025-09-07T06:30:17.1147877Z  --cap-add=SYS_PTRACE \ 2025-09-07T06:30:17.1148180Z  --ipc=host \ 2025-09-07T06:30:17.1148432Z  ${SHM_OPTS} \ 2025-09-07T06:30:17.1148692Z  --tty \ 2025-09-07T06:30:17.1148936Z  --detach \ 2025-09-07T06:30:17.1149210Z  --name="${container_name}" \ 2025-09-07T06:30:17.1149521Z  ${JENKINS_USER} \ 2025-09-07T06:30:17.1149884Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-09-07T06:30:17.1150300Z  -w /var/lib/jenkins/workspace \ 2025-09-07T06:30:17.1150634Z  "${DOCKER_IMAGE}" \ 2025-09-07T06:30:17.1150912Z  ${DOCKER_SHELL_CMD} 2025-09-07T06:30:17.1151183Z ) 2025-09-07T06:30:17.1151481Z # Propagate download.pytorch.org IP to container 2025-09-07T06:30:17.1152169Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-09-07T06:30:17.1152909Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-09-07T06:30:17.1153317Z  2025-09-07T06:30:17.1153590Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-09-07T06:30:17.1154189Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-09-07T06:30:17.1154728Z fi 2025-09-07T06:30:17.1154933Z  2025-09-07T06:30:17.1155443Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-09-07T06:30:17.1161555Z shell: /usr/bin/bash -e {0} 2025-09-07T06:30:17.1161841Z env: 2025-09-07T06:30:17.1162071Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:30:17.1162391Z BUILD_ENVIRONMENT: linux-jammy-py3.13-clang12 2025-09-07T06:30:17.1162741Z PR_NUMBER: 2025-09-07T06:30:17.1162990Z GITHUB_REPOSITORY: pytorch/pytorch 2025-09-07T06:30:17.1163306Z GITHUB_WORKFLOW: pull 2025-09-07T06:30:17.1163553Z GITHUB_JOB: test 2025-09-07T06:30:17.1163797Z GITHUB_RUN_ID: 17524754568 2025-09-07T06:30:17.1164192Z GITHUB_RUN_NUMBER: 353985 2025-09-07T06:30:17.1164472Z GITHUB_RUN_ATTEMPT: 1 2025-09-07T06:30:17.1164719Z JOB_ID: 49774041707 2025-09-07T06:30:17.1165144Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:30:17.1165631Z BRANCH: main 2025-09-07T06:30:17.1165902Z SHA1: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:17.1166392Z BASE_SHA: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:17.1166770Z TEST_CONFIG: dynamo_wrapped 2025-09-07T06:30:17.1167054Z SHARD_NUMBER: 1 2025-09-07T06:30:17.1167295Z NUM_TEST_SHARDS: 3 2025-09-07T06:30:17.1167533Z REENABLED_ISSUES: 2025-09-07T06:30:17.1167800Z CONTINUE_THROUGH_ERROR: True 2025-09-07T06:30:17.1168100Z VERBOSE_TEST_LOGS: False 2025-09-07T06:30:17.1168383Z TEST_SHOWLOCALS: False 2025-09-07T06:30:17.1168641Z NO_TEST_TIMEOUT: False 2025-09-07T06:30:17.1168906Z NO_TD: False 2025-09-07T06:30:17.1169146Z TD_DISTRIBUTED: False 2025-09-07T06:30:17.1169504Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-09-07T06:30:17.1169867Z SCCACHE_REGION: us-east-1 2025-09-07T06:30:17.1170144Z SHM_SIZE: 1g 2025-09-07T06:30:17.1170890Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:30:17.1171697Z XLA_CUDA: 2025-09-07T06:30:17.1172071Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-09-07T06:30:17.1172532Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-09-07T06:30:17.1172863Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-09-07T06:30:17.1173172Z DASHBOARD_TAG: 2025-09-07T06:30:17.1173928Z VLLM_TEST_HUGGING_FACE_TOKEN: *** 2025-09-07T06:30:17.1174352Z HUGGING_FACE_HUB_TOKEN: *** 2025-09-07T06:30:17.1174776Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-09-07T06:30:17.1175209Z ARTIFACTS_FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_49774041707 2025-09-07T06:30:17.1175668Z ##[endgroup] 2025-09-07T06:30:17.1201060Z + [[ dynamo_wrapped == \m\u\l\t\i\g\p\u ]] 2025-09-07T06:30:17.1201432Z + [[ linux-jammy-py3.13-clang12 == *onnx* ]] 2025-09-07T06:30:17.1201772Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-09-07T06:30:17.1204593Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-09-07T06:30:17.1282003Z + TOTAL_AVAILABLE_MEMORY_IN_GB='14.244 ' 2025-09-07T06:30:17.1282552Z + TOTAL_MEMORY_WITH_SWAP=17 2025-09-07T06:30:17.1282924Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-09-07T06:30:17.1283282Z + SHM_OPTS=--shm-size=1g 2025-09-07T06:30:17.1283563Z + JENKINS_USER='--user jenkins' 2025-09-07T06:30:17.1283848Z + DOCKER_SHELL_CMD= 2025-09-07T06:30:17.1291077Z +++ nproc --ignore=2 2025-09-07T06:30:17.1314802Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_REGION -e 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 VLLM_TEST_HUGGING_FACE_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e ARTIFACTS_FILE_SUFFIX --memory=14g --memory-swap=17g --env-file=/tmp/github_env_17524754568 --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/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:30:22.0250204Z + container_name=1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T06:30:22.0253101Z + grep download.pytorch.org /etc/hosts 2025-09-07T06:30:22.0254908Z + docker exec -i 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 sudo bash -c '/bin/cat >> /etc/hosts' 2025-09-07T06:30:22.2219341Z + echo DOCKER_CONTAINER_ID=1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T06:30:22.2220852Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-09-07T06:30:22.2225203Z ++ echo dist/torch-2.9.0a0+git93fb23d-cp313-cp313-linux_x86_64.whl 2025-09-07T06:30:22.2227216Z + docker exec -t 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 sh -c 'python3 -m pip install dist/torch-2.9.0a0+git93fb23d-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-09-07T06:30:22.7955954Z Processing ./dist/torch-2.9.0a0+git93fb23d-cp313-cp313-linux_x86_64.whl (from torch==2.9.0a0+git93fb23d) 2025-09-07T06:30:23.2918077Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (3.19.1) 2025-09-07T06:30:23.2920977Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (4.15.0) 2025-09-07T06:30:23.2925653Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (80.9.0) 2025-09-07T06:30:23.2929147Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (1.13.3) 2025-09-07T06:30:23.2932614Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (2.8.8) 2025-09-07T06:30:23.2935470Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (3.1.6) 2025-09-07T06:30:23.2939360Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (2025.7.0) 2025-09-07T06:30:23.2951723Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (3.3.0) 2025-09-07T06:30:23.3055779Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (2.1.2) 2025-09-07T06:30:23.3101620Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (1.3.0) 2025-09-07T06:30:23.3133510Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (3.0.2) 2025-09-07T06:30:23.5126408Z Installing collected packages: torch 2025-09-07T06:30:34.7288450Z Successfully installed torch-2.9.0a0+git93fb23d 2025-09-07T06:30:34.8133622Z + export TERM=vt100 2025-09-07T06:30:34.8134075Z + TERM=vt100 2025-09-07T06:30:34.8135978Z ++ dirname .ci/pytorch/test.sh 2025-09-07T06:30:34.8156911Z + source .ci/pytorch/common.sh 2025-09-07T06:30:34.8165130Z +++ dirname .ci/pytorch/common.sh 2025-09-07T06:30:34.8172399Z ++ source .ci/pytorch/common_utils.sh 2025-09-07T06:30:34.8180392Z +++ declare -f -t trap_add 2025-09-07T06:30:34.8185610Z ++ set -ex -o pipefail 2025-09-07T06:30:34.8185923Z ++ [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-09-07T06:30:34.8186269Z ++ BUILD_TEST_LIBTORCH=0 2025-09-07T06:30:34.8189369Z ++ dirname .ci/pytorch/test.sh 2025-09-07T06:30:34.8209884Z + source .ci/pytorch/common-build.sh 2025-09-07T06:30:34.8217264Z ++ [[ linux-jammy-py3.13-clang12 != *win-* ]] 2025-09-07T06:30:34.8223779Z ++++ dirname .ci/pytorch/common-build.sh 2025-09-07T06:30:34.8231006Z +++ cd .ci/pytorch 2025-09-07T06:30:34.8231498Z +++ pwd -P 2025-09-07T06:30:34.8233689Z ++ script_dir=/var/lib/jenkins/workspace/.ci/pytorch 2025-09-07T06:30:34.8234333Z ++ [[ linux-jammy-py3.13-clang12 == *-pch* ]] 2025-09-07T06:30:34.8234659Z ++ which sccache 2025-09-07T06:30:34.8256265Z ++ [[ -z ossci-compiler-cache-circleci-v2 ]] 2025-09-07T06:30:34.8256716Z ++ sccache --stop-server 2025-09-07T06:30:34.8316705Z ++ true 2025-09-07T06:30:34.8317187Z ++ rm -f /var/lib/jenkins/sccache_error.log 2025-09-07T06:30:34.8339310Z ++ trap_add sccache_epilogue EXIT 2025-09-07T06:30:34.8339881Z ++ trap_add_cmd=sccache_epilogue 2025-09-07T06:30:34.8340152Z ++ shift 2025-09-07T06:30:34.8340386Z ++ for trap_add_name in "$@" 2025-09-07T06:30:34.8345714Z ++++ trap -p EXIT 2025-09-07T06:30:34.8348311Z +++ eval 'extract_trap_cmd ' 2025-09-07T06:30:34.8348832Z ++++ extract_trap_cmd 2025-09-07T06:30:34.8349284Z ++++ printf '%s\n' '' 2025-09-07T06:30:34.8349590Z +++ printf '%s\n' sccache_epilogue 2025-09-07T06:30:34.8350775Z ++ trap -- ' 2025-09-07T06:30:34.8351173Z sccache_epilogue' EXIT 2025-09-07T06:30:34.8351624Z ++ [[ -n 1 ]] 2025-09-07T06:30:34.8352346Z ++ echo 'Skipping sccache server initialization, setting environment variables' 2025-09-07T06:30:34.8353412Z Skipping sccache server initialization, setting environment variables 2025-09-07T06:30:34.8353879Z ++ export SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:30:34.8354202Z ++ SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:30:34.8354558Z ++ export SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:30:34.8355019Z ++ SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:30:34.8355443Z ++ export RUST_LOG=sccache::server=error 2025-09-07T06:30:34.8355766Z ++ RUST_LOG=sccache::server=error 2025-09-07T06:30:34.8356069Z ++ sccache --zero-stats 2025-09-07T06:30:34.9551462Z Statistics zeroed. 2025-09-07T06:30:34.9555276Z ++ which ccache 2025-09-07T06:30:34.9580178Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-09-07T06:30:34.9580586Z + [[ linux-jammy-py3.13-clang12 != *s390x* ]] 2025-09-07T06:30:34.9580950Z + [[ -d /var/lib/jenkins/workspace ]] 2025-09-07T06:30:34.9583117Z ++ stat -c %u /var/lib/jenkins/workspace 2025-09-07T06:30:34.9640722Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-09-07T06:30:34.9641145Z + trap_add cleanup_workspace EXIT 2025-09-07T06:30:34.9641510Z + trap_add_cmd=cleanup_workspace 2025-09-07T06:30:34.9641818Z + shift 2025-09-07T06:30:34.9642048Z + for trap_add_name in "$@" 2025-09-07T06:30:34.9648164Z +++ trap -p EXIT 2025-09-07T06:30:34.9650686Z ++ eval 'extract_trap_cmd trap -- '\'' 2025-09-07T06:30:34.9651063Z sccache_epilogue'\'' EXIT' 2025-09-07T06:30:34.9651357Z +++ extract_trap_cmd trap -- ' 2025-09-07T06:30:34.9651639Z sccache_epilogue' EXIT 2025-09-07T06:30:34.9651908Z +++ printf '%s\n' ' 2025-09-07T06:30:34.9652158Z sccache_epilogue' 2025-09-07T06:30:34.9652456Z ++ printf '%s\n' cleanup_workspace 2025-09-07T06:30:34.9653265Z + trap -- ' 2025-09-07T06:30:34.9653665Z sccache_epilogue 2025-09-07T06:30:34.9654091Z cleanup_workspace' EXIT 2025-09-07T06:30:34.9654404Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-09-07T06:30:35.6127328Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-09-07T06:30:35.6292957Z + echo 'Environment variables:' 2025-09-07T06:30:35.6293325Z Environment variables: 2025-09-07T06:30:35.6293576Z + env 2025-09-07T06:30:35.6314861Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T06:30:35.6315781Z CONTINUE_THROUGH_ERROR=True 2025-09-07T06:30:35.6316107Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-09-07T06:30:35.6316932Z VLLM_TEST_HUGGING_FACE_TOKEN=*** 2025-09-07T06:30:35.6317495Z HOSTNAME=1383124d873a 2025-09-07T06:30:35.6318151Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6318851Z GITHUB_ACTION=__run_2 2025-09-07T06:30:35.6319130Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-09-07T06:30:35.6319530Z GITHUB_RUN_NUMBER=353985 2025-09-07T06:30:35.6319910Z TEST_CONFIG=dynamo_wrapped 2025-09-07T06:30:35.6320298Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-09-07T06:30:35.6320695Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-09-07T06:30:35.6321023Z SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:30:35.6321496Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-09-07T06:30:35.6321984Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-09-07T06:30:35.6322311Z GITHUB_REF_TYPE=branch 2025-09-07T06:30:35.6322612Z BASE_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:35.6322953Z XLA_CUDA= 2025-09-07T06:30:35.6323186Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-09-07T06:30:35.6323589Z HUGGING_FACE_HUB_TOKEN=*** 2025-09-07T06:30:35.6324160Z *** 2025-09-07T06:30:35.6324454Z GITHUB_REPOSITORY_ID=65600975 2025-09-07T06:30:35.6324851Z GITHUB_ACTIONS=true 2025-09-07T06:30:35.6325256Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:30:35.6325840Z SHA1=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:35.6326427Z GITHUB_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:35.6327255Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-09-07T06:30:35.6328092Z UCC_HOME=/usr 2025-09-07T06:30:35.6328481Z VERBOSE_TEST_LOGS=False 2025-09-07T06:30:35.6328946Z GITHUB_REF=refs/heads/main 2025-09-07T06:30:35.6329372Z SHARD_NUMBER=1 2025-09-07T06:30:35.6329778Z GITHUB_REF_PROTECTED=true 2025-09-07T06:30:35.6330195Z HOME=/var/lib/jenkins 2025-09-07T06:30:35.6330670Z GITHUB_API_URL=https://api.github.com 2025-09-07T06:30:35.6331238Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-09-07T06:30:35.6331719Z UCX_COMMIT= 2025-09-07T06:30:35.6332036Z USE_SYSTEM_NCCL=1 2025-09-07T06:30:35.6332438Z NUM_TEST_SHARDS=3 2025-09-07T06:30:35.6332834Z UCX_HOME=/usr 2025-09-07T06:30:35.6333814Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6335244Z JOB_NAME=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:30:35.6336571Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6337864Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-09-07T06:30:35.6338726Z GITHUB_EVENT_NAME=push 2025-09-07T06:30:35.6339158Z DASHBOARD_TAG= 2025-09-07T06:30:35.6339553Z GITHUB_RUN_ID=17524754568 2025-09-07T06:30:35.6339991Z INSTALLED_OPENBLAS= 2025-09-07T06:30:35.6340882Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6341594Z GITHUB_ACTOR=pytorchmergebot 2025-09-07T06:30:35.6341875Z PR_NUMBER= 2025-09-07T06:30:35.6342089Z DESIRED_CUDA= 2025-09-07T06:30:35.6342322Z GITHUB_RUN_ATTEMPT=1 2025-09-07T06:30:35.6342572Z ANACONDA_PYTHON_VERSION=3.13 2025-09-07T06:30:35.6342921Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-09-07T06:30:35.6343274Z TERM=vt100 2025-09-07T06:30:35.6343492Z INSTALLED_VISION=yes 2025-09-07T06:30:35.6343723Z BRANCH=main 2025-09-07T06:30:35.6343950Z SCCACHE_REGION=us-east-1 2025-09-07T06:30:35.6344229Z OPENSSL_ROOT_DIR=/opt/openssl 2025-09-07T06:30:35.6344542Z CUDA_PATH=/usr/local/cuda 2025-09-07T06:30:35.6345079Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-09-07T06:30:35.6345680Z GITHUB_SERVER_URL=https://github.com 2025-09-07T06:30:35.6346138Z UCC_COMMIT= 2025-09-07T06:30:35.6346356Z REENABLED_ISSUES= 2025-09-07T06:30:35.6346584Z DOCS= 2025-09-07T06:30:35.6346772Z SHLVL=1 2025-09-07T06:30:35.6346976Z MAX_JOBS=6 2025-09-07T06:30:35.6347208Z GITHUB_ACTOR_ID=97764156 2025-09-07T06:30:35.6347560Z GITHUB_WORKFLOW_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:35.6347958Z GITHUB_REF_NAME=main 2025-09-07T06:30:35.6348341Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-09-07T06:30:35.6348787Z GITHUB_JOB=test 2025-09-07T06:30:35.6349027Z NO_TEST_TIMEOUT=False 2025-09-07T06:30:35.6349292Z TD_DISTRIBUTED=False 2025-09-07T06:30:35.6349556Z GITHUB_REPOSITORY=pytorch/pytorch 2025-09-07T06:30:35.6349871Z GITHUB_RETENTION_DAYS=90 2025-09-07T06:30:35.6350150Z OPENSSL_DIR=/opt/openssl 2025-09-07T06:30:35.6350417Z GITHUB_ACTION_REPOSITORY= 2025-09-07T06:30:35.6351299Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-09-07T06:30:35.6352140Z GITHUB_BASE_REF= 2025-09-07T06:30:35.6352382Z INSTALLED_ACL= 2025-09-07T06:30:35.6352762Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-linux.2xlarge_49774041707 2025-09-07T06:30:35.6353193Z CI=true 2025-09-07T06:30:35.6353424Z GITHUB_REPOSITORY_OWNER=pytorch 2025-09-07T06:30:35.6353764Z RUST_LOG=sccache::server=error 2025-09-07T06:30:35.6354045Z JOB_ID=49774041707 2025-09-07T06:30:35.6354333Z GITHUB_HEAD_REF= 2025-09-07T06:30:35.6354567Z GITHUB_ACTION_REF= 2025-09-07T06:30:35.6354861Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-09-07T06:30:35.6355221Z TEST_SHOWLOCALS=False 2025-09-07T06:30:35.6355465Z GITHUB_WORKFLOW=pull 2025-09-07T06:30:35.6355728Z DEBIAN_FRONTEND=noninteractive 2025-09-07T06:30:35.6356372Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6357032Z NO_TD=False 2025-09-07T06:30:35.6357258Z SKIP_SCCACHE_INITIALIZATION=1 2025-09-07T06:30:35.6357575Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-09-07T06:30:35.6357897Z _=/usr/bin/env 2025-09-07T06:30:35.6358221Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-09-07T06:30:35.6528192Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch 2025-09-07T06:30:35.6529220Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T06:30:35.6529987Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib 2025-09-07T06:30:35.6530606Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test 2025-09-07T06:30:35.6531073Z + BUILD_DIR=build 2025-09-07T06:30:35.6531317Z + BUILD_RENAMED_DIR=build_renamed 2025-09-07T06:30:35.6531624Z + BUILD_BIN_DIR=build/bin 2025-09-07T06:30:35.6531888Z + SHARD_NUMBER=1 2025-09-07T06:30:35.6532125Z + NUM_TEST_SHARDS=3 2025-09-07T06:30:35.6532374Z + export TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:30:35.6532707Z + TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:30:35.6533108Z + export VALGRIND=ON 2025-09-07T06:30:35.6533402Z + VALGRIND=ON 2025-09-07T06:30:35.6533696Z + [[ linux-jammy-py3.13-clang12 == *clang9* ]] 2025-09-07T06:30:35.6534063Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-09-07T06:30:35.6534386Z + detect_cuda_arch 2025-09-07T06:30:35.6534659Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-09-07T06:30:35.6535005Z + [[ linux-jammy-py3.13-clang12 == *s390x* ]] 2025-09-07T06:30:35.6535332Z + [[ 0 == \1 ]] 2025-09-07T06:30:35.6535556Z + [[ True == \1 ]] 2025-09-07T06:30:35.6535824Z + [[ linux-jammy-py3.13-clang12 != *bazel* ]] 2025-09-07T06:30:35.6536162Z ++ realpath build/custom_test_artifacts 2025-09-07T06:30:35.6556259Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-09-07T06:30:35.6556872Z + [[ -n '' ]] 2025-09-07T06:30:35.6557123Z + echo 'Environment variables' 2025-09-07T06:30:35.6557461Z Environment variables 2025-09-07T06:30:35.6557710Z + env 2025-09-07T06:30:35.6576796Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T06:30:35.6577837Z CONTINUE_THROUGH_ERROR=True 2025-09-07T06:30:35.6578405Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-09-07T06:30:35.6579373Z VLLM_TEST_HUGGING_FACE_TOKEN=*** 2025-09-07T06:30:35.6579916Z HOSTNAME=1383124d873a 2025-09-07T06:30:35.6581049Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6581826Z GITHUB_ACTION=__run_2 2025-09-07T06:30:35.6582116Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-09-07T06:30:35.6582687Z GITHUB_RUN_NUMBER=353985 2025-09-07T06:30:35.6583093Z TEST_CONFIG=dynamo_wrapped 2025-09-07T06:30:35.6583383Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-09-07T06:30:35.6583704Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-09-07T06:30:35.6584024Z SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:30:35.6584427Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-09-07T06:30:35.6584884Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-09-07T06:30:35.6585205Z GITHUB_REF_TYPE=branch 2025-09-07T06:30:35.6585557Z BASE_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:35.6585883Z XLA_CUDA= 2025-09-07T06:30:35.6586116Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-09-07T06:30:35.6586550Z HUGGING_FACE_HUB_TOKEN=*** 2025-09-07T06:30:35.6586866Z *** 2025-09-07T06:30:35.6587073Z GITHUB_REPOSITORY_ID=65600975 2025-09-07T06:30:35.6587362Z GITHUB_ACTIONS=true 2025-09-07T06:30:35.6587673Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:30:35.6588073Z SHA1=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:35.6588442Z GITHUB_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:35.6588965Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-09-07T06:30:35.6589443Z UCC_HOME=/usr 2025-09-07T06:30:35.6589682Z TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:30:35.6589954Z VERBOSE_TEST_LOGS=False 2025-09-07T06:30:35.6590220Z GITHUB_REF=refs/heads/main 2025-09-07T06:30:35.6590516Z SHARD_NUMBER=1 2025-09-07T06:30:35.6590758Z GITHUB_REF_PROTECTED=true 2025-09-07T06:30:35.6591015Z HOME=/var/lib/jenkins 2025-09-07T06:30:35.6591301Z GITHUB_API_URL=https://api.github.com 2025-09-07T06:30:35.6591643Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-09-07T06:30:35.6591945Z UCX_COMMIT= 2025-09-07T06:30:35.6592153Z USE_SYSTEM_NCCL=1 2025-09-07T06:30:35.6592394Z NUM_TEST_SHARDS=3 2025-09-07T06:30:35.6592627Z UCX_HOME=/usr 2025-09-07T06:30:35.6593219Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6594031Z JOB_NAME=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T06:30:35.6594837Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6595676Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-09-07T06:30:35.6596192Z GITHUB_EVENT_NAME=push 2025-09-07T06:30:35.6596459Z DASHBOARD_TAG= 2025-09-07T06:30:35.6596692Z GITHUB_RUN_ID=17524754568 2025-09-07T06:30:35.6597025Z INSTALLED_OPENBLAS= 2025-09-07T06:30:35.6597658Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6598369Z GITHUB_ACTOR=pytorchmergebot 2025-09-07T06:30:35.6598638Z PR_NUMBER= 2025-09-07T06:30:35.6598858Z DESIRED_CUDA= 2025-09-07T06:30:35.6599087Z GITHUB_RUN_ATTEMPT=1 2025-09-07T06:30:35.6599329Z VALGRIND=ON 2025-09-07T06:30:35.6599552Z ANACONDA_PYTHON_VERSION=3.13 2025-09-07T06:30:35.6613209Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-09-07T06:30:35.6613598Z TERM=vt100 2025-09-07T06:30:35.6613816Z INSTALLED_VISION=yes 2025-09-07T06:30:35.6614068Z BRANCH=main 2025-09-07T06:30:35.6614302Z SCCACHE_REGION=us-east-1 2025-09-07T06:30:35.6614573Z OPENSSL_ROOT_DIR=/opt/openssl 2025-09-07T06:30:35.6614870Z CUDA_PATH=/usr/local/cuda 2025-09-07T06:30:35.6615421Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-09-07T06:30:35.6616163Z GITHUB_SERVER_URL=https://github.com 2025-09-07T06:30:35.6616478Z UCC_COMMIT= 2025-09-07T06:30:35.6616686Z REENABLED_ISSUES= 2025-09-07T06:30:35.6616923Z DOCS= 2025-09-07T06:30:35.6617125Z SHLVL=1 2025-09-07T06:30:35.6617317Z MAX_JOBS=6 2025-09-07T06:30:35.6617542Z GITHUB_ACTOR_ID=97764156 2025-09-07T06:30:35.6617898Z GITHUB_WORKFLOW_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:30:35.6618295Z GITHUB_REF_NAME=main 2025-09-07T06:30:35.6618674Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-09-07T06:30:35.6619116Z GITHUB_JOB=test 2025-09-07T06:30:35.6619356Z NO_TEST_TIMEOUT=False 2025-09-07T06:30:35.6619613Z TD_DISTRIBUTED=False 2025-09-07T06:30:35.6619872Z GITHUB_REPOSITORY=pytorch/pytorch 2025-09-07T06:30:35.6620183Z GITHUB_RETENTION_DAYS=90 2025-09-07T06:30:35.6620456Z OPENSSL_DIR=/opt/openssl 2025-09-07T06:30:35.6620734Z GITHUB_ACTION_REPOSITORY= 2025-09-07T06:30:35.6621603Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-09-07T06:30:35.6622435Z GITHUB_BASE_REF= 2025-09-07T06:30:35.6622669Z INSTALLED_ACL= 2025-09-07T06:30:35.6623050Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-linux.2xlarge_49774041707 2025-09-07T06:30:35.6623474Z CI=true 2025-09-07T06:30:35.6623701Z GITHUB_REPOSITORY_OWNER=pytorch 2025-09-07T06:30:35.6624034Z RUST_LOG=sccache::server=error 2025-09-07T06:30:35.6624322Z JOB_ID=49774041707 2025-09-07T06:30:35.6624543Z GITHUB_HEAD_REF= 2025-09-07T06:30:35.6624780Z GITHUB_ACTION_REF= 2025-09-07T06:30:35.6625081Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-09-07T06:30:35.6625449Z TEST_SHOWLOCALS=False 2025-09-07T06:30:35.6625697Z GITHUB_WORKFLOW=pull 2025-09-07T06:30:35.6625968Z DEBIAN_FRONTEND=noninteractive 2025-09-07T06:30:35.6626617Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_dce10492-77fc-4a2b-8455-025f7ac69424 2025-09-07T06:30:35.6627284Z NO_TD=False 2025-09-07T06:30:35.6627509Z SKIP_SCCACHE_INITIALIZATION=1 2025-09-07T06:30:35.6627829Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-09-07T06:30:35.6628149Z _=/usr/bin/env 2025-09-07T06:30:35.6628384Z + echo 'Testing pytorch' 2025-09-07T06:30:35.6628634Z Testing pytorch 2025-09-07T06:30:35.6628878Z + export LANG=C.UTF-8 2025-09-07T06:30:35.6629125Z + LANG=C.UTF-8 2025-09-07T06:30:35.6647902Z + PR_NUMBER= 2025-09-07T06:30:35.6648560Z + [[ dynamo_wrapped == \d\e\f\a\u\l\t ]] 2025-09-07T06:30:35.6649161Z + [[ dynamo_wrapped == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-09-07T06:30:35.6649768Z + [[ dynamo_wrapped == \s\l\o\w ]] 2025-09-07T06:30:35.6650395Z + [[ linux-jammy-py3.13-clang12 == *slow-gradcheck* ]] 2025-09-07T06:30:35.6651129Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-09-07T06:30:35.6651766Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-09-07T06:30:35.6652415Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-09-07T06:30:35.6652896Z + [[ dynamo_wrapped == *crossref* ]] 2025-09-07T06:30:35.6653231Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-09-07T06:30:35.6653568Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-09-07T06:30:35.6653925Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-09-07T06:30:35.6654273Z + pip_install ninja==1.10.2 2025-09-07T06:30:35.6654651Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-09-07T06:30:35.6655121Z + python3 -m pip install --progress-bar off ninja==1.10.2 2025-09-07T06:30:36.1187456Z Collecting ninja==1.10.2 2025-09-07T06:30:36.1358120Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-09-07T06:30:36.1469588Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-09-07T06:30:36.3193811Z Installing collected packages: ninja 2025-09-07T06:30:36.3194530Z Attempting uninstall: ninja 2025-09-07T06:30:36.3227722Z Found existing installation: ninja 1.11.1.3 2025-09-07T06:30:36.3248978Z Uninstalling ninja-1.11.1.3: 2025-09-07T06:30:36.3305975Z Successfully uninstalled ninja-1.11.1.3 2025-09-07T06:30:36.3543116Z Successfully installed ninja-1.10.2 2025-09-07T06:30:36.4172631Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-09-07T06:30:36.4174470Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-09-07T06:30:36.4175510Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-09-07T06:30:36.4176048Z + [[ linux-jammy-py3.13-clang12 == *asan* ]] 2025-09-07T06:30:36.4176545Z + [[ linux-jammy-py3.13-clang12 == *-debug* ]] 2025-09-07T06:30:36.4177004Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-09-07T06:30:36.4177927Z + echo 'We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass' 2025-09-07T06:30:36.4178702Z We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass 2025-09-07T06:30:36.4179249Z + cd test 2025-09-07T06:30:36.4179593Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-09-07T06:30:37.8172945Z + [[ dynamo_wrapped == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-09-07T06:30:37.8173609Z + [[ dynamo_wrapped == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-09-07T06:30:37.8174041Z + [[ dynamo_wrapped == \l\e\g\a\c\y\_\n\v\i\d\i\a\_\d\r\i\v\e\r ]] 2025-09-07T06:30:37.8177812Z + DYNAMO_BENCHMARK_FLAGS=() 2025-09-07T06:30:37.8178665Z + [[ dynamo_wrapped == *pr_time_benchmarks* ]] 2025-09-07T06:30:37.8179127Z + [[ dynamo_wrapped == *dynamo_eager* ]] 2025-09-07T06:30:37.8179472Z + [[ dynamo_wrapped == *aot_eager* ]] 2025-09-07T06:30:37.8179818Z + [[ dynamo_wrapped == *aot_inductor* ]] 2025-09-07T06:30:37.8180182Z + [[ dynamo_wrapped == *max_autotune_inductor* ]] 2025-09-07T06:30:37.8180545Z + [[ dynamo_wrapped == *inductor* ]] 2025-09-07T06:30:37.8180878Z + [[ dynamo_wrapped == *dynamic* ]] 2025-09-07T06:30:37.8181192Z + [[ dynamo_wrapped == *cpu* ]] 2025-09-07T06:30:37.8181520Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-09-07T06:30:37.8212163Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-09-07T06:30:37.8212822Z + [[ linux-jammy-py3.13-clang12 == *-bazel-* ]] 2025-09-07T06:30:37.8215332Z + cd test 2025-09-07T06:30:37.8216205Z + python -c 'import torch; print(torch.__config__.show())' 2025-09-07T06:30:38.9152377Z PyTorch built with: 2025-09-07T06:30:38.9152697Z - GCC 4.2 2025-09-07T06:30:38.9152928Z - C++ Version: 201703 2025-09-07T06:30:38.9153193Z - clang 12.0.1 2025-09-07T06:30:38.9153740Z - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-09-07T06:30:38.9154511Z - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-09-07T06:30:38.9154951Z - OpenMP 201811 2025-09-07T06:30:38.9155267Z - LAPACK is enabled (usually provided by MKL) 2025-09-07T06:30:38.9155625Z - NNPACK is enabled 2025-09-07T06:30:38.9155884Z - CPU capability usage: AVX512 2025-09-07T06:30:38.9161307Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=93fb23d6fae7c4e82c4239a1033e522088742634, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wsuggest-override -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Qunused-arguments -faligned-new -Werror -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.9.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, 2025-09-07T06:30:38.9167318Z 2025-09-07T06:30:39.1985550Z + cd test 2025-09-07T06:30:39.1985982Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-09-07T06:30:40.2873066Z ATen/Parallel: 2025-09-07T06:30:40.2873728Z at::get_num_threads() : 4 2025-09-07T06:30:40.2874189Z at::get_num_interop_threads() : 4 2025-09-07T06:30:40.2874618Z OpenMP 201811 2025-09-07T06:30:40.2874938Z omp_get_max_threads() : 4 2025-09-07T06:30:40.2875773Z Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-09-07T06:30:40.2877062Z mkl_get_max_threads() : 4 2025-09-07T06:30:40.2877653Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-09-07T06:30:40.2878300Z std::thread::hardware_concurrency() : 8 2025-09-07T06:30:40.2878764Z Environment variables: 2025-09-07T06:30:40.2879171Z OMP_NUM_THREADS : [not set] 2025-09-07T06:30:40.2879550Z MKL_NUM_THREADS : [not set] 2025-09-07T06:30:40.2879955Z ATen parallel backend: OpenMP 2025-09-07T06:30:40.2880221Z 2025-09-07T06:30:40.5370874Z + [[ dynamo_wrapped == *numpy_2* ]] 2025-09-07T06:30:40.5371549Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-09-07T06:30:40.5371986Z + [[ dynamo_wrapped == *backward* ]] 2025-09-07T06:30:40.5372313Z + [[ dynamo_wrapped == *xla* ]] 2025-09-07T06:30:40.5372613Z + [[ dynamo_wrapped == *vllm* ]] 2025-09-07T06:30:40.5372925Z + [[ dynamo_wrapped == *executorch* ]] 2025-09-07T06:30:40.5373392Z + [[ dynamo_wrapped == \j\i\t\_\l\e\g\a\c\y ]] 2025-09-07T06:30:40.5373769Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-09-07T06:30:40.5374162Z + [[ dynamo_wrapped == distributed ]] 2025-09-07T06:30:40.5374529Z + [[ dynamo_wrapped == *operator_benchmark* ]] 2025-09-07T06:30:40.5374895Z + [[ dynamo_wrapped == *inductor_distributed* ]] 2025-09-07T06:30:40.5375246Z + [[ dynamo_wrapped == *inductor-halide* ]] 2025-09-07T06:30:40.5375608Z + [[ dynamo_wrapped == *inductor-triton-cpu* ]] 2025-09-07T06:30:40.5376047Z + [[ dynamo_wrapped == *inductor-micro-benchmark* ]] 2025-09-07T06:30:40.5376517Z + [[ dynamo_wrapped == *huggingface* ]] 2025-09-07T06:30:40.5376878Z + [[ dynamo_wrapped == *timm* ]] 2025-09-07T06:30:40.5377180Z + [[ dynamo_wrapped == cachebench ]] 2025-09-07T06:30:40.5377557Z + [[ dynamo_wrapped == verify_cachebench ]] 2025-09-07T06:30:40.5377896Z + [[ dynamo_wrapped == *torchbench* ]] 2025-09-07T06:30:40.5378233Z + [[ dynamo_wrapped == *inductor_cpp_wrapper* ]] 2025-09-07T06:30:40.5378580Z + [[ dynamo_wrapped == *inductor* ]] 2025-09-07T06:30:40.5378893Z + [[ dynamo_wrapped == *einops* ]] 2025-09-07T06:30:40.5379215Z + [[ dynamo_wrapped == *dynamo_wrapped* ]] 2025-09-07T06:30:40.5379534Z + install_torchvision 2025-09-07T06:30:40.5379794Z + local orig_preload 2025-09-07T06:30:40.5380036Z + local commit 2025-09-07T06:30:40.5380277Z ++ get_pinned_commit vision 2025-09-07T06:30:40.5380563Z ++ cat .github/ci_commit_pins/vision.txt 2025-09-07T06:30:40.5409131Z + commit=966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:40.5409675Z + orig_preload= 2025-09-07T06:30:40.5409994Z + '[' -n '' ']' 2025-09-07T06:30:40.5410387Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-09-07T06:30:40.5411240Z + pip_build_and_install git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 dist/vision 2025-09-07T06:30:40.5412202Z + local build_target=git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:40.5412794Z + local wheel_dir=dist/vision 2025-09-07T06:30:40.5413073Z + local found_whl=0 2025-09-07T06:30:40.5413319Z + for file in "${wheel_dir}"/*.whl 2025-09-07T06:30:40.5413642Z + [[ -f dist/vision/*.whl ]] 2025-09-07T06:30:40.5414161Z + '[' 0 == 0 ']' 2025-09-07T06:30:40.5414935Z + python3 -m pip wheel --no-build-isolation --no-deps --no-use-pep517 -w dist/vision git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:40.8495908Z Collecting git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:40.8501026Z Cloning https://github.com/pytorch/vision.git (to revision 966da7e46f65d6d49df3e31214470a4fe5cc8e66) to /tmp/pip-req-build-xi8j5vga 2025-09-07T06:30:40.8683765Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-xi8j5vga 2025-09-07T06:30:42.6700978Z Running command git rev-parse -q --verify 'sha^966da7e46f65d6d49df3e31214470a4fe5cc8e66' 2025-09-07T06:30:42.6721808Z Running command git fetch -q https://github.com/pytorch/vision.git 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:42.7945498Z Running command git checkout -q 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:43.1149536Z Resolved https://github.com/pytorch/vision.git to commit 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:44.7230781Z Preparing metadata (setup.py) ... [?25l- \ done 2025-09-07T06:30:44.7273681Z [?25hBuilding wheels for collected packages: torchvision 2025-09-07T06:30:44.7323483Z  DEPRECATION: Building 'torchvision' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torchvision'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T06:32:19.4735545Z  Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done 2025-09-07T06:32:19.4762964Z [?25h Created wheel for torchvision: filename=torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl size=1199434 sha256=f3deeff5a548df8183eca0db68c93a4951a534426b7dbc1c81b066b9f9676d6e 2025-09-07T06:32:19.4764419Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/89/53/07/16d05bff0990e585bfa232438657da6e5e4f6d0cea71bd3e09 2025-09-07T06:32:19.4801131Z Successfully built torchvision 2025-09-07T06:32:19.5648727Z + for file in "${wheel_dir}"/*.whl 2025-09-07T06:32:19.5649328Z + pip_install_whl dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-09-07T06:32:19.5650079Z + args=('dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl') 2025-09-07T06:32:19.5650578Z + local args 2025-09-07T06:32:19.5650997Z + [[ dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl == *\ * ]] 2025-09-07T06:32:19.5651531Z + for path in "${args[@]}" 2025-09-07T06:32:19.5652015Z + echo 'Installing dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl' 2025-09-07T06:32:19.5652730Z Installing dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-09-07T06:32:19.5653547Z + python3 -mpip install --no-index --no-deps dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-09-07T06:32:19.9083135Z Processing ./dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-09-07T06:32:19.9180729Z Installing collected packages: torchvision 2025-09-07T06:32:20.3757686Z Successfully installed torchvision-0.22.0a0+966da7e 2025-09-07T06:32:20.4218784Z + '[' -n '' ']' 2025-09-07T06:32:20.4219108Z + test_dynamo_wrapped_shard 1 2025-09-07T06:32:20.4219408Z + [[ -z 3 ]] 2025-09-07T06:32:20.4219650Z + python tools/dynamo/verify_dynamo.py 2025-09-07T06:32:21.5370004Z Python version: 3.13.7 2025-09-07T06:32:21.5370358Z `torch` version: 2.9.0a0+git93fb23d 2025-09-07T06:32:21.5370696Z CUDA version: None 2025-09-07T06:32:21.5371275Z ROCM version: None 2025-09-07T06:32:21.5371417Z 2025-09-07T06:32:21.5371954Z /var/lib/jenkins/workspace/tools/dynamo/verify_dynamo.py:220: UserWarning: Dynamo not yet supported in Python 3.13. Skipping check. 2025-09-07T06:32:21.5372834Z warnings.warn("Dynamo not yet supported in Python 3.13. Skipping check.") 2025-09-07T06:32:21.5373520Z All required checks passed 2025-09-07T06:32:21.7784474Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --exclude-aot-dispatch-tests --shard 1 3 --verbose --upload-artifacts-while-running 2025-09-07T06:32:24.3857012Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T06:32:24.3860306Z import pkg_resources 2025-09-07T06:32:25.9386378Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-09-07T06:32:25.9933597Z Ignoring disabled issues: [''] 2025-09-07T06:32:26.0075336Z Found test times from artifacts 2025-09-07T06:32:26.0633265Z Found test times from artifacts 2025-09-07T06:32:26.0651697Z Running all tests 2025-09-07T06:32:26.0813691Z Running parallel tests on 3 processes 2025-09-07T06:32:26.0818908Z Name: tests to run (est. time: 117.75min) 2025-09-07T06:32:26.0819628Z Serial tests (40): 2025-09-07T06:32:26.0820093Z test_reductions 1/2 2025-09-07T06:32:26.0820575Z test_ci_sanity_check_fail 1/1 2025-09-07T06:32:26.0821118Z test_openreg 1/1 2025-09-07T06:32:26.0821599Z test_tensor_creation_ops 1/1 2025-09-07T06:32:26.0822132Z test_nn 2/4 2025-09-07T06:32:26.0822529Z test_nn 4/4 2025-09-07T06:32:26.0822931Z test_fx 1/1 2025-09-07T06:32:26.0823416Z test_transformers_privateuse1 1/1 2025-09-07T06:32:26.0824032Z test_show_pickle 1/1 2025-09-07T06:32:26.0824498Z test_utils 1/1 2025-09-07T06:32:26.0824945Z test_tensorexpr 1/1 2025-09-07T06:32:26.0825442Z test_multiprocessing 1/1 2025-09-07T06:32:26.0825943Z test_dispatch 1/1 2025-09-07T06:32:26.0826429Z test_namedtuple_return_api 1/1 2025-09-07T06:32:26.0826997Z test_jit_disabled 1/1 2025-09-07T06:32:26.0827491Z test_fake_tensor 1/1 2025-09-07T06:32:26.0827957Z test_cuda_trace 1/1 2025-09-07T06:32:26.0828352Z test_cuda_nvml_based_avail 1/1 2025-09-07T06:32:26.0828885Z test_autograd_fallback 1/1 2025-09-07T06:32:26.0829424Z dynamo/test_fake_distributed 1/1 2025-09-07T06:32:26.0829978Z test_autocast 1/1 2025-09-07T06:32:26.0830403Z test_torch 1/2 2025-09-07T06:32:26.0830804Z test_torch 2/2 2025-09-07T06:32:26.0831227Z test_sort_and_select 1/1 2025-09-07T06:32:26.0831719Z test_native_mha 1/1 2025-09-07T06:32:26.0832194Z test_cuda_primary_ctx 1/1 2025-09-07T06:32:26.0832691Z nn/test_pooling 1/1 2025-09-07T06:32:26.0833173Z test_multiprocessing_spawn 1/1 2025-09-07T06:32:26.0833717Z nn/test_convolution 1/2 2025-09-07T06:32:26.0834158Z nn/test_convolution 2/2 2025-09-07T06:32:26.0834563Z test_mobile_optimizer 1/1 2025-09-07T06:32:26.0835045Z test_spectral_ops 1/1 2025-09-07T06:32:26.0835566Z distributions/test_distributions 1/3 2025-09-07T06:32:26.0836162Z distributions/test_distributions 2/3 2025-09-07T06:32:26.0836667Z distributions/test_distributions 3/3 2025-09-07T06:32:26.0837138Z doctests 1/1 2025-09-07T06:32:26.0837520Z test_autoload_disable 1/1 2025-09-07T06:32:26.0837969Z test_autoload_enable 1/1 2025-09-07T06:32:26.0838405Z test_cpp_extensions_aot_ninja 1/1 2025-09-07T06:32:26.0838924Z test_cpp_extensions_aot_no_ninja 1/1 2025-09-07T06:32:26.0839433Z Parallel tests (36): 2025-09-07T06:32:26.0839826Z dynamo/test_functions 1/1 2025-09-07T06:32:26.0840294Z dynamo/test_repros 1/1 2025-09-07T06:32:26.0841066Z dynamo/test_aot_autograd_cache 1/1 2025-09-07T06:32:26.0841628Z dynamo/test_subclasses 1/1 2025-09-07T06:32:26.0842103Z dynamo/test_skip_guard_eval_unsafe 1/1 2025-09-07T06:32:26.0842509Z dynamo/test_nops 1/1 2025-09-07T06:32:26.0842801Z test_appending_byte_serializer 1/1 2025-09-07T06:32:26.0843131Z dynamo/test_inline_and_install 1/1 2025-09-07T06:32:26.0843444Z dynamo/test_dicts 1/1 2025-09-07T06:32:26.0843698Z xpu/test_fusion 1/1 2025-09-07T06:32:26.0843974Z dynamo/test_nested_graph_breaks 1/1 2025-09-07T06:32:26.0844474Z dynamo/test_subgraphs 1/1 2025-09-07T06:32:26.0844907Z dynamo/test_config 1/1 2025-09-07T06:32:26.0845298Z dynamo/test_install_free_tensors 1/1 2025-09-07T06:32:26.0845647Z dynamo/test_export 1/1 2025-09-07T06:32:26.0845914Z xpu/test_gemm 1/1 2025-09-07T06:32:26.0846186Z dynamo/test_guard_serialization 1/1 2025-09-07T06:32:26.0846491Z dynamo/test_misc 1/1 2025-09-07T06:32:26.0846947Z dynamo/test_export_mutations 1/1 2025-09-07T06:32:26.0847264Z test_jiterator 1/1 2025-09-07T06:32:26.0847528Z dynamo/test_profiler 1/1 2025-09-07T06:32:26.0847801Z dynamo/test_base_hop 1/1 2025-09-07T06:32:26.0848099Z dynamo/test_python_dispatcher 1/1 2025-09-07T06:32:26.0848423Z dynamo/test_higher_order_ops 1/1 2025-09-07T06:32:26.0848735Z dynamo/test_debug_utils 1/1 2025-09-07T06:32:26.0849025Z dynamo/test_graph_deduplication 1/1 2025-09-07T06:32:26.0849355Z dynamo/test_decorators 1/1 2025-09-07T06:32:26.0849650Z dynamo/test_aot_compile 1/1 2025-09-07T06:32:26.0849946Z dynamo/test_reorder_logs 1/1 2025-09-07T06:32:26.0850221Z dynamo/test_exc 1/1 2025-09-07T06:32:26.0850483Z dynamo/test_minifier 1/1 2025-09-07T06:32:26.0850768Z dynamo/test_guard_manager 1/1 2025-09-07T06:32:26.0851076Z dynamo/test_bytecode_utils 1/1 2025-09-07T06:32:26.0851368Z dynamo/test_generator 1/1 2025-09-07T06:32:26.0851668Z test_unary_ufuncs 3/3 2025-09-07T06:32:26.0851943Z test_cuda_multigpu 1/1 2025-09-07T06:32:26.0852218Z Name: excluded (est. time: 0.0min) 2025-09-07T06:32:26.0852516Z Serial tests (0): 2025-09-07T06:32:26.0852765Z Parallel tests (0): 2025-09-07T06:32:26.0853117Z Running test_reductions 1/2 ... [2025-09-07 06:32:26.083415] 2025-09-07T06:32:26.0853507Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:32:26.0854561Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:32:26.083855] 2025-09-07T06:40:33.7445927Z 2025-09-07T06:40:33.7447761Z test_reductions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.2_50cd90362b1ebb58_.log 2025-09-07T06:40:33.8999704Z Running 2401 items in this shard: test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_all_any_with_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float64, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float64, 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_uint8, 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_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_uint8, 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_complex64, 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_complex64, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32, 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_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_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_float64, 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_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, 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_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_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_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, 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_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 2025-09-07T06:40:33.9884075Z 2025-09-07T06:40:34.2602012Z Uploading artifacts took 0.51 seconds 2025-09-07T06:40:34.2605506Z Running test_ci_sanity_check_fail 1/1 ... [2025-09-07 06:40:34.260382] 2025-09-07T06:40:34.2606160Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:40:34.2609687Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:40:34.260758] 2025-09-07T06:40:47.1777226Z Running test_openreg 1/1 ... [2025-09-07 06:40:47.177351] 2025-09-07T06:40:47.4875605Z Processing /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg 2025-09-07T06:40:47.7763795Z Preparing metadata (pyproject.toml) ... [?25l- done 2025-09-07T06:40:47.7792358Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch_openreg==0.0.1) (2.9.0a0+git93fb23d) 2025-09-07T06:40:47.7804012Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (3.19.1) 2025-09-07T06:40:47.7808988Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (4.15.0) 2025-09-07T06:40:47.7814656Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (80.9.0) 2025-09-07T06:40:47.7819089Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (1.13.3) 2025-09-07T06:40:47.7824009Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (2.8.8) 2025-09-07T06:40:47.7829256Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (3.1.6) 2025-09-07T06:40:47.7833867Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (2025.7.0) 2025-09-07T06:40:47.7942652Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch->torch_openreg==0.0.1) (1.3.0) 2025-09-07T06:40:47.7971879Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torch_openreg==0.0.1) (3.0.2) 2025-09-07T06:40:47.8035195Z Building wheels for collected packages: torch_openreg 2025-09-07T06:40:53.0312164Z Building wheel for torch_openreg (pyproject.toml) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | done 2025-09-07T06:40:53.0326462Z [?25h Created wheel for torch_openreg: filename=torch_openreg-0.0.1-cp313-cp313-linux_x86_64.whl size=277882 sha256=02244a65c04b4a275c39ab92b34262dfceaa5597455d98f7396b72776967b48f 2025-09-07T06:40:53.0327880Z Stored in directory: /tmp/pip-ephem-wheel-cache-uur9hq1v/wheels/bc/4f/31/9af65770c0a69187e95f1d791df9c71156b2b3f469bce9d735 2025-09-07T06:40:53.0352400Z Successfully built torch_openreg 2025-09-07T06:40:53.1781168Z Installing collected packages: torch_openreg 2025-09-07T06:40:53.1965090Z Successfully installed torch_openreg-0.0.1 2025-09-07T06:40:53.2538353Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:40:53.2541807Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_openreg.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:40:53.253935] 2025-09-07T06:41:05.6344756Z 2025-09-07T06:41:05.6345598Z test_openreg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openreg_1.1_73027f1aeff29825_.log 2025-09-07T06:41:05.6358704Z Running 44 items in this shard: test/test_openreg.py::TestPrivateUse1::test_backend_dispatchstub, test/test_openreg.py::TestPrivateUse1::test_backend_generate_methods, test/test_openreg.py::TestPrivateUse1::test_backend_module_function, test/test_openreg.py::TestPrivateUse1::test_backend_module_methods, test/test_openreg.py::TestPrivateUse1::test_backend_module_registration, test/test_openreg.py::TestPrivateUse1::test_backend_name, test/test_openreg.py::TestPrivateUse1::test_backend_operator_registration, test/test_openreg.py::TestPrivateUse1::test_backend_packed_sequence_methods, test/test_openreg.py::TestPrivateUse1::test_backend_storage_methods, test/test_openreg.py::TestPrivateUse1::test_backend_tensor_methods, test/test_openreg.py::TestPrivateUse1::test_backend_tensor_type, test/test_openreg.py::TestPrivateUse1::test_backend_type_methods, test/test_openreg.py::TestOpenReg::test_autograd_init, test/test_openreg.py::TestOpenReg::test_compile_autograd_function_aliasing, test/test_openreg.py::TestOpenReg::test_compile_autograd_function_returns_self, test/test_openreg.py::TestOpenReg::test_copy_same_device, test/test_openreg.py::TestOpenReg::test_cross_device_copy, test/test_openreg.py::TestOpenReg::test_cross_diff_devices_copy, test/test_openreg.py::TestOpenReg::test_data_dependent_output, test/test_openreg.py::TestOpenReg::test_event_elapsed_time, test/test_openreg.py::TestOpenReg::test_event_wait_stream, test/test_openreg.py::TestOpenReg::test_expand, test/test_openreg.py::TestOpenReg::test_factory, test/test_openreg.py::TestOpenReg::test_fake_tensor, test/test_openreg.py::TestOpenReg::test_generator, test/test_openreg.py::TestOpenReg::test_manual_seed, test/test_openreg.py::TestOpenReg::test_named_tensor, test/test_openreg.py::TestOpenReg::test_open_device_cpu_serialization, test/test_openreg.py::TestOpenReg::test_open_device_dlpack, test/test_openreg.py::TestOpenReg::test_open_device_numpy_serialization, test/test_openreg.py::TestOpenReg::test_pin_memory, test/test_openreg.py::TestOpenReg::test_printing, test/test_openreg.py::TestOpenReg::test_quantize, test/test_openreg.py::TestOpenReg::test_record_event, test/test_openreg.py::TestOpenReg::test_resize, test/test_openreg.py::TestOpenReg::test_rewrapped_storage, test/test_openreg.py::TestOpenReg::test_rng_state, test/test_openreg.py::TestOpenReg::test_scalar_type_fallback, test/test_openreg.py::TestOpenReg::test_serialization, test/test_openreg.py::TestOpenReg::test_stream_synchronize, test/test_openreg.py::TestOpenReg::test_stream_wait_event, test/test_openreg.py::TestOpenReg::test_stream_wait_stream, test/test_openreg.py::TestOpenReg::test_tensor_type_fallback, test/test_openreg.py::TestOpenReg::test_tensorlist_type_fallback 2025-09-07T06:41:05.6370208Z 2025-09-07T06:41:05.6370422Z Running test_tensor_creation_ops 1/1 ... [2025-09-07 06:41:05.634767] 2025-09-07T06:41:05.6370876Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:41:05.6371952Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensor_creation_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:41:05.635096] 2025-09-07T06:49:23.9160110Z 2025-09-07T06:49:23.9161370Z test_tensor_creation_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensor_creation_ops_1.1_b62828f47917d1f1_.log 2025-09-07T06:49:23.9426950Z Running 646 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_strided_neg_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_scipy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cartesian_prod_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_big_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_in_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_misaligned_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_multi_batch_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_memory_format_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_preserve_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_size1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_trailing_dim_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_combinations_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_complex_type_conversions_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_concat_empty_list_error_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_device_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_dtypes_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ctor_with_numpy_array_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diag_embed_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diagflat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_full_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_overflow_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_tensor_props_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_eye_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_fill_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_False_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_True_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linlogspace_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_default_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_is_default_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_dtype_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_non_1d_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_unsupported_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_vs_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_warns_if_no_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_xy_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_methods_requires_grad_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_offset_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ones_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_factories_64bit_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_warning_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_repeat_interleave_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_roll_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_simple_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_storage_filename_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_strided_mismatched_stride_shape_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_ctor_device_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factories_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_copy_var_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_non_writable_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_sequence_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_bounds_checking_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_out_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_std_error_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_distribution_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_inference_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_random_neg_values_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_device_compatibility_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_large_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_empty_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_full_like_inference_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_byte_to_int_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_astensor_consistency_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_default_device_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_device_without_index_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_numpy_scalars_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_mult_devices_cpu_float32 2025-09-07T06:49:23.9663707Z 2025-09-07T06:49:23.9663925Z Running test_nn 2/4 ... [2025-09-07 06:49:23.917534] 2025-09-07T06:49:23.9664483Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:49:23.9665497Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:49:23.917879] 2025-09-07T06:58:59.2100163Z 2025-09-07T06:58:59.2100978Z test_nn 2/4 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_2.4_681c25a0e36952f5_.log 2025-09-07T06:58:59.2431924Z Running 573 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_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, 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_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar, 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_groups, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad2, test/test_nn.py::TestNN::test_Conv1d_pad_same, 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_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda, 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_pad_same_dilated, test/test_nn.py::TestNN::test_Conv2d_pad_valid, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, 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_1x1x1_no_bias, 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_groups, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated, 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_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_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_none_cuda_double, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, 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_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_Hardsigmoid_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_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_sum_cuda_float, 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_sum_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, 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_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_half, 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_cuda, test/test_nn.py::TestNN::test_Linear_no_batch_dim, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_float, 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_MarginRankingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_ModuleDict, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_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_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_MultiMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index, 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_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_float, 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_cuda, 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_PoissonNLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_RNN_change_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_ReLU6_no_batch_dim, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d, 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_insert_fail_case, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_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_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_Softplus_no_batch_dim, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Threshold_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim, test/test_nn.py::TestNN::test_Unfold_int_input, test/test_nn.py::TestNN::test_adaptive_log_softmax, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_half_overflow, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_SyncBatchNorm, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_weight_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_loss_broadcasts_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_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_del, test/test_nn.py::TestNN::test_channel_shuffle_input_checks, test/test_nn.py::TestNN::test_children, 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_cudnn_forward_exception, test/test_nn.py::TestNN::test_dir, test/test_nn.py::TestNN::test_dir_digit, 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_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_zero_dim_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_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_cuda, test/test_nn.py::TestNN::test_interpolate_buffer_overflow, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d, test/test_nn.py::TestNN::test_interpolate_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_tuple_2d_cuda, 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_cuda, test/test_nn.py::TestNN::test_kl_div_with_diff_type_log_target, test/test_nn.py::TestNN::test_layer_norm_backwards_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_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_scalar_cuda, 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_native_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_normalize, test/test_nn.py::TestNN::test_parameterlistdict_pickle, test/test_nn.py::TestNN::test_parameters_to_vector, test/test_nn.py::TestNN::test_parse_to, test/test_nn.py::TestNN::test_pdist_cuda_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_zeros, 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_rnn_args_check, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, 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_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_transformer_layer_args_check, test/test_nn.py::TestNN::test_triplet_margin_loss, test/test_nn.py::TestNN::test_upsamplingLinear1d, 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::TestFusionUtils::test_fuse_conv_bn_requires_grad, 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_GRU_grad_and_gradgrad_cpu_float64, 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_ReflectionPad2d_large_deterministic_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_fails_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_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_simple_average_cpu_float32, 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_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_consistent_index_target_and_probs_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_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cuda_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_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_nan_inf_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float16, 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_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_InstanceNorm3d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_weight_bias_cpu, 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_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_grad_cpu, 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_nll_loss_empty_tensor_reduction_mean_cpu_float32, 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_rmsnorm_numeric_cpu_bfloat16, 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_softmax_backward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_without_fully_vectorized_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_low_threshold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_inplace_overlap_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_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_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_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_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_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_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_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_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_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_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_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_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_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_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_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_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_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_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_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_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_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_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_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_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_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_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_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_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_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_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_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_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_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_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_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_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_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_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_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_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_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_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_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_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_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_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_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_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_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_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_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_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_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_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_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_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_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_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_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_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_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_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_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_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_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_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_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_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_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_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_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_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_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_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_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_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_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_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_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_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_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_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_float64_cpu_float64, 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_bilinear_float64_cpu_float64, 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_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_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_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_bilinear_float32_cpu_float32, 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_nearest-exact_int16_cpu_int16, 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_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest-exact_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_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_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_bfloat16 2025-09-07T06:58:59.2752205Z 2025-09-07T06:58:59.2752375Z Running test_nn 4/4 ... [2025-09-07 06:58:59.211286] 2025-09-07T06:58:59.2752766Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:58:59.2753789Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:58:59.211754] 2025-09-07T07:05:23.0362737Z 2025-09-07T07:05:23.0364041Z test_nn 4/4 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_4.4_377f0cba73e627bc_.log 2025-09-07T07:05:23.0669916Z Running 581 items in this shard: test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_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_none, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_CTCLoss_critical_target_len, 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_pad2size1, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda, 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_Conv2d, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, 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_cuda, 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_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_Conv3d, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_dilated_strided, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_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_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, 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_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_ELU_no_batch_dim, 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_Flatten_no_batch_dim, test/test_nn.py::TestNN::test_Flatten_no_batch_dim_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_Hardswish_no_batch_dim_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_none_cuda_half, 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_no_batch_dim_mean_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_none_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum, 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_L1Loss_no_batch_dim_sum_cuda_float, 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_Linear, 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_half, 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_cuda_double, test/test_nn.py::TestNN::test_MaxUnpool1d_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_MultiLabelMarginLoss_0d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half, 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_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce, 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_weights_cuda, 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_half, 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_no_batch_dim, 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_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_RNN_cell, 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_RReLU_with_up_down_scalar_cuda, test/test_nn.py::TestNN::test_ReLU_no_batch_dim, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, 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_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_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_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_reduce, 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_Tanhshrink_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim, 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_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_sum_cuda_half, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Unfold_int_input_cuda, 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_2D_inference_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_float32, 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_bce_loss_always_nonnegative, 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_bilinear_broadcasting, test/test_nn.py::TestNN::test_bilinear_value_error, 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_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_container_copy, test/test_nn.py::TestNN::test_convert_sync_batchnorm, test/test_nn.py::TestNN::test_cross_entropy_loss_zero_div, test/test_nn.py::TestNN::test_cudnn_weight_format, test/test_nn.py::TestNN::test_elu_inplace_gradgrad, 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_interpolate_bicubic_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d, 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_tuple_2d_align_corners, 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_scale_1d, 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_3d, 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_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_layer_norm_eps, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightStrided, 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_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_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_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_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_parameters_and_named_parameters, 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_empty_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_softmax_functional_dim3_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_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_transformerdecoder, test/test_nn.py::TestNN::test_transformerdecoderlayer, 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_upsamplingTrilinear3d_spatial_invariance, test/test_nn.py::TestNN::test_vector_to_parameters, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestNNDeviceTypeCPU::test_BatchNorm_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_Bilinear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_False_cpu, 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_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_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_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_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_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_multi_device_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float32, 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_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_no_batch_dim_reduction_none_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_error_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_fold_cpu, 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_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float32, 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_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_layernorm_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_TxT_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_devices_parity_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_module_to_empty_non_recursive_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mse_loss_error_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_all_ignored_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_none_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_sum_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_target_dim_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_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_rmsnorm_epsilon_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_epsilon_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_epsilon_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float32, 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_smem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_output_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_softshrink_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_negative_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_True_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_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_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_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_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_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_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_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_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_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_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_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_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_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_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_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_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_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_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_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_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_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_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_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_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_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_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_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_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_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_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_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_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_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_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_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_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_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_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_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_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_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_bilinear_uint8_cpu_uint8, 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_int8_cpu_int8, 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_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_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_5_mode_bicubic_float64_cpu_float64, 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_float64_cpu_float64, 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_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_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_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_launch_config_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_launch_config_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_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_rescale_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format0_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 2025-09-07T07:05:23.0975063Z 2025-09-07T07:05:23.3873429Z Uploading artifacts took 0.35 seconds 2025-09-07T07:05:23.3876173Z Running test_fx 1/1 ... [2025-09-07 07:05:23.387423] 2025-09-07T07:05:23.3876556Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:05:23.3879794Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:05:23.387756] 2025-09-07T07:10:02.3689768Z 2025-09-07T07:10:02.3690907Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_cf20edb00898fd7b_.log 2025-09-07T07:10:02.4222996Z Running 1261 items in this shard: test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationInput_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationMetadata_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationTorchTensorCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_ReturnList_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_FactoryFunctionCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_MutationFactory_cpu, test/test_fx.py::TestCSEPass::test_banned_list, test/test_fx.py::TestCSEPass::test_empty, test/test_fx.py::TestCSEPass::test_immutable_list_multiple_entries, test/test_fx.py::TestCSEPass::test_immutable_list_type, test/test_fx.py::TestCSEPass::test_kwarg, test/test_fx.py::TestCSEPass::test_nested_immutable_list_type, test/test_fx.py::TestCSEPass::test_nochange, test/test_fx.py::TestCSEPass::test_rand_like, test/test_fx.py::TestCSEPass::test_rand_n, test/test_fx.py::TestCSEPass::test_random, test/test_fx.py::TestCSEPass::test_simple, test/test_fx.py::TestCSEPass::test_simple_2, test/test_fx.py::TestCSEPass::test_simple_multiple_same_ops, test/test_fx.py::TestCSEPass::test_two_args, test/test_fx.py::TestCSEPass::test_two_args_default, test/test_fx.py::TestDCE::test_dead_chain, test/test_fx.py::TestDCE::test_dead_getattr, test/test_fx.py::TestDCE::test_dead_placeholder, test/test_fx.py::TestDCE::test_dead_placeholder_with_user, test/test_fx.py::TestDCE::test_impure_custom, test/test_fx.py::TestDCE::test_impure_kwargs, test/test_fx.py::TestDCE::test_impure_nodes_args, test/test_fx.py::TestDCE::test_impure_random, test/test_fx.py::TestDCE::test_keep_collectives, test/test_fx.py::TestDCE::test_keep_collectives_no_overload, test/test_fx.py::TestDCE::test_keep_module_with_side_effects, test/test_fx.py::TestDCE::test_keep_setitem, test/test_fx.py::TestDCE::test_keep_torch_assert, test/test_fx.py::TestDCE::test_simple, test/test_fx.py::TestConstFold::test_check_inline_non_const, test/test_fx.py::TestConstFold::test_check_inline_non_const_mult_return, test/test_fx.py::TestConstFold::test_check_skip_folding_quant_dequant_pattern, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_no_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_placeholder_reordered, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr_three_input, test/test_fx.py::TestConstFold::test_const_fold_has_inlined_call_module_node, test/test_fx.py::TestConstFold::test_const_fold_module_attr, test/test_fx.py::TestConstFold::test_const_fold_multi_const_folded_attrs, test/test_fx.py::TestConstFold::test_const_fold_noop, test/test_fx.py::TestConstFold::test_const_fold_submod_hierarchy, test/test_fx.py::TestConstFold::test_const_fold_tensor_meta, test/test_fx.py::TestConstFold::test_const_fold_unused_placeholder, test/test_fx.py::TestConstFold::test_dict_output, test/test_fx.py::TestConstFold::test_fold_module, test/test_fx.py::TestConstFold::test_retain_node_meta, test/test_fx.py::TestConstFold::test_three_outputs, test/test_fx.py::TestConstFold::test_two_outputs, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_dim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_ndim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_nelement_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_numel_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_shape_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_size_const, test/test_fx.py::AnnotationsTest::test_annotate, test/test_fx.py::AnnotationsTest::test_annotations, test/test_fx.py::AnnotationsTest::test_broadcasting1, test/test_fx.py::AnnotationsTest::test_broadcasting2, test/test_fx.py::AnnotationsTest::test_broadcasting3, test/test_fx.py::AnnotationsTest::test_consistency, test/test_fx.py::AnnotationsTest::test_precision, test/test_fx.py::TypeCheckerTest::test_flatten_fully_static, test/test_fx.py::TypeCheckerTest::test_resnet50, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast_2, test/test_fx.py::TypeCheckerTest::test_type_check_add_false, test/test_fx.py::TypeCheckerTest::test_type_check_add_true, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_scalar, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_false, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_symbolic, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2_fully_static, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_types, test/test_fx.py::TypeCheckerTest::test_type_check_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_flatten3, test/test_fx.py::TypeCheckerTest::test_type_check_flatten_2, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_false, 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test/test_fx.py::TestMatcher::test_subgraph_matcher_with_attributes, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list_bad, test/test_fx.py::TestMatcher::test_variatic_arg_matching, test/test_fx.py::TestPassManager::test_pass_manager, test/test_fx.py::TestPassManager::test_pass_manager_bad_checks, test/test_fx.py::TestPassManager::test_pass_manager_checks, test/test_fx.py::TestPassManager::test_pass_manager_error, test/test_fx.py::TestPassManager::test_this_before_that_pass_constraint, test/test_fx.py::TestPassManager::test_topological_sort, test/test_fx.py::TestSourceMatcher::test_legalize_slice, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_True, 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test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_True, test/test_fx.py::TestSubgraphRewriter::test_matching_pattern_with_list_type_arg, test/test_fx.py::TestSubgraphRewriter::test_matching_variable_arguments, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_callback, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_filters, test/test_fx.py::TestSubgraphRewriter::test_replaced_nodes, test/test_fx.py::TestSubgraphRewriter::test_replacement_with_attrs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_call_method, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_correct_output_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, 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test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_multiple_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_traced_as_callable, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_overlapping_matches, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_trivial_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_args, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_results, test/test_fx.py::TestFX::test_all_input_nodes, test/test_fx.py::TestFX::test_annotation_with_future, test/test_fx.py::TestFX::test_annotations_empty_tuple, test/test_fx.py::TestFX::test_annotations_with_forward_references, test/test_fx.py::TestFX::test_annotations_with_no_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_internal_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_no_internal_forward_references, test/test_fx.py::TestFX::test_args_kwargs, test/test_fx.py::TestFX::test_args_kwargs_no_self, test/test_fx.py::TestFX::test_assert, test/test_fx.py::TestFX::test_ast_rewriter_reassigns_submodules, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert_with_message, test/test_fx.py::TestFX::test_ast_rewriter_wrap, test/test_fx.py::TestFX::test_ast_rewriter_wrap_fn_directly, test/test_fx.py::TestFX::test_ast_rewriter_wrap_with_submodule, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_autowrap_functions, test/test_fx.py::TestFX::test_concrete_arg_none_assert, test/test_fx.py::TestFX::test_construct_root_dict, test/test_fx.py::TestFX::test_control_flow_tracing, test/test_fx.py::TestFX::test_copy_it, test/test_fx.py::TestFX::test_copy_no_remap, test/test_fx.py::TestFX::test_ctx_mgr, test/test_fx.py::TestFX::test_custom_codegen, test/test_fx.py::TestFX::test_custom_codegen_with_transformer, test/test_fx.py::TestFX::test_custom_import, test/test_fx.py::TestFX::test_custom_proxy_dynamic_value, test/test_fx.py::TestFX::test_custom_proxy_input_dependent_control_flow, test/test_fx.py::TestFX::test_custom_proxy_type, test/test_fx.py::TestFX::test_custom_proxy_type_literal, test/test_fx.py::TestFX::test_custom_traceback_not_raised_when_exception_source_is_submodule, test/test_fx.py::TestFX::test_custom_traceback_raised_when_exception_source_is_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graph_with_tracer_cls, test/test_fx.py::TestFX::test_deepcopy_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graphmodule_with_transform, test/test_fx.py::TestFX::test_deepcopy_no_recursion, test/test_fx.py::TestFX::test_deepcopy_recursion_depth, test/test_fx.py::TestFX::test_deepcopy_tracer, test/test_fx.py::TestFX::test_deepcopy_with_submods_params, test/test_fx.py::TestFX::test_delete_unused_submodules_leaf, test/test_fx.py::TestFX::test_delete_unused_values, test/test_fx.py::TestFX::test_dict, test/test_fx.py::TestFX::test_direct_param_use, test/test_fx.py::TestFX::test_disallow_override, test/test_fx.py::TestFX::test_ellipsis, test/test_fx.py::TestFX::test_empty_graph_codegen, test/test_fx.py::TestFX::test_enum, test/test_fx.py::TestFX::test_erase_node_error, test/test_fx.py::TestFX::test_example_shape_prop, test/test_fx.py::TestFX::test_find_uses, test/test_fx.py::TestFX::test_fn_type_annotation_empty, test/test_fx.py::TestFX::test_fn_type_annotations, test/test_fx.py::TestFX::test_fx_and_or, test/test_fx.py::TestFX::test_fx_create_arg, test/test_fx.py::TestFX::test_fx_shifts, test/test_fx.py::TestFX::test_fx_stateless, test/test_fx.py::TestFX::test_get_torch_func_signature, test/test_fx.py::TestFX::test_getitem, test/test_fx.py::TestFX::test_getitem_subproc, test/test_fx.py::TestFX::test_graph_edit_with_proxy, test/test_fx.py::TestFX::test_graph_fns, test/test_fx.py::TestFX::test_graph_module, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_dict_init, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_mod_init, test/test_fx.py::TestFX::test_graph_module_replicate_for_dp, test/test_fx.py::TestFX::test_graph_unique_names, test/test_fx.py::TestFX::test_graph_unique_names_manual, test/test_fx.py::TestFX::test_immutable_dict_pytree_ops, test/test_fx.py::TestFX::test_immutable_list_pytree_ops, test/test_fx.py::TestFX::test_imul_code_print, test/test_fx.py::TestFX::test_inf_nan, test/test_fx.py::TestFX::test_inf_nan_kwds, test/test_fx.py::TestFX::test_informative_co_filename, test/test_fx.py::TestFX::test_inline_graph, test/test_fx.py::TestFX::test_insert_arg, test/test_fx.py::TestFX::test_insertion_point, test/test_fx.py::TestFX::test_interpreter, test/test_fx.py::TestFX::test_interpreter_default_args, test/test_fx.py::TestFX::test_interpreter_gc_values, test/test_fx.py::TestFX::test_interpreter_noop_resnet18, test/test_fx.py::TestFX::test_interpreter_not_enough_args, test/test_fx.py::TestFX::test_interpreter_onthefly_swap, test/test_fx.py::TestFX::test_interpreter_other_graph, test/test_fx.py::TestFX::test_interpreter_partial_eval, test/test_fx.py::TestFX::test_interpreter_run_node_override, test/test_fx.py::TestFX::test_interpreter_star_args, test/test_fx.py::TestFX::test_interpreter_with_codegen, test/test_fx.py::TestFX::test_layout, test/test_fx.py::TestFX::test_leaf_module, test/test_fx.py::TestFX::test_lineno_map, test/test_fx.py::TestFX::test_matmul_tracing, test/test_fx.py::TestFX::test_metadata_on_ph, test/test_fx.py::TestFX::test_module_deepcopy_edit_nodes, test/test_fx.py::TestFX::test_move_before, test/test_fx.py::TestFX::test_multi_insert_point, test/test_fx.py::TestFX::test_multiple_default_args, test/test_fx.py::TestFX::test_named_tuple_inlined, test/test_fx.py::TestFX::test_namedtuple_return_qualname, test/test_fx.py::TestFX::test_namedtuple_return_trace, test/test_fx.py::TestFX::test_native_callable, test/test_fx.py::TestFX::test_nn_module_stack, test/test_fx.py::TestFX::test_no_mutation, test/test_fx.py::TestFX::test_node_tagging, test/test_fx.py::TestFX::test_nonetype_annotation, test/test_fx.py::TestFX::test_partial_trace, test/test_fx.py::TestFX::test_pickle_custom_import, test/test_fx.py::TestFX::test_pickle_graphmodule, test/test_fx.py::TestFX::test_pickle_nonetype_annotation, test/test_fx.py::TestFX::test_pickle_torch_custom_ops, test/test_fx.py::TestFX::test_prepend_self, test/test_fx.py::TestFX::test_pretty_print, test/test_fx.py::TestFX::test_pretty_print_graph, test/test_fx.py::TestFX::test_pretty_print_node, test/test_fx.py::TestFX::test_pretty_print_targets, test/test_fx.py::TestFX::test_print_graph, test/test_fx.py::TestFX::test_profiler_ranges_side_effect, test/test_fx.py::TestFX::test_proxy_deepcopy_with_tracer, test/test_fx.py::TestFX::test_proxy_deepcopy_without_tracer, test/test_fx.py::TestFX::test_pytree, test/test_fx.py::TestFX::test_pytree_concrete, test/test_fx.py::TestFX::test_reassign_args_kwargs_uses, test/test_fx.py::TestFX::test_regular_and_default_args, test/test_fx.py::TestFX::test_remove_uses, test/test_fx.py::TestFX::test_remove_uses_with_custom_filter, test/test_fx.py::TestFX::test_replace_input, test/test_fx.py::TestFX::test_replace_uses, test/test_fx.py::TestFX::test_reserved_getattr, test/test_fx.py::TestFX::test_return_tuple, test/test_fx.py::TestFX::test_return_type_exists, test/test_fx.py::TestFX::test_return_type_exists_pre_pep585, test/test_fx.py::TestFX::test_script_method_trace, test/test_fx.py::TestFX::test_script_tensor_constant, test/test_fx.py::TestFX::test_sequential, test/test_fx.py::TestFX::test_shape_prop_aggregate, test/test_fx.py::TestFX::test_shape_prop_layout, test/test_fx.py::TestFX::test_shape_prop_layout_3d, test/test_fx.py::TestFX::test_shape_prop_unbacked_sym, test/test_fx.py::TestFX::test_single_default_arg, test/test_fx.py::TestFX::test_snake_case, test/test_fx.py::TestFX::test_sqrt, test/test_fx.py::TestFX::test_stack_traces, test/test_fx.py::TestFX::test_stack_traces_with_transformer, test/test_fx.py::TestFX::test_string_literal_return, test/test_fx.py::TestFX::test_submodule_manipulation_API, test/test_fx.py::TestFX::test_symbolic_trace_assert, test/test_fx.py::TestFX::test_symbolic_trace_sequential, test/test_fx.py::TestFX::test_tensor_attribute, test/test_fx.py::TestFX::test_tensor_attribute_coalseced, test/test_fx.py::TestFX::test_tensor_constant, test/test_fx.py::TestFX::test_throw_out_variant, test/test_fx.py::TestFX::test_torch_custom_ops, test/test_fx.py::TestFX::test_torch_fx_getattr, test/test_fx.py::TestFX::test_torch_fx_len, test/test_fx.py::TestFX::test_torch_op_overloads, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx_tensor_arg, test/test_fx.py::TestFX::test_trace_buffer_slice, test/test_fx.py::TestFX::test_trace_dict_int_keys, test/test_fx.py::TestFX::test_trace_dict_proxy_keys, test/test_fx.py::TestFX::test_trace_fn_constant, test/test_fx.py::TestFX::test_trace_function, test/test_fx.py::TestFX::test_trace_multiple_funcs, test/test_fx.py::TestFX::test_trace_return_dataclass, test/test_fx.py::TestFX::test_trace_return_dataclass_nested, test/test_fx.py::TestFX::test_trace_return_namedtuple, test/test_fx.py::TestFX::test_tracing_graphmodules_as_leaf_submodules, test/test_fx.py::TestFX::test_transformer_multi_outputs, test/test_fx.py::TestFX::test_transformer_noop, test/test_fx.py::TestFX::test_transformer_op_swap, test/test_fx.py::TestFX::test_transformer_preserves_nn_module_stack_for_get_attr, test/test_fx.py::TestFX::test_tuple_no_subscript, test/test_fx.py::TestFX::test_typename_print, test/test_fx.py::TestFX::test_typename_print_pre_pep585, test/test_fx.py::TestFX::test_unpack, test/test_fx.py::TestFX::test_unpack_dict_better_error, test/test_fx.py::TestFX::test_unpack_list_better_error, test/test_fx.py::TestFX::test_update_args_api, test/test_fx.py::TestFX::test_update_args_kwargs_yells_at_you, test/test_fx.py::TestFX::test_update_kwargs_api, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_function, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_module, test/test_fx.py::TestFX::test_varargs_concrete, test/test_fx.py::TestFX::test_wrap, test/test_fx.py::TestFX::test_wrap_decorated_function, test/test_fx.py::TestFX::test_wrap_fn_directly, test/test_fx.py::TestFX::test_wrap_with_submodule, test/test_fx.py::TestFX::test_wrapped_method, test/test_fx.py::TestFX::test_wrapped_retrace, test/test_fx.py::TestFX::test_wrapped_via_decorator, test/test_fx.py::TestFX::test_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_wrong_target_type, test/test_fx.py::TestFX::test_wrong_topo, test/test_fx.py::TestFXAPIBackwardCompatibility::test_adding_side_effect_function, test/test_fx.py::TestFXAPIBackwardCompatibility::test_class_member_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_function_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_preserve_unused_attr_after_unpickle, test/test_fx.py::TestFXAPIBackwardCompatibility::test_public_api_surface, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_affine_grid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_batch_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy_with_logits, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_tbc, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_similarity, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_ctc_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding_bag, test/test_fx.py::TestFunctionalTracing::test_nn_functional_feature_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gaussian_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_glu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_grid_sample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_group_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gumbel_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardswish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hinge_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_huber_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_instance_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_interpolate, test/test_fx.py::TestFunctionalTracing::test_nn_functional_kl_div, test/test_fx.py::TestFunctionalTracing::test_nn_functional_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_layer_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_linear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_local_response_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_log_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_logsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_margin_ranking_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mse_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_head_attention_forward, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_native_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_normalize, test/test_fx.py::TestFunctionalTracing::test_nn_functional_one_hot, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pad, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pairwise_distance, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pdist, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_unshuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_poisson_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_prelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu6, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rms_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_scaled_dot_product_attention, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_silu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_smooth_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmin, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softplus, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_with_distance_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_unfold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_nearest, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_H_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_T_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___getitem___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___radd___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rdiv___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmatmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmod___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rpow___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rsub___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__batch_norm_with_update_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__chunk_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__native_batch_norm_legit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_lengths_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_offsets_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__softmax_backward_data_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_abs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcdiv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_decomposed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_alias_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_all_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_allclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_aminmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_angle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_any_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_arange_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argsort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argwhere_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_partial_views_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_baddbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bernoulli_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bfloat16_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_block_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bool_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_shapes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bucketize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_byte_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cartesian_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cauchy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdouble_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ceil_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cfloat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chalf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_char_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_inverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_max_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_min_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clone_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_column_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_combinations_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_physical_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_constant_pad_nd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_contiguous_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_copysign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_corrcoef_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_count_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cov_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumulative_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_deg2rad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_embed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagflat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diff_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_digamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_floor_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_no_rounding_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_trunc_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_double_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_einsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_permuted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_equal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expm1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eye_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flip_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fliplr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flipud_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gather_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ge_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geometric_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geqrf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gradient_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_grid_sampler_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_grid_sampler_3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_half_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hash_tensor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_heaviside_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histogram_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histogramdd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hypot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_i0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_igamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_igammac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_put_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_inner_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_int_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isfinite_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isinf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isnan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isneginf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isposinf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isreal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_item_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_2inputs_2outputs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_4inputs_with_extra_args_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_binary_return_by_ref_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_unary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_kron_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_kthvalue_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ldexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_le_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lerp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lgamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cholesky_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cond_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_det_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eig_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigvals_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigvalsh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_householder_product_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_inv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_inv_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lstsq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lstsq_grad_oriented_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_factor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_factor_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_hermitian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_multi_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_norm_subgradients_at_zero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_hermitian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_singular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_qr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_slogdet_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_triangular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_svd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_svdvals_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_tensorinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_tensorsolve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vander_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vecdot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vector_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linspace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linspace_tensor_overload_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log10_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log1p_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_normal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_softmax_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logaddexp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logaddexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logcumsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logdet_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_and_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_not_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_or_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_xor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logspace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logspace_tensor_overload_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_long_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_unpack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mH_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mT_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_log_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_logaddexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_logsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_median_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_normalize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_softmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_std_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_var_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_matmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_matrix_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_pool2d_with_indices_backward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_reduction_no_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_reduction_with_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_maximum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_median_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_meshgrid_list_of_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_meshgrid_variadic_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_reduction_no_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_reduction_with_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_minimum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_movedim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_msort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_multinomial_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nan_to_num_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanmean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanmedian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanquantile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nansum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_narrow_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_narrow_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_batch_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_dropout_backward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_layer_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ne_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_neg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_ones_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_zeros_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nextafter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_alpha_dropout_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_batch_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_celu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_channel_shuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cosine_embedding_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cosine_similarity_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cross_entropy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_ctc_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_elu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_embedding_bag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_embedding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_gelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_glu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_grid_sample_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_group_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardsigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardswish_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardtanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hinge_embedding_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_huber_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_instance_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_area_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bicubic_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_linear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_trilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_kl_div_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_l1_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_layer_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_leaky_relu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_linear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_local_response_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_logsigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_margin_ranking_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_mish_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_mse_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multi_head_attention_forward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multi_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_normalize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_circular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_constant_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_reflect_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_negative_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pairwise_distance_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pixel_shuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_poisson_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_prelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_relu6_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_relu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_rms_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_rrelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_selu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_silu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_smooth_l1_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_soft_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softmin_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softplus_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softsign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_tanhshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_threshold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_unfold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_upsample_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_upsample_nearest_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nonzero_static_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_fro_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_inf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_nuc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_in_place_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_number_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ones_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ones_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ormqr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_outer_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pca_lowrank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_permute_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_permute_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pinverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polar_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_4_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_positive_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pow_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_put_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_qr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_quantile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rad2deg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rand_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randint_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randint_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randn_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ravel_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_real_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reciprocal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_remainder_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_renorm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_repeat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_repeat_interleave_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reshape_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reshape_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resize__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resize_as__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resolve_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resolve_neg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_roll_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rot90_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_neg_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rsqrt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rsub_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scalar_tensor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_searchsorted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_select_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sgn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_short_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_bartlett_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_blackman_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_cosine_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_gaussian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_general_cosine_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_general_hamming_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_hamming_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_hann_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_kaiser_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_nuttall_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signbit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sinc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_slice_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_slice_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_softmax_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sparse_mm_reduce_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sparse_sampled_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_airy_ai_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_j0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_j1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_y0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_y1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_u_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_w_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_entr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_erfcx_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_h_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_he_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i0e_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i1e_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_legendre_polynomial_p_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_log_ndtr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_i0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_i1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_k0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_k1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_ndtr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_ndtri_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_spherical_bessel_j0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_xlog1py_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_zeta_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_list_args_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_with_sizes_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_with_sizes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sqrt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_square_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_multiple_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_mean_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_stft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sub_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sum_to_size_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_svd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_svd_lowrank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_t_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_take_along_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_take_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tensor_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tensordot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_to_sparse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_topk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_transpose_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_transpose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trapz_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_triangular_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tril_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_triu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_true_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trunc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unbind_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unbind_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unflatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unfold_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unfold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_uniform_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unique_consecutive_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unique_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsafe_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsafe_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsqueeze_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsqueeze_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_mean_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vdot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_as_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_where_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_xlogy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zero__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zeros_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zeros_like_cpu_float32, test/test_fx.py::TestVisionTracing::test_torchvision_models_alexnet, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_base, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_small, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_tiny, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet121, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet161, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet169, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet201, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_320_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fcos_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_keypointrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_ssd300_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_ssdlite320_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b0, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b1, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b2, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b3, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b4, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b5, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b6, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b7, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_l, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_m, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_googlenet, test/test_fx.py::TestVisionTracing::test_torchvision_models_inception_v3, test/test_fx.py::TestVisionTracing::test_torchvision_models_maxvit_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_75, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_3, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_small, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_3_2gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_128gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_3_2gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet152, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet18, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet34, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_32x8d, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_64x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext50_32x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_lraspp_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x2_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_1, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mc3_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v1_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r2plus1d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r3d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_s3d, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_h_14, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet101_2, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet50_2 2025-09-07T07:10:02.4711317Z 2025-09-07T07:10:02.4711615Z Running test_transformers_privateuse1 1/1 ... [2025-09-07 07:10:02.371384] 2025-09-07T07:10:02.8535994Z Processing /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg 2025-09-07T07:10:03.1371766Z Preparing metadata (pyproject.toml) ... [?25l- done 2025-09-07T07:10:03.1372833Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch_openreg==0.0.1) (2.9.0a0+git93fb23d) 2025-09-07T07:10:03.1374138Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (3.19.1) 2025-09-07T07:10:03.1375346Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (4.15.0) 2025-09-07T07:10:03.1376554Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (80.9.0) 2025-09-07T07:10:03.1378142Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (1.13.3) 2025-09-07T07:10:03.1379598Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (2.8.8) 2025-09-07T07:10:03.1380732Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (3.1.6) 2025-09-07T07:10:03.1381845Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (2025.7.0) 2025-09-07T07:10:03.1444692Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch->torch_openreg==0.0.1) (1.3.0) 2025-09-07T07:10:03.1475012Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torch_openreg==0.0.1) (3.0.2) 2025-09-07T07:10:03.1538324Z Building wheels for collected packages: torch_openreg 2025-09-07T07:10:09.0397867Z Building wheel for torch_openreg (pyproject.toml) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / done 2025-09-07T07:10:09.0412622Z [?25h Created wheel for torch_openreg: filename=torch_openreg-0.0.1-cp313-cp313-linux_x86_64.whl size=277882 sha256=9138bb64588d45b86a77e9210a515100b98b92c9e79be05339e53d0c8e856945 2025-09-07T07:10:09.0414118Z Stored in directory: /tmp/pip-ephem-wheel-cache-9ensvbd5/wheels/bc/4f/31/9af65770c0a69187e95f1d791df9c71156b2b3f469bce9d735 2025-09-07T07:10:09.0437897Z Successfully built torch_openreg 2025-09-07T07:10:09.1892790Z Installing collected packages: torch_openreg 2025-09-07T07:10:09.2078617Z Successfully installed torch_openreg-0.0.1 2025-09-07T07:10:09.2659510Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:10:09.2663079Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers_privateuse1.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:10:09.266044] 2025-09-07T07:10:12.7849856Z 2025-09-07T07:10:12.7850815Z test_transformers_privateuse1 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_privateuse1_1.1_f124b01148fbae59_.log 2025-09-07T07:10:12.7853235Z Running 3 items in this shard: test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_fused_sdp_choice_privateuseone, test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable, test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable_backward 2025-09-07T07:10:12.7855163Z 2025-09-07T07:10:12.7855358Z Running test_show_pickle 1/1 ... [2025-09-07 07:10:12.785246] 2025-09-07T07:10:12.7855773Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:10:12.7858064Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_show_pickle.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:10:12.785570] 2025-09-07T07:10:16.0040406Z 2025-09-07T07:10:16.0041611Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_7650c6266d3081dd_.log 2025-09-07T07:10:16.0042591Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2025-09-07T07:10:16.0043017Z 2025-09-07T07:10:16.0043990Z Running test_utils 1/1 ... [2025-09-07 07:10:16.004241] 2025-09-07T07:10:16.0044461Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:10:16.0048679Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:10:16.004564] 2025-09-07T07:15:24.3838378Z 2025-09-07T07:15:24.3839286Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_c6e14503a3fd34a2_.log 2025-09-07T07:15:24.6460728Z Running 6047 items in this shard: test/test_utils.py::TestCheckpoint::test_checkpoint, test/test_utils.py::TestCheckpoint::test_checkpoint_module_list, test/test_utils.py::TestCheckpoint::test_checkpoint_no_tensors, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor_inputs_outputs, test/test_utils.py::TestCheckpoint::test_checkpoint_not_preserve_rng_state_and_without_reentrant, test/test_utils.py::TestCheckpoint::test_checkpoint_partial_grad, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cpu, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cuda, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_multiple_args, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_no_args, test/test_utils.py::TestCheckpoint::test_checkpoint_trigger, test/test_utils.py::TestCheckpoint::test_checkpoint_valid, test/test_utils.py::TestCheckpoint::test_checkpointing_without_reentrant_early_free, test/test_utils.py::TestCheckpoint::test_get_device_states_recursive, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_meta, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_multi_cuda, test/test_utils.py::TestDataLoaderUtils::test_multi_drop, test/test_utils.py::TestDataLoaderUtils::test_multi_keep, test/test_utils.py::TestDataLoaderUtils::test_random_seed, test/test_utils.py::TestDataLoaderUtils::test_single_drop, test/test_utils.py::TestDataLoaderUtils::test_single_keep, test/test_utils.py::TestBottleneck::test_bottleneck_cpu_only, test/test_utils.py::TestBottleneck::test_bottleneck_cuda, test/test_utils.py::TestCollectEnv::test_smoke, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bool, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_shapes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_prod_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_std_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_sum_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_one_hot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e5m2fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_more_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_nn_module_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_set_default_device_cpu, test/test_utils.py::TestCppExtensionUtils::test_cc_compiler_is_ok, test/test_utils.py::TestCppExtensionUtils::test_cpp_compiler_is_ok, test/test_utils.py::TestTraceback::test_basic, test/test_utils.py::TestTraceback::test_captured_traceback, test/test_utils.py::TestTraceback::test_captured_traceback_format_all, test/test_utils.py::TestTraceback::test_captured_traceback_format_all_cached, test/test_utils.py::TestTraceback::test_format_traceback_short, test/test_utils.py::TestTryImport::test_import_existing, test/test_utils.py::TestTryImport::test_import_imported, test/test_utils.py::TestTryImport::test_import_missing, test/test_utils.py::TestDeprecate::test_deprecated 2025-09-07T07:15:24.8573492Z 2025-09-07T07:15:24.8573723Z Running test_tensorexpr 1/1 ... [2025-09-07 07:15:24.391700] 2025-09-07T07:15:24.8574161Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:15:24.8575221Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorexpr.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:15:24.392020] 2025-09-07T07:15:27.1099231Z 2025-09-07T07:15:27.1100395Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_9d7f7a30658bbd14_.log 2025-09-07T07:15:27.1121059Z Running 74 items in this shard: test/test_tensorexpr.py::TestTensorExprFuser::test_add_const_rhs, test/test_tensorexpr.py::TestTensorExprFuser::test_add_sub, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_input_and_module, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_module, test/test_tensorexpr.py::TestTensorExprFuser::test_all_combos, test/test_tensorexpr.py::TestTensorExprFuser::test_alpha, test/test_tensorexpr.py::TestTensorExprFuser::test_binary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_bitwise_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast3, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_2, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_big2, test/test_tensorexpr.py::TestTensorExprFuser::test_cat, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_empty_tensors, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_negative_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_only, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_promote_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_with_constant_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_char, test/test_tensorexpr.py::TestTensorExprFuser::test_chunk, test/test_tensorexpr.py::TestTensorExprFuser::test_clamp, test/test_tensorexpr.py::TestTensorExprFuser::test_constant, test/test_tensorexpr.py::TestTensorExprFuser::test_double, test/test_tensorexpr.py::TestTensorExprFuser::test_double_intrinsics, test/test_tensorexpr.py::TestTensorExprFuser::test_dynamic_shape, test/test_tensorexpr.py::TestTensorExprFuser::test_easy, test/test_tensorexpr.py::TestTensorExprFuser::test_eq, test/test_tensorexpr.py::TestTensorExprFuser::test_exp_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_four_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_ge, test/test_tensorexpr.py::TestTensorExprFuser::test_gt, test/test_tensorexpr.py::TestTensorExprFuser::test_guard_fails, test/test_tensorexpr.py::TestTensorExprFuser::test_half_bn_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_half_gelu, test/test_tensorexpr.py::TestTensorExprFuser::test_int64_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_int_output, test/test_tensorexpr.py::TestTensorExprFuser::test_le, test/test_tensorexpr.py::TestTensorExprFuser::test_loop, test/test_tensorexpr.py::TestTensorExprFuser::test_lt, test/test_tensorexpr.py::TestTensorExprFuser::test_mask, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction2, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1_2, test/test_tensorexpr.py::TestTensorExprFuser::test_multi_rand, test/test_tensorexpr.py::TestTensorExprFuser::test_multioutput, test/test_tensorexpr.py::TestTensorExprFuser::test_multiple_outputs, test/test_tensorexpr.py::TestTensorExprFuser::test_nans, test/test_tensorexpr.py::TestTensorExprFuser::test_ne, test/test_tensorexpr.py::TestTensorExprFuser::test_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_propagated_mem_layout, test/test_tensorexpr.py::TestTensorExprFuser::test_rand_like, test/test_tensorexpr.py::TestTensorExprFuser::test_rank_two, test/test_tensorexpr.py::TestTensorExprFuser::test_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_remainder, test/test_tensorexpr.py::TestTensorExprFuser::test_reps, test/test_tensorexpr.py::TestTensorExprFuser::test_round_2, test/test_tensorexpr.py::TestTensorExprFuser::test_scalar, test/test_tensorexpr.py::TestTensorExprFuser::test_short, test/test_tensorexpr.py::TestTensorExprFuser::test_simple_add, test/test_tensorexpr.py::TestTensorExprFuser::test_sin_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_slice, test/test_tensorexpr.py::TestTensorExprFuser::test_sliced_stride, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cpu, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cuda, test/test_tensorexpr.py::TestTensorExprFuser::test_strided_output_preserved, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg2, test/test_tensorexpr.py::TestTensorExprFuser::test_transpose, test/test_tensorexpr.py::TestTensorExprFuser::test_unary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_unsqueeze, test/test_tensorexpr.py::TestTensorExprFuser::test_where 2025-09-07T07:15:27.1142685Z 2025-09-07T07:15:27.1142890Z Running test_multiprocessing 1/1 ... [2025-09-07 07:15:27.110223] 2025-09-07T07:15:27.1143330Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:15:27.1144467Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:15:27.110565] 2025-09-07T07:16:51.2953495Z 2025-09-07T07:16:51.2954860Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_26e5cd707fe992ac_.log 2025-09-07T07:16:51.2975062Z Running 42 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_rebuild_cuda_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork 2025-09-07T07:16:51.2989336Z 2025-09-07T07:16:51.2989531Z Running test_dispatch 1/1 ... [2025-09-07 07:16:51.295886] 2025-09-07T07:16:51.2989935Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:16:51.2990979Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:16:51.296300] 2025-09-07T07:18:06.5076887Z 2025-09-07T07:18:06.5077776Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_3bc39ec513eb99c9_.log 2025-09-07T07:18:06.5090102Z Running 32 items in this shard: test/test_dispatch.py::TestDispatch::test_all_invariants, test/test_dispatch.py::TestDispatch::test_computed_table, test/test_dispatch.py::TestDispatch::test_computed_table_with_ambiguous_autogradother, test/test_dispatch.py::TestDispatch::test_computed_table_with_autograd, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math_autogradcpu_fallthrough, test/test_dispatch.py::TestDispatch::test_computed_table_with_math, test/test_dispatch.py::TestDispatch::test_def, test/test_dispatch.py::TestDispatch::test_def_impl_schema_mismatch, test/test_dispatch.py::TestDispatch::test_def_only, test/test_dispatch.py::TestDispatch::test_def_with_explicit_alias, test/test_dispatch.py::TestDispatch::test_def_with_inference, test/test_dispatch.py::TestDispatch::test_dispatch_print_registrations_for_dispatch_key_invalid, test/test_dispatch.py::TestDispatch::test_find_dangling_impls, test/test_dispatch.py::TestDispatch::test_find_dangling_impls_ext, test/test_dispatch.py::TestDispatch::test_impl_only, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_defaulting, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_mismatch, test/test_dispatch.py::TestDispatch::test_multiple_def_error, test/test_dispatch.py::TestDispatch::test_multiple_fallback, test/test_dispatch.py::TestDispatch::test_overwrite_math, test/test_dispatch.py::TestPythonDispatcher::test_autogradother, test/test_dispatch.py::TestPythonDispatcher::test_basic, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_math, test/test_dispatch.py::TestPythonDispatcher::test_duplicate_registrations, test/test_dispatch.py::TestPythonDispatcher::test_math_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_quantized_structured_not_implemented 2025-09-07T07:18:06.5099814Z 2025-09-07T07:18:06.5100052Z Running test_namedtuple_return_api 1/1 ... [2025-09-07 07:18:06.508151] 2025-09-07T07:18:06.5100516Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:18:06.5101621Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtuple_return_api.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:18:06.508533] 2025-09-07T07:18:11.7802281Z 2025-09-07T07:18:11.7803504Z test_namedtuple_return_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtuple_return_api_1.1_58642f3508ef4ff8_.log 2025-09-07T07:18:11.7805884Z Running 3 items in this shard: test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_import_return_types, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_namedtuple_return, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_native_functions_yaml 2025-09-07T07:18:11.7809834Z 2025-09-07T07:18:11.7810176Z Running test_jit_disabled 1/1 ... [2025-09-07 07:18:11.780411] 2025-09-07T07:18:11.7810603Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:18:11.7811780Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_disabled.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:18:11.780728] 2025-09-07T07:18:15.0492600Z 2025-09-07T07:18:15.0493502Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_5cee9a760b466640_.log 2025-09-07T07:18:15.0495459Z Running 3 items in this shard: test/test_jit_disabled.py::TestJitDisabled::test_attribute, test/test_jit_disabled.py::TestJitDisabled::test_recursive_script, test/test_jit_disabled.py::TestJitDisabled::test_script_module_construction 2025-09-07T07:18:15.0497013Z 2025-09-07T07:18:15.0497220Z Running test_fake_tensor 1/1 ... [2025-09-07 07:18:15.049448] 2025-09-07T07:18:15.0497620Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:18:15.0500151Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fake_tensor.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:18:15.049763] 2025-09-07T07:19:08.6847940Z 2025-09-07T07:19:08.6849204Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_7522f608948f01ad_.log 2025-09-07T07:19:08.6972441Z Running 290 items in this shard: test/test_fake_tensor.py::FakeTensorTest::test__adaptive_avg_pool2d_backward, test/test_fake_tensor.py::FakeTensorTest::test_alias_call, test/test_fake_tensor.py::FakeTensorTest::test_allow_meta, test/test_fake_tensor.py::FakeTensorTest::test_aten_copy_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_index_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_slice_scatter_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_basic, test/test_fake_tensor.py::FakeTensorTest::test_batch_tensor, test/test_fake_tensor.py::FakeTensorTest::test_binary_op_type_promotion, test/test_fake_tensor.py::FakeTensorTest::test_constructor, test/test_fake_tensor.py::FakeTensorTest::test_convert_fake_to_real, test/test_fake_tensor.py::FakeTensorTest::test_cpu_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cuda_initialized, test/test_fake_tensor.py::FakeTensorTest::test_cuda_lstm, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_with_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_without_fallback, test/test_fake_tensor.py::FakeTensorTest::test_custom_op_fallback, test/test_fake_tensor.py::FakeTensorTest::test_data_dependent_operator, test/test_fake_tensor.py::FakeTensorTest::test_deepcopy, test/test_fake_tensor.py::FakeTensorTest::test_device_inplace_copy, test/test_fake_tensor.py::FakeTensorTest::test_embedding_bag_meta, test/test_fake_tensor.py::FakeTensorTest::test_export_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fake_device, test/test_fake_tensor.py::FakeTensorTest::test_fake_dispatch_keys, test/test_fake_tensor.py::FakeTensorTest::test_fake_grad_copy, test/test_fake_tensor.py::FakeTensorTest::test_fake_mode_error, test/test_fake_tensor.py::FakeTensorTest::test_fast_div, test/test_fake_tensor.py::FakeTensorTest::test_from_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fsdp_flat_param, test/test_fake_tensor.py::FakeTensorTest::test_full, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex128, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int16, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int8, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_uint8, test/test_fake_tensor.py::FakeTensorTest::test_index_put_error, test/test_fake_tensor.py::FakeTensorTest::test_jagged_fake_to_fake_preserved, test/test_fake_tensor.py::FakeTensorTest::test_like_constructor, test/test_fake_tensor.py::FakeTensorTest::test_mixed_real_and_fake_inputs, test/test_fake_tensor.py::FakeTensorTest::test_mode, test/test_fake_tensor.py::FakeTensorTest::test_nan_to_num, test/test_fake_tensor.py::FakeTensorTest::test_nanmean_out, test/test_fake_tensor.py::FakeTensorTest::test_new, test/test_fake_tensor.py::FakeTensorTest::test_no_tag_func, test/test_fake_tensor.py::FakeTensorTest::test_non_kwarg_device, test/test_fake_tensor.py::FakeTensorTest::test_non_overlapping_stride_zero, test/test_fake_tensor.py::FakeTensorTest::test_non_parameter_grad, test/test_fake_tensor.py::FakeTensorTest::test_normalize_device, test/test_fake_tensor.py::FakeTensorTest::test_op_with_zero_dim_bypassed, test/test_fake_tensor.py::FakeTensorTest::test_out_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_parameter_instantiation, test/test_fake_tensor.py::FakeTensorTest::test_parameter_view, test/test_fake_tensor.py::FakeTensorTest::test_print_in_fake_mode, test/test_fake_tensor.py::FakeTensorTest::test_randperm, test/test_fake_tensor.py::FakeTensorTest::test_recursive_invocation, test/test_fake_tensor.py::FakeTensorTest::test_repr, test/test_fake_tensor.py::FakeTensorTest::test_same_shape_env_preserved, test/test_fake_tensor.py::FakeTensorTest::test_scalar_inputs, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_False, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_True, test/test_fake_tensor.py::FakeTensorTest::test_setitem, test/test_fake_tensor.py::FakeTensorTest::test_shape_take_not_device, test/test_fake_tensor.py::FakeTensorTest::test_split_return_self, test/test_fake_tensor.py::FakeTensorTest::test_throw, test/test_fake_tensor.py::FakeTensorTest::test_tolist, test/test_fake_tensor.py::FakeTensorTest::test_type_as, test/test_fake_tensor.py::FakeTensorTest::test_unbind_copy_out, test/test_fake_tensor.py::FakeTensorTest::test_unsqueeze_copy, test/test_fake_tensor.py::FakeTensorTest::test_upsample_bilinear_small_channels, test/test_fake_tensor.py::FakeTensorTest::test_zero_dim, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test__adaptive_avg_pool2d_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_alias_call_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_allow_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_copy_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_index_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_slice_scatter_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_basic_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_batch_tensor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_binary_op_type_promotion_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_convert_fake_to_real_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cpu_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_initialized_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_lstm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_with_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_without_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_custom_op_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_data_dependent_operator_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_deepcopy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_device_inplace_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_embedding_bag_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_export_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_dispatch_keys_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_grad_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fast_div_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_from_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fsdp_flat_param_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_full_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex128_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int16_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_uint8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_put_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_jagged_fake_to_fake_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_like_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mixed_real_and_fake_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nanmean_out_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_no_tag_func_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_overlapping_stride_zero_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_parameter_grad_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_normalize_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_op_with_zero_dim_bypassed_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_out_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_instantiation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_print_in_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_randperm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_recursive_invocation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_repr_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_same_shape_env_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scalar_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_False_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_True_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_setitem_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_shape_take_not_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_split_return_self_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_throw_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_tolist_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_type_as_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unbind_copy_out_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unsqueeze_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_upsample_bilinear_small_channels_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_zero_dim_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorConstHandling::test_aliased_const_write, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_propagate_through_functions, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_batch_norm_cpu, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_in_intlist_repro, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_add, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_view_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storage_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storages, test/test_fake_tensor.py::FakeTensorConstHandling::test_simple, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_aliased_const_write_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_propagate_through_functions_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_batch_norm_cpu_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_in_intlist_repro_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_add_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_view_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storage_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storages_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_simple_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_key, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_weak_ref, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_from_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_to_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_multiple_modes, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_active_mode, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_ref_cycle, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_mode_error, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_non_view, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_view, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_key_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_weak_ref_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_from_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_to_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_multiple_modes_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_active_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_ref_cycle_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_non_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_view_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_conv_c1_backward, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_cross_entropy_loss, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_embedding_bag_private, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_fake_gpu_no_init, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_flash_attention, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_like_ops, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_module_to, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_no_dispatch_with_like_function, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_non_kwarg_only_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_sparse_new, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_str_storage, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_new, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_conv_c1_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_cross_entropy_loss_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_embedding_bag_private_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_fake_gpu_no_init_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_flash_attention_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_like_ops_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_module_to_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_no_dispatch_with_like_function_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_non_kwarg_only_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_sparse_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_str_storage_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_new_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args, test/test_fake_tensor.py::FakeTensorPropTest::test_nan_to_num, test/test_fake_tensor.py::FakeTensorPropTest::test_nonzero_stride, test/test_fake_tensor.py::FakeTensorPropTest::test_torch_load_with_fake_mode, test/test_fake_tensor.py::FakeTensorPropTest::test_unbacked_shape_realloc, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_fake_tensor_prop_on_nn_module_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_nonzero_stride_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_torch_load_with_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_unbacked_shape_realloc_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorSerialization::test_serialization, test/test_fake_tensor.py::FakeTensorSerialization::test_serialization_with_tracing, test/test_fake_tensor.py::FakeTensorDispatchCache::test__upsample_bilinear2d_aa_backward_dynamic_shapes, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_aten_index, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_bypass, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_dispatch_key_set, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_hit, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_inplace_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_constants, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_conj, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_inference, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_neg, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_memory_format, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_requires_grad, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_shape, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_storage_offset, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_stride, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_tuple_outputs, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_view_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_fft_hfft2_issue145522, test/test_fake_tensor.py::FakeTensorDispatchCache::test_from_buffer, test/test_fake_tensor.py::FakeTensorDispatchCache::test_inference_mode, test/test_fake_tensor.py::FakeTensorDispatchCache::test_invoke_subgraph, test/test_fake_tensor.py::FakeTensorDispatchCache::test_invoke_subgraph_cacheable_inplace, test/test_fake_tensor.py::FakeTensorDispatchCache::test_meta_tensor_to_fake_cpu, test/test_fake_tensor.py::FakeTensorDispatchCache::test_shape_env_settings, test/test_fake_tensor.py::FakeTensorDispatchCache::test_unbacked_output, test/test_fake_tensor.py::FakeTensorDispatchCache::test_wrapper_tensor_subclass_different_device, test/test_fake_tensor.py::FakeTensorPreferDeviceType::test_fake_tensor_prefer_device_type, test/test_fake_tensor.py::FakeTensorPreferDeviceType::test_fake_tensor_prefer_device_type_cpu_only 2025-09-07T07:19:08.7089554Z 2025-09-07T07:19:08.7089774Z Running test_cuda_trace 1/1 ... [2025-09-07 07:19:08.685746] 2025-09-07T07:19:08.7090379Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:19:08.7091471Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_trace.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:19:08.686152] 2025-09-07T07:19:11.1372194Z 2025-09-07T07:19:11.1373104Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_9b42d8ffb2e53662_.log 2025-09-07T07:19:11.1374151Z Running 0 items in this shard: 2025-09-07T07:19:11.1374346Z 2025-09-07T07:19:11.1374577Z Running test_cuda_nvml_based_avail 1/1 ... [2025-09-07 07:19:11.137212] 2025-09-07T07:19:11.1375026Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:19:11.1378508Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:19:11.137529] 2025-09-07T07:19:13.5842308Z 2025-09-07T07:19:13.5843581Z 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_e776bbed02995f99_.log 2025-09-07T07:19:13.5844468Z Running 0 items in this shard: 2025-09-07T07:19:13.5844660Z 2025-09-07T07:19:13.5846225Z Running test_autograd_fallback 1/1 ... [2025-09-07 07:19:13.584448] 2025-09-07T07:19:13.5846901Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:19:13.5849902Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autograd_fallback.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:19:13.584758] 2025-09-07T07:19:24.7637618Z 2025-09-07T07:19:24.7638551Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_1960d1b8d268787b_.log 2025-09-07T07:19:24.7651264Z Running 28 items in this shard: test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_warn 2025-09-07T07:19:24.7663832Z 2025-09-07T07:19:24.7664197Z Running dynamo/test_fake_distributed 1/1 ... [2025-09-07 07:19:24.764021] 2025-09-07T07:19:24.7664660Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:19:24.7665769Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_fake_distributed.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:19:24.764372] 2025-09-07T07:19:27.3623464Z 2025-09-07T07:19:27.3624432Z dynamo/test_fake_distributed 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fake_distributed_1.1_af4b022cd27a7c59_.log 2025-09-07T07:19:27.3625204Z 2025-09-07T07:19:27.3627391Z Running test_autocast 1/1 ... [2025-09-07 07:19:27.362574] 2025-09-07T07:19:27.3627855Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:19:27.3631238Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autocast.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:19:27.362890] 2025-09-07T07:19:39.8933184Z 2025-09-07T07:19:39.8934077Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_ebd7cea658de62a9_.log 2025-09-07T07:19:39.8941514Z Running 20 items in this shard: test/test_autocast.py::TestAutocastCPU::test_autocast_disabled_with_fp32_dtype, test/test_autocast.py::TestAutocastCPU::test_autocast_methods_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_16, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_rnn, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_16, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_need_autocast_promote, test/test_autocast.py::TestAutocastCPU::test_cpu_autocast_deprecated_warning, test/test_autocast.py::TestAutocastCPU::test_generic_autocast, test/test_autocast.py::TestAutocastGPU::test_autocast_prioritize, test/test_autocast.py::TestAutocastGPU::test_cache_disabled, test/test_autocast.py::TestAutocastGPU::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_bfloat16_supported, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_error_message, test/test_autocast.py::TestTorchAutocast::test_autocast_fast_dtype, test/test_autocast.py::TestTorchAutocast::test_invalid_device, test/test_autocast.py::TestTorchAutocast::test_non_string_device 2025-09-07T07:19:39.8948151Z 2025-09-07T07:19:39.8948347Z Running test_torch 1/2 ... [2025-09-07 07:19:39.893570] 2025-09-07T07:19:39.8948731Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:19:39.8949854Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:19:39.893878] 2025-09-07T07:27:07.9215446Z 2025-09-07T07:27:07.9218093Z test_torch 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.2_4fa9bb4a1196d583_.log 2025-09-07T07:27:07.9364288Z Running 472 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_concat_non_tuple_sequence, test/test_torch.py::TestTorch::test_Size_concat_wildcard, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, 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_chunk_neg_dim, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, 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_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, 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_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_manual_seed, test/test_torch.py::TestTorch::test_median_neg_dim, 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_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_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_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_function, 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_qengine, 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_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, 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_storage_base_init, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, 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_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, 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_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, 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_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_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unflatten, 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::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test__local_scalar_dense_with_empty_tensor_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_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_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_float32, 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_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_uint8, 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_copy_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_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_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_select_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_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_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_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_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, 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_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_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_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_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_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, 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, 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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_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_shift_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_skip_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_False_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_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_float64, 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_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, 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_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, 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_setitem_cpu_complex128, 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_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, 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_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, 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_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, 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_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, 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_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, 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 2025-09-07T07:27:07.9515483Z 2025-09-07T07:27:08.3664111Z Uploading artifacts took 0.44 seconds 2025-09-07T07:27:08.3665459Z Running test_torch 2/2 ... [2025-09-07 07:27:08.366374] 2025-09-07T07:27:08.3666073Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:27:08.3670910Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_torch.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:27:08.366787] 2025-09-07T07:33:33.1489342Z 2025-09-07T07:33:33.1490140Z test_torch 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_2.2_fd6e37324e77adbd_.log 2025-09-07T07:33:33.1651131Z Running 481 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_add_meta_scalar, 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_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_conj_neg_tolist, test/test_torch.py::TestTorch::test_conj_physical_meta_stride, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_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_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_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_device, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, 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_invalid_arg_error_handling, 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_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_memory_format, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, 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_dtype, 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_pyobj_preserved, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, 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_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_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, 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_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, 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_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, 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_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, 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_slot_dealloc, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, 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_slots, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_item_no_warning, test/test_torch.py::TestTorch::test_tensor_ressurecting_clear, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensor_with_grad_to_scalar_warning, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, 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_unbind_neg_dim, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_wildcard_import, 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_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cuda_errors_with_cpu_scalars_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, 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_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, 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_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_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_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_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, 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_uint32, 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_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_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_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, 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_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_64bit_indexing_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex32, 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_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_replication_pad2d_cpu, 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_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, 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_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, 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_int16, 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_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, 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_float32, 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_geometric_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int16, 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_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scale_will_not_overflow_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_foreach2_fused_True_AdamW_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_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_multiple_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_gradient_all_cpu_complex64, 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_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_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_large_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_invalid_shapes_grid_sampler_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e4m3fn, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e4m3fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e5m2fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_large_cumprod_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_large_cumsum_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_mem_overlap_cpu, 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_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_clone_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_consistency_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_preserved_after_permute_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_propagation_rules_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_to_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_shortcuts_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_module_share_memory_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_device_constrain_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_empty_wo_replacement_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_copy_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_no_nondeterministic_alert_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveAvgPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveAvgPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveMaxPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_CTCLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float64, 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_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_NLLLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_histc_cpu_float32, 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_kthvalue_cpu_float64, 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_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_float16, 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_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_put_accumulate_cpu_complex64, 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_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_float32, 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_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_bfloat16, 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_reduce_multiply_unsupported_dtypes_cpu_complex128, 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_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_int64, 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_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex64, 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_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_default_tensor_type_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex64, 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_storage_all_devices_non_blocking_True_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float32, 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_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bfloat16, 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_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_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, 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_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_float16, 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_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, 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_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, 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_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_use_count_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, 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_int32, 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_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, 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_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_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, 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_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_int64, 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_complex128, 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_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu 2025-09-07T07:33:33.1797994Z 2025-09-07T07:33:33.1798208Z Running test_sort_and_select 1/1 ... [2025-09-07 07:33:33.150741] 2025-09-07T07:33:33.1798639Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:33:33.1799701Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sort_and_select.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:33:33.151163] 2025-09-07T07:36:25.1327406Z 2025-09-07T07:36:25.1328535Z 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_61894b801b4e3d0c_.log 2025-09-07T07:36:25.1368167Z Running 113 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCPU::test_complex_unsupported_cpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_dtypes_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_scalar_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_slow_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_expanded_tensor_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_slice_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_restride_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_stable_none_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_4d_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_arguments_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_noncontiguous_gpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_quantized_scalar_input_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_dim_cpu 2025-09-07T07:36:25.1406564Z 2025-09-07T07:36:25.1406742Z Running test_native_mha 1/1 ... [2025-09-07 07:36:25.133415] 2025-09-07T07:36:25.1407153Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:36:25.1408189Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_native_mha.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:36:25.133738] 2025-09-07T07:37:06.2516324Z 2025-09-07T07:37:06.2517245Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_6f111f28e940f18b_.log 2025-09-07T07:37:06.2540679Z Running 28 items in this shard: test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_encoder_decoder_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_nested_cpu_float32 2025-09-07T07:37:06.2562764Z 2025-09-07T07:37:06.2562974Z Running test_cuda_primary_ctx 1/1 ... [2025-09-07 07:37:06.251894] 2025-09-07T07:37:06.2563406Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:37:06.2564588Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_primary_ctx.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:37:06.252220] 2025-09-07T07:37:08.7171704Z 2025-09-07T07:37:08.7172591Z 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_1a75975f7c9ff5c6_.log 2025-09-07T07:37:08.7173540Z Running 0 items in this shard: 2025-09-07T07:37:08.7173736Z 2025-09-07T07:37:08.7176100Z Running nn/test_pooling 1/1 ... [2025-09-07 07:37:08.717417] 2025-09-07T07:37:08.7176860Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:37:08.7179948Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_pooling.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:37:08.717742] 2025-09-07T07:40:09.6374102Z 2025-09-07T07:40:09.6375476Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_1b98b83d6e48472a_.log 2025-09-07T07:40:09.6420179Z Running 111 items in this shard: test/nn/test_pooling.py::TestAvgPool::test_avg_pool1d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool2d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool3d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d_with_divisor, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d_with_divisor, test/nn/test_pooling.py::TestPoolingNN::test_MaxUnpool2d_output_size, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_nhwc_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_backward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_forward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_non_contiguous, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_lower_precision, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_none, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_overflow, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool2d_nhwc_cpu, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool3d_input_check, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool1d_empty_kernel, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool3d, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool2d_empty_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool3d_backward_after_cat_dim1_device_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_errors_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_avg_pooling_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_max_pooling_backward_fails_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_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool3d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_indices_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_with_indices_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool3d_non_square_backward_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool_indices_no_batch_dim_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_large_size_int64_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_size_one_feature_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_invalid_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_large_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_bfloat16_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_large_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu 2025-09-07T07:40:09.6462633Z 2025-09-07T07:40:09.6462869Z Running test_multiprocessing_spawn 1/1 ... [2025-09-07 07:40:09.637882] 2025-09-07T07:40:09.6463331Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:09.6464419Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing_spawn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:09.638227] 2025-09-07T07:42:31.0390423Z 2025-09-07T07:42:31.0391398Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_95fa9eb4a4d8c85f_.log 2025-09-07T07:42:31.0405669Z Running 31 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_all, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_single, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerPerfTest::test_forkserver_perf, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable 2025-09-07T07:42:31.0416826Z 2025-09-07T07:42:31.0417046Z Running nn/test_convolution 1/2 ... [2025-09-07 07:42:31.039358] 2025-09-07T07:42:31.0417473Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:31.0418516Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_convolution.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:42:31.039676] 2025-09-07T07:48:49.6806778Z 2025-09-07T07:48:49.6808150Z nn/test_convolution 1/2 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.2_454ff63b71951be2_.log 2025-09-07T07:48:49.6975553Z Running 284 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_OneDNN, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias, 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_Conv3d_groups_wbias, 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_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_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_grad_conv1d_weight, 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_grouped_conv_cudnn_nhwc_support, test/nn/test_convolution.py::TestConvolutionNN::test_permute_conv2d_issue_120211, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_size_1_kernel_cpu, 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_ConvTranspose3d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_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_conv2d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_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_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_False_contiguous_False_cpu, 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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_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_ic1_channels_last_for_oneDNN_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_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_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_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_depthwise_conv_64bit_indexing_cpu, 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_float64 2025-09-07T07:48:49.7137759Z 2025-09-07T07:48:50.1177338Z Uploading artifacts took 0.44 seconds 2025-09-07T07:48:50.1180005Z Running nn/test_convolution 2/2 ... [2025-09-07 07:48:50.117837] 2025-09-07T07:48:50.1180429Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:48:50.1183694Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_convolution.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:48:50.118163] 2025-09-07T07:57:14.5762627Z 2025-09-07T07:57:14.5763838Z nn/test_convolution 2/2 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_2.2_36595a6965a3d9ae_.log 2025-09-07T07:57:14.5944934Z Running 314 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_backward_twice, 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_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_module_same_padding, 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_conv_cudnn_memory_layout_dominance, 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_conv2d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_weight, 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_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_dilation_1_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_float16, 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_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float16, 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_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, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_mismatch_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_batch_1_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_convert_conv3d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float32 2025-09-07T07:57:14.6124155Z 2025-09-07T07:57:14.6124416Z Running test_mobile_optimizer 1/1 ... [2025-09-07 07:57:14.577341] 2025-09-07T07:57:14.6124862Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:57:14.6125930Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mobile_optimizer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:57:14.577708] 2025-09-07T07:57:27.0084868Z 2025-09-07T07:57:27.0085975Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_6bc6a21439af0959_.log 2025-09-07T07:57:27.0089326Z Running 7 items in this shard: test/test_mobile_optimizer.py::TestOptimizer::test_clone_module_with_class, test/test_mobile_optimizer.py::TestOptimizer::test_generate_mobile_module_lints, test/test_mobile_optimizer.py::TestOptimizer::test_hoist_conv_packed_params, test/test_mobile_optimizer.py::TestOptimizer::test_mobilenet_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_preserve_bundled_inputs_methods, test/test_mobile_optimizer.py::TestOptimizer::test_quantized_conv_no_asan_failures 2025-09-07T07:57:27.0092034Z 2025-09-07T07:57:27.0092251Z Running test_spectral_ops 1/1 ... [2025-09-07 07:57:27.008719] 2025-09-07T07:57:27.0092721Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:57:27.0093965Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_spectral_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:57:27.009051] 2025-09-07T07:58:36.0776887Z 2025-09-07T07:58:36.0779396Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_a2d5bca88591050b_.log 2025-09-07T07:58:36.0873704Z Running 281 items in this shard: test/test_spectral_ops.py::TestFFTCPU::test_batch_istft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_istft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_definition_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_onesided_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_plan_cache_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_complex64, 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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_align_to_window_only_requires_non_center_cpu, 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 2025-09-07T07:58:36.0964991Z 2025-09-07T07:58:36.0965267Z Running distributions/test_distributions 1/3 ... [2025-09-07 07:58:36.078347] 2025-09-07T07:58:36.0965757Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:58:36.0966911Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:58:36.078664] 2025-09-07T08:03:22.3184719Z 2025-09-07T08:03:22.3186593Z distributions/test_distributions 1/3 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.3_2708ccc555891435_.log 2025-09-07T08:03:22.3217559Z Running 71 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, 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_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_generalized_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_geometric_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_invalid_parameter_broadcasting, 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_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_sequential_draw, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, 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_2d, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_studentT, test/distributions/test_distributions.py::TestDistributions::test_torch_binomial_dtype_errors, 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_shape, test/distributions/test_distributions.py::TestDistributions::test_wishart_stable_with_precision_matrix, test/distributions/test_distributions.py::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_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_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_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_infinite, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestJit::test_mean, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_variance 2025-09-07T08:03:22.3246175Z 2025-09-07T08:03:22.3246430Z Running distributions/test_distributions 2/3 ... [2025-09-07 08:03:22.318302] 2025-09-07T08:03:22.3246925Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:03:22.3248051Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:03:22.318659] 2025-09-07T08:16:18.0522080Z 2025-09-07T08:16:18.0523403Z distributions/test_distributions 2/3 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.3_8220522cd00328e1_.log 2025-09-07T08:16:18.0555617Z Running 78 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_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_stable, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, 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_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, 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_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, test/distributions/test_distributions.py::TestDistributions::test_repr, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, 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_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_wishart_sample, 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_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, test/distributions/test_distributions.py::TestRsample::test_dirichlet_on_diagonal, test/distributions/test_distributions.py::TestRsample::test_dirichlet_tangent_field, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_scalar_params, 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_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_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::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_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, 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_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, 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::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_sample 2025-09-07T08:16:18.0586185Z 2025-09-07T08:16:18.5205954Z Uploading artifacts took 0.47 seconds 2025-09-07T08:16:18.5209034Z Running distributions/test_distributions 3/3 ... [2025-09-07 08:16:18.520696] 2025-09-07T08:16:18.5209578Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:16:18.5212543Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:16:18.521034] 2025-09-07T08:19:12.0120363Z 2025-09-07T08:19:12.0121527Z distributions/test_distributions 3/3 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_3.3_fff498c2fb80b150_.log 2025-09-07T08:19:12.0155486Z Running 81 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, 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_log_prob_and_entropy, 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_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_dirichlet_log_prob, 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_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, 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_shape, test/distributions/test_distributions.py::TestDistributions::test_generalized_pareto, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, 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_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, 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_normal_log_prob, 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_2d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, 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_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_properties, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_beta, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape_vectorized_n, 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_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_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_pareto_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_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints 2025-09-07T08:19:12.0188307Z 2025-09-07T08:19:12.0188467Z Running doctests 1/1 ... [2025-09-07 08:19:12.012339] 2025-09-07T08:19:12.1137337Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch') 2025-09-07T08:19:12.1137853Z Listing tests 2025-09-07T08:19:12.3601548Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1493. 2025-09-07T08:19:12.3602473Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.3602871Z 2025-09-07T08:19:12.3603005Z dim_order(ambiguity_check=False) -> tuple 2025-09-07T08:19:12.3603253Z 2025-09-07T08:19:12.3603488Z Returns the uniquely determined tuple of int describing the dim order or 2025-09-07T08:19:12.3603958Z physical layout of :attr:`self`. 2025-09-07T08:19:12.3604238Z 2025-09-07T08:19:12.3604508Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-09-07T08:19:12.3605051Z starting from the outermost to the innermost dimension. 2025-09-07T08:19:12.3605345Z 2025-09-07T08:19:12.3605531Z Note that the dim order may not always be uniquely determined. 2025-09-07T08:19:12.3606234Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-09-07T08:19:12.3607154Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-09-07T08:19:12.3608133Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-09-07T08:19:12.3608823Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-09-07T08:19:12.3609387Z Otherwise, it will raise TypeError. 2025-09-07T08:19:12.3609613Z 2025-09-07T08:19:12.3609694Z Args: 2025-09-07T08:19:12.3610135Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-09-07T08:19:12.3610572Z 2025-09-07T08:19:12.3610703Z Examples:: 2025-09-07T08:19:12.3610826Z 2025-09-07T08:19:12.3610938Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-09-07T08:19:12.3611251Z (0, 1, 2, 3) 2025-09-07T08:19:12.3611549Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-09-07T08:19:12.3611911Z (0, 2, 1, 3) 2025-09-07T08:19:12.3612342Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-09-07T08:19:12.3612761Z (0, 2, 3, 1) 2025-09-07T08:19:12.3613013Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-09-07T08:19:12.3613323Z (0, 1, 2, 3) 2025-09-07T08:19:12.3613533Z >>> try: 2025-09-07T08:19:12.3613838Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-09-07T08:19:12.3614242Z ... except RuntimeError as e: 2025-09-07T08:19:12.3614539Z ... print(e) 2025-09-07T08:19:12.3614991Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-09-07T08:19:12.3615545Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-09-07T08:19:12.3615980Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-09-07T08:19:12.3616452Z ... ) # It can be mapped to contiguous format 2025-09-07T08:19:12.3616781Z (0, 1, 2, 3) 2025-09-07T08:19:12.3616990Z >>> try: 2025-09-07T08:19:12.3617302Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-09-07T08:19:12.3617714Z ... except TypeError as e: 2025-09-07T08:19:12.3617994Z ... print(e) 2025-09-07T08:19:12.3618368Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-09-07T08:19:12.3618737Z 2025-09-07T08:19:12.3618826Z .. warning:: 2025-09-07T08:19:12.3619156Z The dim_order tensor API is experimental and subject to change. 2025-09-07T08:19:12.3619464Z 2025-09-07T08:19:12.3619726Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.3620090Z 2025-09-07T08:19:12.4169379Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=397. 2025-09-07T08:19:12.4170275Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.4171178Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-09-07T08:19:12.4171564Z 2025-09-07T08:19:12.4171749Z This is helpful when you want to visualize data over some 2025-09-07T08:19:12.4172198Z range of inputs. See below for a plotting example. 2025-09-07T08:19:12.4172463Z 2025-09-07T08:19:12.4172638Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-09-07T08:19:12.4173110Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-09-07T08:19:12.4173734Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-09-07T08:19:12.4174207Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-09-07T08:19:12.4174671Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-09-07T08:19:12.4175085Z to the result shape. 2025-09-07T08:19:12.4175266Z 2025-09-07T08:19:12.4175376Z .. note:: 2025-09-07T08:19:12.4175686Z 0D inputs are treated equivalently to 1D inputs of a 2025-09-07T08:19:12.4176061Z single element. 2025-09-07T08:19:12.4176233Z 2025-09-07T08:19:12.4176339Z .. warning:: 2025-09-07T08:19:12.4176768Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-09-07T08:19:12.4177222Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-09-07T08:19:12.4177513Z 2025-09-07T08:19:12.4177664Z In the future `torch.meshgrid` will transition to 2025-09-07T08:19:12.4178076Z `indexing='xy'` as the default. 2025-09-07T08:19:12.4178305Z 2025-09-07T08:19:12.4178501Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-09-07T08:19:12.4178974Z this issue with the goal of migrating to NumPy's behavior. 2025-09-07T08:19:12.4179264Z 2025-09-07T08:19:12.4179366Z .. seealso:: 2025-09-07T08:19:12.4179510Z 2025-09-07T08:19:12.4179681Z :func:`torch.cartesian_prod` has the same effect but it 2025-09-07T08:19:12.4180104Z collects the data in a tensor of vectors. 2025-09-07T08:19:12.4180355Z 2025-09-07T08:19:12.4180437Z Args: 2025-09-07T08:19:12.4180934Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-09-07T08:19:12.4181471Z treated as tensors of size :math:`(1,)` automatically 2025-09-07T08:19:12.4181759Z 2025-09-07T08:19:12.4181934Z indexing: (str, optional): the indexing mode, either "xy" 2025-09-07T08:19:12.4182397Z or "ij", defaults to "ij". See warning for future changes. 2025-09-07T08:19:12.4182681Z 2025-09-07T08:19:12.4182849Z If "xy" is selected, the first dimension corresponds 2025-09-07T08:19:12.4183287Z to the cardinality of the second input and the second 2025-09-07T08:19:12.4183725Z dimension corresponds to the cardinality of the first 2025-09-07T08:19:12.4184103Z input. 2025-09-07T08:19:12.4184270Z 2025-09-07T08:19:12.4184417Z If "ij" is selected, the dimensions are in the same 2025-09-07T08:19:12.4184813Z order as the cardinality of the inputs. 2025-09-07T08:19:12.4185053Z 2025-09-07T08:19:12.4185149Z Returns: 2025-09-07T08:19:12.4185439Z seq (sequence of Tensors): If the input has :math:`N` 2025-09-07T08:19:12.4185872Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-09-07T08:19:12.4186321Z output will also have :math:`N` tensors, where each tensor 2025-09-07T08:19:12.4186744Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-09-07T08:19:12.4186971Z 2025-09-07T08:19:12.4187058Z Example:: 2025-09-07T08:19:12.4187207Z 2025-09-07T08:19:12.4187314Z >>> x = torch.tensor([1, 2, 3]) 2025-09-07T08:19:12.4187643Z >>> y = torch.tensor([4, 5, 6]) 2025-09-07T08:19:12.4187854Z 2025-09-07T08:19:12.4188060Z Observe the element-wise pairings across the grid, (1, 4), 2025-09-07T08:19:12.4188644Z (1, 5), ..., (3, 6). This is the same thing as the 2025-09-07T08:19:12.4188988Z cartesian product. 2025-09-07T08:19:12.4189403Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-09-07T08:19:12.4189774Z >>> grid_x 2025-09-07T08:19:12.4190147Z tensor([[1, 1, 1], 2025-09-07T08:19:12.4190646Z [2, 2, 2], 2025-09-07T08:19:12.4191036Z [3, 3, 3]]) 2025-09-07T08:19:12.4191300Z >>> grid_y 2025-09-07T08:19:12.4191548Z tensor([[4, 5, 6], 2025-09-07T08:19:12.4191890Z [4, 5, 6], 2025-09-07T08:19:12.4192386Z [4, 5, 6]]) 2025-09-07T08:19:12.4192727Z 2025-09-07T08:19:12.4192909Z This correspondence can be seen when these grids are 2025-09-07T08:19:12.4193297Z stacked properly. 2025-09-07T08:19:12.4193673Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-09-07T08:19:12.4194127Z ... torch.cartesian_prod(x, y)) 2025-09-07T08:19:12.4194455Z True 2025-09-07T08:19:12.4194589Z 2025-09-07T08:19:12.4194784Z `torch.meshgrid` is commonly used to produce a grid for 2025-09-07T08:19:12.4195231Z plotting. 2025-09-07T08:19:12.4195511Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-09-07T08:19:12.4195888Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-09-07T08:19:12.4196263Z >>> import matplotlib.pyplot as plt 2025-09-07T08:19:12.4196629Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-09-07T08:19:12.4196976Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-09-07T08:19:12.4197346Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-09-07T08:19:12.4197724Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-09-07T08:19:12.4198085Z >>> ax = plt.axes(projection='3d') 2025-09-07T08:19:12.4198453Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-09-07T08:19:12.4198815Z >>> plt.show() 2025-09-07T08:19:12.4198992Z 2025-09-07T08:19:12.4199214Z .. image:: ../_static/img/meshgrid.png 2025-09-07T08:19:12.4199539Z :width: 512 2025-09-07T08:19:12.4199691Z 2025-09-07T08:19:12.4199773Z 2025-09-07T08:19:12.4200146Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.4200529Z 2025-09-07T08:19:12.4201074Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=793. 2025-09-07T08:19:12.4201925Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.4202687Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-09-07T08:19:12.4203197Z 2025-09-07T08:19:12.4203345Z Returns the unique elements of the input tensor. 2025-09-07T08:19:12.4203600Z 2025-09-07T08:19:12.4203906Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-09-07T08:19:12.4204598Z this function also eliminates non-consecutive duplicate values. 2025-09-07T08:19:12.4204920Z 2025-09-07T08:19:12.4205159Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-09-07T08:19:12.4205784Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-09-07T08:19:12.4206479Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-09-07T08:19:12.4207061Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-09-07T08:19:12.4207352Z 2025-09-07T08:19:12.4207446Z Args: 2025-09-07T08:19:12.4207670Z input (Tensor): the input tensor 2025-09-07T08:19:12.4208104Z sorted (bool): Whether to sort the unique elements in ascending order 2025-09-07T08:19:12.4208549Z before returning as output. 2025-09-07T08:19:12.4209012Z return_inverse (bool): Whether to also return the indices for where 2025-09-07T08:19:12.4209569Z elements in the original input ended up in the returned unique list. 2025-09-07T08:19:12.4210122Z return_counts (bool): Whether to also return the counts for each unique 2025-09-07T08:19:12.4210557Z element. 2025-09-07T08:19:12.4210916Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-09-07T08:19:12.4211447Z unique of the flattened input is returned. Otherwise, each of the 2025-09-07T08:19:12.4211966Z tensors indexed by the given dimension is treated as one of the 2025-09-07T08:19:12.4212499Z elements to apply the unique operation upon. See examples for more 2025-09-07T08:19:12.4212937Z details. Default: ``None`` 2025-09-07T08:19:12.4213144Z 2025-09-07T08:19:12.4213241Z Returns: 2025-09-07T08:19:12.4213643Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-09-07T08:19:12.4214059Z 2025-09-07T08:19:12.4214253Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-09-07T08:19:12.4214715Z - **inverse_indices** (*Tensor*): (optional) if 2025-09-07T08:19:12.4215187Z :attr:`return_inverse` is True, there will be an additional 2025-09-07T08:19:12.4215695Z returned tensor (same shape as input) representing the indices 2025-09-07T08:19:12.4216205Z for where elements in the original input map to in the output; 2025-09-07T08:19:12.4216709Z otherwise, this function will only return a single tensor. 2025-09-07T08:19:12.4217145Z - **counts** (*Tensor*): (optional) if 2025-09-07T08:19:12.4217560Z :attr:`return_counts` is True, there will be an additional 2025-09-07T08:19:12.4218041Z returned tensor (same shape as output or output.size(dim), 2025-09-07T08:19:12.4218525Z if dim was specified) representing the number of occurrences 2025-09-07T08:19:12.4218952Z for each unique value or tensor. 2025-09-07T08:19:12.4219186Z 2025-09-07T08:19:12.4219327Z Example:: 2025-09-07T08:19:12.4219459Z 2025-09-07T08:19:12.4219685Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-09-07T08:19:12.4220095Z >>> output 2025-09-07T08:19:12.4220333Z tensor([1, 2, 3]) 2025-09-07T08:19:12.4220511Z 2025-09-07T08:19:12.4220635Z >>> output, inverse_indices = torch.unique( 2025-09-07T08:19:12.4221115Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T08:19:12.4221560Z >>> output 2025-09-07T08:19:12.4221783Z tensor([1, 2, 3]) 2025-09-07T08:19:12.4222048Z >>> inverse_indices 2025-09-07T08:19:12.4222318Z tensor([0, 2, 1, 2]) 2025-09-07T08:19:12.4222489Z 2025-09-07T08:19:12.4222626Z >>> output, inverse_indices = torch.unique( 2025-09-07T08:19:12.4223096Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T08:19:12.4223539Z >>> output 2025-09-07T08:19:12.4223777Z tensor([1, 2, 3]) 2025-09-07T08:19:12.4224040Z >>> inverse_indices 2025-09-07T08:19:12.4224295Z tensor([[0, 2], 2025-09-07T08:19:12.4224537Z [1, 2]]) 2025-09-07T08:19:12.4224691Z 2025-09-07T08:19:12.4224799Z >>> a = torch.tensor([ 2025-09-07T08:19:12.4225068Z ... [ 2025-09-07T08:19:12.4225286Z ... [1, 1, 0, 0], 2025-09-07T08:19:12.4225563Z ... [1, 1, 0, 0], 2025-09-07T08:19:12.4225837Z ... [0, 0, 1, 1], 2025-09-07T08:19:12.4226107Z ... ], 2025-09-07T08:19:12.4226319Z ... [ 2025-09-07T08:19:12.4226548Z ... [0, 0, 1, 1], 2025-09-07T08:19:12.4226822Z ... [0, 0, 1, 1], 2025-09-07T08:19:12.4227104Z ... [1, 1, 1, 1], 2025-09-07T08:19:12.4227359Z ... ], 2025-09-07T08:19:12.4227615Z ... [ 2025-09-07T08:19:12.4227842Z ... [1, 1, 0, 0], 2025-09-07T08:19:12.4228115Z ... [1, 1, 0, 0], 2025-09-07T08:19:12.4228378Z ... [0, 0, 1, 1], 2025-09-07T08:19:12.4228647Z ... ], 2025-09-07T08:19:12.4228871Z ... ]) 2025-09-07T08:19:12.4228996Z 2025-09-07T08:19:12.4229219Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-09-07T08:19:12.4229752Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-09-07T08:19:12.4230216Z >>> # each other, so one of them will be removed. 2025-09-07T08:19:12.4230569Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-09-07T08:19:12.4230874Z tensor(True) 2025-09-07T08:19:12.4231138Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-09-07T08:19:12.4231474Z >>> a_unique_dim0 2025-09-07T08:19:12.4231738Z tensor([[[0, 0, 1, 1], 2025-09-07T08:19:12.4232007Z [0, 0, 1, 1], 2025-09-07T08:19:12.4232261Z [1, 1, 1, 1]], 2025-09-07T08:19:12.4232528Z [[1, 1, 0, 0], 2025-09-07T08:19:12.4232794Z [1, 1, 0, 0], 2025-09-07T08:19:12.4233091Z [0, 0, 1, 1]]]) 2025-09-07T08:19:12.4233266Z 2025-09-07T08:19:12.4233478Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-09-07T08:19:12.4233908Z >>> # `a_unique_dim0`: 2025-09-07T08:19:12.4234211Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-09-07T08:19:12.4234534Z tensor(True) 2025-09-07T08:19:12.4234794Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-09-07T08:19:12.4235115Z tensor(True) 2025-09-07T08:19:12.4235272Z 2025-09-07T08:19:12.4235472Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-09-07T08:19:12.4235981Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-09-07T08:19:12.4236387Z >>> # them will be removed. 2025-09-07T08:19:12.4236678Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-09-07T08:19:12.4236978Z tensor(True) 2025-09-07T08:19:12.4237295Z >>> torch.unique(a, dim=1) 2025-09-07T08:19:12.4237592Z tensor([[[0, 0, 1, 1], 2025-09-07T08:19:12.4237849Z [1, 1, 0, 0]], 2025-09-07T08:19:12.4238117Z [[1, 1, 1, 1], 2025-09-07T08:19:12.4238388Z [0, 0, 1, 1]], 2025-09-07T08:19:12.4238646Z [[0, 0, 1, 1], 2025-09-07T08:19:12.4238919Z [1, 1, 0, 0]]]) 2025-09-07T08:19:12.4239109Z 2025-09-07T08:19:12.4239317Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-09-07T08:19:12.4239807Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-09-07T08:19:12.4240257Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-09-07T08:19:12.4240651Z >>> # sub-tensors will be removed. 2025-09-07T08:19:12.4240978Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-09-07T08:19:12.4241276Z tensor(True) 2025-09-07T08:19:12.4241531Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-09-07T08:19:12.4241818Z tensor(True) 2025-09-07T08:19:12.4242072Z >>> torch.unique(a, dim=2) 2025-09-07T08:19:12.4242363Z tensor([[[0, 1], 2025-09-07T08:19:12.4242614Z [0, 1], 2025-09-07T08:19:12.4242846Z [1, 0]], 2025-09-07T08:19:12.4243095Z [[1, 0], 2025-09-07T08:19:12.4243341Z [1, 0], 2025-09-07T08:19:12.4243573Z [1, 1]], 2025-09-07T08:19:12.4243824Z [[0, 1], 2025-09-07T08:19:12.4244067Z [0, 1], 2025-09-07T08:19:12.4244416Z [1, 0]]]) 2025-09-07T08:19:12.4244653Z 2025-09-07T08:19:12.4245023Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.4245410Z 2025-09-07T08:19:12.4361966Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=565. 2025-09-07T08:19:12.4362994Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.4363452Z 2025-09-07T08:19:12.4363623Z Load a model from a github repo or a local directory. 2025-09-07T08:19:12.4363944Z 2025-09-07T08:19:12.4364249Z Note: Loading a model is the typical use case, but this can also be used to 2025-09-07T08:19:12.4364892Z for loading other objects such as tokenizers, loss functions, etc. 2025-09-07T08:19:12.4365268Z 2025-09-07T08:19:12.4365480Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-09-07T08:19:12.4365962Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-09-07T08:19:12.4366409Z ref (a tag or a branch). 2025-09-07T08:19:12.4366573Z 2025-09-07T08:19:12.4366740Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-09-07T08:19:12.4367188Z path to a local directory. 2025-09-07T08:19:12.4367378Z 2025-09-07T08:19:12.4367458Z Args: 2025-09-07T08:19:12.4367709Z repo_or_dir (str): If ``source`` is 'github', 2025-09-07T08:19:12.4368318Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-09-07T08:19:12.4369253Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-09-07T08:19:12.4369992Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-09-07T08:19:12.4370691Z If ``source`` is 'local' then it should be a path to a local directory. 2025-09-07T08:19:12.4371268Z model (str): the name of a callable (entrypoint) defined in the 2025-09-07T08:19:12.4371700Z repo/dir's ``hubconf.py``. 2025-09-07T08:19:12.4372157Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T08:19:12.4372705Z source (str, optional): 'github' or 'local'. Specifies how 2025-09-07T08:19:12.4373165Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-09-07T08:19:12.4373853Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-09-07T08:19:12.4374610Z This parameter was introduced in v1.12 and helps ensuring that users 2025-09-07T08:19:12.4375168Z only run code from repos that they trust. 2025-09-07T08:19:12.4375476Z 2025-09-07T08:19:12.4375690Z - If ``False``, a prompt will ask the user whether the repo should 2025-09-07T08:19:12.4376194Z be trusted. 2025-09-07T08:19:12.4376563Z - If ``True``, the repo will be added to the trusted list and loaded 2025-09-07T08:19:12.4377048Z without requiring explicit confirmation. 2025-09-07T08:19:12.4377535Z - If ``"check"``, the repo will be checked against the list of 2025-09-07T08:19:12.4378040Z trusted repos in the cache. If it is not present in that list, the 2025-09-07T08:19:12.4378642Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-09-07T08:19:12.4379207Z - If ``None``: this will raise a warning, inviting the user to set 2025-09-07T08:19:12.4379731Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-09-07T08:19:12.4380283Z is only present for backward compatibility and will be removed in 2025-09-07T08:19:12.4380769Z v2.0. 2025-09-07T08:19:12.4380905Z 2025-09-07T08:19:12.4381124Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-09-07T08:19:12.4381720Z force_reload (bool, optional): whether to force a fresh download of 2025-09-07T08:19:12.4382301Z the github repo unconditionally. Does not have any effect if 2025-09-07T08:19:12.4382739Z ``source = 'local'``. Default is ``False``. 2025-09-07T08:19:12.4383252Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-09-07T08:19:12.4383851Z local caches. Note that the message about first download cannot be 2025-09-07T08:19:12.4384360Z muted. Does not have any effect if ``source = 'local'``. 2025-09-07T08:19:12.4384836Z Default is ``True``. 2025-09-07T08:19:12.4385385Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-09-07T08:19:12.4386154Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-09-07T08:19:12.4386879Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-09-07T08:19:12.4387471Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-09-07T08:19:12.4388036Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T08:19:12.4388431Z 2025-09-07T08:19:12.4388527Z Returns: 2025-09-07T08:19:12.4388833Z The output of the ``model`` callable when called with the given 2025-09-07T08:19:12.4389286Z ``*args`` and ``**kwargs``. 2025-09-07T08:19:12.4389481Z 2025-09-07T08:19:12.4389564Z Example: 2025-09-07T08:19:12.4389843Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T08:19:12.4390227Z >>> # from a github repo 2025-09-07T08:19:12.4390496Z >>> repo = "pytorch/vision" 2025-09-07T08:19:12.4390849Z >>> model = torch.hub.load( 2025-09-07T08:19:12.4391764Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-09-07T08:19:12.4392159Z ... ) 2025-09-07T08:19:12.4392423Z >>> # from a local directory 2025-09-07T08:19:12.4392748Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T08:19:12.4393144Z >>> # xdoctest: +SKIP 2025-09-07T08:19:12.4393561Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-09-07T08:19:12.4393988Z 2025-09-07T08:19:12.4394242Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.4394685Z 2025-09-07T08:19:12.4395131Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=657. 2025-09-07T08:19:12.4396028Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.4396473Z 2025-09-07T08:19:12.4396658Z Load a model from a local directory with a ``hubconf.py``. 2025-09-07T08:19:12.4397071Z 2025-09-07T08:19:12.4397169Z Args: 2025-09-07T08:19:12.4397465Z hubconf_dir (str): path to a local directory that contains a 2025-09-07T08:19:12.4397927Z ``hubconf.py``. 2025-09-07T08:19:12.4398278Z model (str): name of an entrypoint defined in the directory's 2025-09-07T08:19:12.4398746Z ``hubconf.py``. 2025-09-07T08:19:12.4399099Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T08:19:12.4399704Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T08:19:12.4400047Z 2025-09-07T08:19:12.4400134Z Returns: 2025-09-07T08:19:12.4400483Z a single model with corresponding pretrained weights. 2025-09-07T08:19:12.4400758Z 2025-09-07T08:19:12.4400855Z Example: 2025-09-07T08:19:12.4401083Z >>> # xdoctest: +SKIP("stub local path") 2025-09-07T08:19:12.4401440Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T08:19:12.4401844Z >>> model = _load_local( 2025-09-07T08:19:12.4402115Z ... path, 2025-09-07T08:19:12.4402336Z ... "resnet50", 2025-09-07T08:19:12.4402634Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-09-07T08:19:12.4402968Z ... ) 2025-09-07T08:19:12.4403083Z 2025-09-07T08:19:12.4403341Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.4403720Z 2025-09-07T08:19:12.4404349Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=696. 2025-09-07T08:19:12.4405200Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.4405731Z Download object at the given URL to a local path. 2025-09-07T08:19:12.4406001Z 2025-09-07T08:19:12.4406083Z Args: 2025-09-07T08:19:12.4406333Z url (str): URL of the object to download 2025-09-07T08:19:12.4406832Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-09-07T08:19:12.4407559Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-09-07T08:19:12.4408138Z Default: None 2025-09-07T08:19:12.4408560Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-09-07T08:19:12.4409025Z Default: True 2025-09-07T08:19:12.4409187Z 2025-09-07T08:19:12.4409270Z Example: 2025-09-07T08:19:12.4409536Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T08:19:12.4409895Z >>> # xdoctest: +REQUIRES(POSIX) 2025-09-07T08:19:12.4410233Z >>> torch.hub.download_url_to_file( 2025-09-07T08:19:12.4410676Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-09-07T08:19:12.4411121Z ... "/tmp/temporary_file", 2025-09-07T08:19:12.4411415Z ... ) 2025-09-07T08:19:12.4411544Z 2025-09-07T08:19:12.4411636Z 2025-09-07T08:19:12.4411989Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.4412370Z 2025-09-07T08:19:12.4412918Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=821. 2025-09-07T08:19:12.4413793Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.4414332Z Loads the Torch serialized object at the given URL. 2025-09-07T08:19:12.4414660Z 2025-09-07T08:19:12.4414853Z If downloaded file is a zip file, it will be automatically 2025-09-07T08:19:12.4415234Z decompressed. 2025-09-07T08:19:12.4415391Z 2025-09-07T08:19:12.4415603Z If the object is already present in `model_dir`, it's deserialized and 2025-09-07T08:19:12.4416021Z returned. 2025-09-07T08:19:12.4416364Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-09-07T08:19:12.4416891Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-09-07T08:19:12.4417201Z 2025-09-07T08:19:12.4417283Z Args: 2025-09-07T08:19:12.4417589Z url (str): URL of the object to download 2025-09-07T08:19:12.4418025Z model_dir (str, optional): directory in which to save the object 2025-09-07T08:19:12.4418689Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-09-07T08:19:12.4419399Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-09-07T08:19:12.4419881Z Default: True 2025-09-07T08:19:12.4420384Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-09-07T08:19:12.4421050Z ``filename-.ext`` where ```` is the first eight or more 2025-09-07T08:19:12.4421619Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-09-07T08:19:12.4422154Z ensure unique names and to verify the contents of the file. 2025-09-07T08:19:12.4422560Z Default: False 2025-09-07T08:19:12.4423075Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-09-07T08:19:12.4423861Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-09-07T08:19:12.4424554Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-09-07T08:19:12.4424947Z 2025-09-07T08:19:12.4425035Z Example: 2025-09-07T08:19:12.4425312Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T08:19:12.4425717Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-09-07T08:19:12.4426206Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-09-07T08:19:12.4426613Z ... ) 2025-09-07T08:19:12.4426752Z 2025-09-07T08:19:12.4426830Z 2025-09-07T08:19:12.4427196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.4427590Z 2025-09-07T08:19:12.4455613Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=375. 2025-09-07T08:19:12.4456486Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:12.4457083Z Registers the function implementation as the fallback for the given key. 2025-09-07T08:19:12.4457452Z 2025-09-07T08:19:12.4457668Z This function only works for a library with global namespace ("_"). 2025-09-07T08:19:12.4458013Z 2025-09-07T08:19:12.4458095Z Args: 2025-09-07T08:19:12.4458502Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-09-07T08:19:12.4459018Z to register a fallthrough. 2025-09-07T08:19:12.4459551Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-09-07T08:19:12.4460161Z the dispatch key that the library was created with. 2025-09-07T08:19:12.4460817Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-09-07T08:19:12.4461693Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-09-07T08:19:12.4462131Z 2025-09-07T08:19:12.4462253Z Example:: 2025-09-07T08:19:12.4462393Z 2025-09-07T08:19:12.4462509Z >>> my_lib = Library("_", "IMPL") 2025-09-07T08:19:12.4462864Z >>> def fallback_kernel(op, *args, **kwargs): 2025-09-07T08:19:12.4463248Z >>> # Handle all autocast ops generically 2025-09-07T08:19:12.4463587Z >>> # ... 2025-09-07T08:19:12.4463880Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T08:19:12.4464229Z 2025-09-07T08:19:12.4464984Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-09-07T08:19:12.4465729Z 2025-09-07T08:19:12.4465930Z my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T08:19:12.4466257Z ^ 2025-09-07T08:19:12.4536404Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=948. 2025-09-07T08:19:12.4537454Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:12.4538053Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-09-07T08:19:12.4538410Z 2025-09-07T08:19:12.4538588Z Also sometimes known as a "meta kernel", "abstract impl". 2025-09-07T08:19:12.4538881Z 2025-09-07T08:19:12.4539139Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-09-07T08:19:12.4539716Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-09-07T08:19:12.4540309Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-09-07T08:19:12.4540809Z what the properties of the output Tensors are. 2025-09-07T08:19:12.4541066Z 2025-09-07T08:19:12.4541312Z The FakeTensor implementation has the same signature as the operator. 2025-09-07T08:19:12.4541875Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-09-07T08:19:12.4542410Z implementation, assume that all Tensor inputs to the operator are 2025-09-07T08:19:12.4542957Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-09-07T08:19:12.4543488Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-09-07T08:19:12.4544046Z The FakeTensor implementation must consist of only PyTorch operations 2025-09-07T08:19:12.4544589Z (and may not directly access the storage or data of any input or 2025-09-07T08:19:12.4544989Z intermediate Tensors). 2025-09-07T08:19:12.4545173Z 2025-09-07T08:19:12.4545323Z This API may be used as a decorator (see examples). 2025-09-07T08:19:12.4545698Z 2025-09-07T08:19:12.4545837Z For a detailed guide on custom ops, please see 2025-09-07T08:19:12.4546343Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-09-07T08:19:12.4546689Z 2025-09-07T08:19:12.4546772Z Args: 2025-09-07T08:19:12.4547116Z op_name: Operator name (along with the overload) or OpOverload object. 2025-09-07T08:19:12.4547701Z func: Fake tensor implementation. 2025-09-07T08:19:12.4548496Z lib (Optional[Library]): Library to register the fake tensor to. 2025-09-07T08:19:12.4549424Z allow_override: Flag controlling if we want to override an 2025-09-07T08:19:12.4549958Z existing registered fake impl. This is by default off, 2025-09-07T08:19:12.4550424Z and will error you're trying to register a fake impl to 2025-09-07T08:19:12.4550895Z an operator that already has a fake impl. This also only 2025-09-07T08:19:12.4551359Z applies if the custom operator was not created via 2025-09-07T08:19:12.4551834Z torch.library.custom_op, as overriding and existing fake 2025-09-07T08:19:12.4552350Z impl is already allowed. 2025-09-07T08:19:12.4552572Z 2025-09-07T08:19:12.4552657Z Examples: 2025-09-07T08:19:12.4552886Z >>> import torch 2025-09-07T08:19:12.4553158Z >>> import numpy as np 2025-09-07T08:19:12.4553448Z >>> from torch import Tensor 2025-09-07T08:19:12.4553726Z >>> 2025-09-07T08:19:12.4554039Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T08:19:12.4554575Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-09-07T08:19:12.4555136Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T08:19:12.4555646Z >>> raise NotImplementedError("Implementation goes here") 2025-09-07T08:19:12.4556032Z >>> 2025-09-07T08:19:12.4556333Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-09-07T08:19:12.4556804Z >>> def _(x, weight, bias): 2025-09-07T08:19:12.4557104Z >>> assert x.dim() == 2 2025-09-07T08:19:12.4557414Z >>> assert weight.dim() == 2 2025-09-07T08:19:12.4557737Z >>> assert bias.dim() == 1 2025-09-07T08:19:12.4558072Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T08:19:12.4558429Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T08:19:12.4558793Z >>> assert x.device == weight.device 2025-09-07T08:19:12.4559108Z >>> 2025-09-07T08:19:12.4559345Z >>> return (x @ weight.t()) + bias 2025-09-07T08:19:12.4559641Z >>> 2025-09-07T08:19:12.4559933Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-09-07T08:19:12.4560339Z >>> x = torch.randn(2, 3) 2025-09-07T08:19:12.4560651Z >>> w = torch.randn(3, 3) 2025-09-07T08:19:12.4560947Z >>> b = torch.randn(3) 2025-09-07T08:19:12.4561284Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-09-07T08:19:12.4561630Z >>> 2025-09-07T08:19:12.4561861Z >>> assert y.shape == (2, 3) 2025-09-07T08:19:12.4562139Z >>> 2025-09-07T08:19:12.4562442Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T08:19:12.4562966Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-09-07T08:19:12.4563439Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-09-07T08:19:12.4563797Z >>> x_np = x.numpy(force=True) 2025-09-07T08:19:12.4564216Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T08:19:12.4564600Z >>> return torch.tensor(res, device=x.device) 2025-09-07T08:19:12.4564944Z >>> 2025-09-07T08:19:12.4565247Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-09-07T08:19:12.4565617Z >>> def _(x): 2025-09-07T08:19:12.4565991Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T08:19:12.4566419Z >>> # Since we cannot peek at the data in an fake impl, 2025-09-07T08:19:12.4566856Z >>> # we use the ctx object to construct a new symint that 2025-09-07T08:19:12.4567252Z >>> # represents the data-dependent size. 2025-09-07T08:19:12.4567618Z >>> ctx = torch.library.get_ctx() 2025-09-07T08:19:12.4567961Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T08:19:12.4568292Z >>> shape = [nnz, x.dim()] 2025-09-07T08:19:12.4568638Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T08:19:12.4569002Z >>> return result 2025-09-07T08:19:12.4569264Z >>> 2025-09-07T08:19:12.4569565Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T08:19:12.4569931Z >>> 2025-09-07T08:19:12.4570166Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-09-07T08:19:12.4570635Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-09-07T08:19:12.4571117Z >>> trace.print_readable() 2025-09-07T08:19:12.4571402Z >>> 2025-09-07T08:19:12.4571741Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-09-07T08:19:12.4572133Z 2025-09-07T08:19:12.4572213Z 2025-09-07T08:19:12.4572863Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-09-07T08:19:12.4573707Z 2025-09-07T08:19:12.4573807Z _._ = None 2025-09-07T08:19:12.4574002Z ^ 2025-09-07T08:19:12.4574651Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1083. 2025-09-07T08:19:12.4575529Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.4576055Z Register a backward formula for this custom op. 2025-09-07T08:19:12.4576306Z 2025-09-07T08:19:12.4576523Z In order for an operator to work with autograd, you need to register 2025-09-07T08:19:12.4576943Z a backward formula: 2025-09-07T08:19:12.4577389Z 1. You must tell us how to compute gradients during the backward pass 2025-09-07T08:19:12.4577837Z by providing us a "backward" function. 2025-09-07T08:19:12.4578276Z 2. If you need any values from the forward to compute gradients, you can 2025-09-07T08:19:12.4578759Z use `setup_context` to save values for backward. 2025-09-07T08:19:12.4579011Z 2025-09-07T08:19:12.4579250Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-09-07T08:19:12.4579782Z - ``grads`` is one or more gradients. The number of gradients matches 2025-09-07T08:19:12.4580224Z the number of outputs of the operator. 2025-09-07T08:19:12.4580670Z The ``ctx`` object is `the same ctx object `_ used by 2025-09-07T08:19:12.4581245Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-09-07T08:19:12.4581756Z same as :meth:`torch.autograd.Function.backward`. 2025-09-07T08:19:12.4582030Z 2025-09-07T08:19:12.4582242Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-09-07T08:19:12.4582803Z Please save quantities needed for backward onto the ``ctx`` object via 2025-09-07T08:19:12.4583381Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-09-07T08:19:12.4583932Z or assigning them as attributes of ``ctx``. If your custom op has 2025-09-07T08:19:12.4584448Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-09-07T08:19:12.4584967Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-09-07T08:19:12.4585283Z 2025-09-07T08:19:12.4585497Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-09-07T08:19:12.4586056Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-09-07T08:19:12.4586623Z not depend on or mutate global state. If you need a non-traceable backward, 2025-09-07T08:19:12.4587235Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-09-07T08:19:12.4587590Z 2025-09-07T08:19:12.4587804Z If you need different autograd behavior on different devices, then we 2025-09-07T08:19:12.4588371Z recommend creating two different custom operators, one for each device 2025-09-07T08:19:12.4588943Z that needs different behavior, and switching between them at runtime. 2025-09-07T08:19:12.4589277Z 2025-09-07T08:19:12.4589365Z Examples: 2025-09-07T08:19:12.4589593Z >>> import torch 2025-09-07T08:19:12.4589860Z >>> import numpy as np 2025-09-07T08:19:12.4590150Z >>> from torch import Tensor 2025-09-07T08:19:12.4590428Z >>> 2025-09-07T08:19:12.4590760Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-09-07T08:19:12.4591197Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-09-07T08:19:12.4591529Z >>> x_np = x.cpu().numpy() 2025-09-07T08:19:12.4591827Z >>> y_np = np.sin(x_np) 2025-09-07T08:19:12.4592187Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T08:19:12.4592582Z >>> 2025-09-07T08:19:12.4592858Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-09-07T08:19:12.4593221Z >>> x, = inputs 2025-09-07T08:19:12.4593489Z >>> ctx.save_for_backward(x) 2025-09-07T08:19:12.4593785Z >>> 2025-09-07T08:19:12.4594016Z >>> def backward(ctx, grad): 2025-09-07T08:19:12.4594313Z >>> x, = ctx.saved_tensors 2025-09-07T08:19:12.4594627Z >>> return grad * x.cos() 2025-09-07T08:19:12.4594914Z >>> 2025-09-07T08:19:12.4595163Z >>> torch.library.register_autograd( 2025-09-07T08:19:12.4595576Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-09-07T08:19:12.4595944Z ... ) 2025-09-07T08:19:12.4596157Z >>> 2025-09-07T08:19:12.4596402Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T08:19:12.4596745Z >>> y = numpy_sin(x) 2025-09-07T08:19:12.4597139Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T08:19:12.4597556Z >>> assert torch.allclose(grad_x, x.cos()) 2025-09-07T08:19:12.4597882Z >>> 2025-09-07T08:19:12.4598123Z >>> # Example with a keyword-only arg 2025-09-07T08:19:12.4598545Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T08:19:12.4599024Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-09-07T08:19:12.4599400Z >>> x_np = x.cpu().numpy() 2025-09-07T08:19:12.4599713Z >>> y_np = x_np * val 2025-09-07T08:19:12.4600052Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T08:19:12.4600457Z >>> 2025-09-07T08:19:12.4600955Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-09-07T08:19:12.4601504Z >>> ctx.val = keyword_only_inputs["val"] 2025-09-07T08:19:12.4601986Z >>> 2025-09-07T08:19:12.4602390Z >>> def backward(ctx, grad): 2025-09-07T08:19:12.4602946Z >>> return grad * ctx.val 2025-09-07T08:19:12.4603242Z >>> 2025-09-07T08:19:12.4603478Z >>> torch.library.register_autograd( 2025-09-07T08:19:12.4603896Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-09-07T08:19:12.4604357Z ... ) 2025-09-07T08:19:12.4604577Z >>> 2025-09-07T08:19:12.4604814Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T08:19:12.4605165Z >>> y = numpy_mul(x, val=3.14) 2025-09-07T08:19:12.4605550Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T08:19:12.4606021Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-09-07T08:19:12.4606308Z 2025-09-07T08:19:12.4606406Z 2025-09-07T08:19:12.4606759Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.4607203Z 2025-09-07T08:19:12.4607696Z msg = Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1482. 2025-09-07T08:19:12.4608526Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:12.4609109Z Returns the computed kernel for a given operator and dispatch key. 2025-09-07T08:19:12.4609434Z 2025-09-07T08:19:12.4609670Z This function retrieves the kernel that would be executed for a given 2025-09-07T08:19:12.4610232Z operator and dispatch key combination. The returned SafeKernelFunction 2025-09-07T08:19:12.4610778Z can be used to call the kernel in a boxed fashion. The intended use 2025-09-07T08:19:12.4611300Z case for this function is to retrieve the original kernel for a given 2025-09-07T08:19:12.4611854Z dispatch key and then register another kernel to the same dispatch key 2025-09-07T08:19:12.4612359Z that calls into the original kernel for certain cases. 2025-09-07T08:19:12.4612634Z 2025-09-07T08:19:12.4612717Z Args: 2025-09-07T08:19:12.4613045Z op: Operator name (along with the overload) or OpOverload object 2025-09-07T08:19:12.4613595Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-09-07T08:19:12.4614507Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-09-07T08:19:12.4615294Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-09-07T08:19:12.4615632Z 2025-09-07T08:19:12.4615764Z Returns: 2025-09-07T08:19:12.4616276Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-09-07T08:19:12.4616737Z call the kernel. 2025-09-07T08:19:12.4616915Z 2025-09-07T08:19:12.4617011Z Raises: 2025-09-07T08:19:12.4617270Z RuntimeError: If the operator does not exist. 2025-09-07T08:19:12.4617535Z 2025-09-07T08:19:12.4617620Z Example: 2025-09-07T08:19:12.4617866Z >>> # Get the CPU kernel for torch.add 2025-09-07T08:19:12.4618299Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-09-07T08:19:12.4618681Z >>> 2025-09-07T08:19:12.4618999Z >>> # You can also use DispatchKey enum 2025-09-07T08:19:12.4619502Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-09-07T08:19:12.4619983Z >>> 2025-09-07T08:19:12.4620210Z >>> # Or use an OpOverload directly 2025-09-07T08:19:12.4620665Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-09-07T08:19:12.4621094Z >>> 2025-09-07T08:19:12.4621432Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-09-07T08:19:12.4621891Z >>> # Get the original kernel for torch.sin 2025-09-07T08:19:12.4624051Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-09-07T08:19:12.4624483Z >>> 2025-09-07T08:19:12.4624834Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-09-07T08:19:12.4625332Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-09-07T08:19:12.4625676Z >>> if (x < 0).any(): 2025-09-07T08:19:12.4626052Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-09-07T08:19:12.4626442Z >>> else: 2025-09-07T08:19:12.4626710Z >>> return torch.zeros_like(x) 2025-09-07T08:19:12.4627007Z >>> 2025-09-07T08:19:12.4627269Z >>> lib = torch.library.Library("aten", "IMPL") 2025-09-07T08:19:12.4627781Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-09-07T08:19:12.4628349Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T08:19:12.4628847Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-09-07T08:19:12.4629229Z >>> 2025-09-07T08:19:12.4629463Z >>> # Test the conditional behavior 2025-09-07T08:19:12.4629869Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-09-07T08:19:12.4630220Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-09-07T08:19:12.4630546Z >>> torch.sin(x_positive) 2025-09-07T08:19:12.4630844Z tensor([0., 0.]) 2025-09-07T08:19:12.4631109Z >>> torch.sin(x_mixed) 2025-09-07T08:19:12.4631390Z tensor([-0.8415, 0.9093]) 2025-09-07T08:19:12.4631649Z 2025-09-07T08:19:12.4632250Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-09-07T08:19:12.4632851Z 2025-09-07T08:19:12.4633029Z which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T08:19:12.4633415Z ^ 2025-09-07T08:19:12.4634025Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1571. 2025-09-07T08:19:12.4634864Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.4635454Z Given an operator and some sample arguments, tests if the operator is 2025-09-07T08:19:12.4635888Z registered correctly. 2025-09-07T08:19:12.4636059Z 2025-09-07T08:19:12.4636324Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-09-07T08:19:12.4636896Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-09-07T08:19:12.4637472Z and these APIs require that the functions you pass them satisfy certain 2025-09-07T08:19:12.4638050Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-09-07T08:19:12.4638544Z ``opcheck`` tests these metadata and properties. 2025-09-07T08:19:12.4638800Z 2025-09-07T08:19:12.4638927Z Concretely, we test the following: 2025-09-07T08:19:12.4639143Z 2025-09-07T08:19:12.4639343Z - test_schema: If the schema matches the implementation of 2025-09-07T08:19:12.4639850Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-09-07T08:19:12.4640408Z then we check the implementation mutates the Tensor. If the schema 2025-09-07T08:19:12.4641001Z specifies that we return a new Tensor, then we check that the 2025-09-07T08:19:12.4641532Z implementation returns a new Tensor (instead of an existing one or 2025-09-07T08:19:12.4641964Z a view of an existing one). 2025-09-07T08:19:12.4642373Z - test_autograd_registration: If the operator supports training 2025-09-07T08:19:12.4642893Z (autograd): we check that its autograd formula is registered via 2025-09-07T08:19:12.4643425Z torch.library.register_autograd or a manual registration to one 2025-09-07T08:19:12.4643968Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-09-07T08:19:12.4644496Z registrations may lead to undefined behavior. 2025-09-07T08:19:12.4644937Z - test_faketensor: If the operator has a FakeTensor kernel 2025-09-07T08:19:12.4645414Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-09-07T08:19:12.4645933Z but not sufficient) for the operator to work with PyTorch compilation 2025-09-07T08:19:12.4646480Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-09-07T08:19:12.4647014Z (also sometimes known as a meta kernel) was registered for the 2025-09-07T08:19:12.4647518Z operator and that it is correct. This test takes the result of 2025-09-07T08:19:12.4648023Z running the operator on real tensors and the result of running 2025-09-07T08:19:12.4648528Z the operator on FakeTensors and checks that they have the same 2025-09-07T08:19:12.4648983Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-09-07T08:19:12.4649450Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-09-07T08:19:12.4649948Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-09-07T08:19:12.4650450Z This checks that the outputs (and gradients, if applicable) are the 2025-09-07T08:19:12.4650917Z same under eager-mode PyTorch and torch.compile. 2025-09-07T08:19:12.4651403Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-09-07T08:19:12.4651882Z other things it tests are that the operator supports 2025-09-07T08:19:12.4652376Z functionalization and that the backward pass (if it exists) also 2025-09-07T08:19:12.4652846Z supports FakeTensor and functionalization. 2025-09-07T08:19:12.4653091Z 2025-09-07T08:19:12.4653286Z For best results, please call ``opcheck`` multiple times with a 2025-09-07T08:19:12.4653785Z representative set of inputs. If your operator supports 2025-09-07T08:19:12.4654310Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-09-07T08:19:12.4654889Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-09-07T08:19:12.4655378Z use ``opcheck`` with inputs on all supported devices. 2025-09-07T08:19:12.4655658Z 2025-09-07T08:19:12.4655743Z Args: 2025-09-07T08:19:12.4656047Z op: The operator. Must either be a function decorated with 2025-09-07T08:19:12.4656561Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-09-07T08:19:12.4657117Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-09-07T08:19:12.4657582Z args: The args to the operator 2025-09-07T08:19:12.4657914Z kwargs: The kwargs to the operator 2025-09-07T08:19:12.4658316Z test_utils: Tests that we should run. Default: all of them. 2025-09-07T08:19:12.4658752Z Example: ("test_schema", "test_faketensor") 2025-09-07T08:19:12.4659181Z raise_exception: If we should raise an exception on the first 2025-09-07T08:19:12.4659659Z error. If False, we will return a dict with information 2025-09-07T08:19:12.4660056Z on if each test passed or not. 2025-09-07T08:19:12.4660518Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-09-07T08:19:12.4661009Z If specified ``atol`` must also be specified. 2025-09-07T08:19:12.4661456Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T08:19:12.4661988Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T08:19:12.4662515Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-09-07T08:19:12.4663016Z If specified ``rtol`` must also be specified. 2025-09-07T08:19:12.4663445Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T08:19:12.4663922Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T08:19:12.4664210Z 2025-09-07T08:19:12.4664301Z .. warning:: 2025-09-07T08:19:12.4664438Z 2025-09-07T08:19:12.4664673Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-09-07T08:19:12.4665203Z opcheck tests if your usage of torch.library APIs is correct while 2025-09-07T08:19:12.4665744Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-09-07T08:19:12.4666298Z mathematically correct. Use both to test custom ops that support 2025-09-07T08:19:12.4666743Z gradient computation. 2025-09-07T08:19:12.4666930Z 2025-09-07T08:19:12.4667026Z Example: 2025-09-07T08:19:12.4667151Z 2025-09-07T08:19:12.4667285Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:12.4667738Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T08:19:12.4668197Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-09-07T08:19:12.4668560Z >>> x_np = x.numpy(force=True) 2025-09-07T08:19:12.4668861Z >>> z_np = x_np * y 2025-09-07T08:19:12.4669181Z >>> return torch.from_numpy(z_np).to(x.device) 2025-09-07T08:19:12.4669514Z >>> 2025-09-07T08:19:12.4669746Z >>> @numpy_mul.register_fake 2025-09-07T08:19:12.4670036Z >>> def _(x, y): 2025-09-07T08:19:12.4670310Z >>> return torch.empty_like(x) 2025-09-07T08:19:12.4670643Z >>> 2025-09-07T08:19:12.4670890Z >>> def setup_context(ctx, inputs, output): 2025-09-07T08:19:12.4671215Z >>> y, = inputs 2025-09-07T08:19:12.4671479Z >>> ctx.y = y 2025-09-07T08:19:12.4671732Z >>> 2025-09-07T08:19:12.4671960Z >>> def backward(ctx, grad): 2025-09-07T08:19:12.4672260Z >>> return grad * ctx.y, None 2025-09-07T08:19:12.4672560Z >>> 2025-09-07T08:19:12.4672907Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-09-07T08:19:12.4673491Z >>> 2025-09-07T08:19:12.4673704Z >>> sample_inputs = [ 2025-09-07T08:19:12.4673996Z >>> (torch.randn(3), 3.14), 2025-09-07T08:19:12.4674340Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-09-07T08:19:12.4674734Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-09-07T08:19:12.4675189Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-09-07T08:19:12.4675576Z >>> ] 2025-09-07T08:19:12.4675792Z >>> 2025-09-07T08:19:12.4676021Z >>> for args in sample_inputs: 2025-09-07T08:19:12.4676447Z >>> torch.library.opcheck(numpy_mul, args) 2025-09-07T08:19:12.4676791Z 2025-09-07T08:19:12.4676903Z 2025-09-07T08:19:12.4688747Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.4689156Z 2025-09-07T08:19:12.5054404Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1285. 2025-09-07T08:19:12.5055436Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.5056231Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-09-07T08:19:12.5056715Z 2025-09-07T08:19:12.5056938Z Loads an object saved with :func:`torch.save` from a file. 2025-09-07T08:19:12.5057230Z 2025-09-07T08:19:12.5057506Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-09-07T08:19:12.5058122Z which underlie tensors, specially. They are first deserialized on the 2025-09-07T08:19:12.5058943Z CPU and are then moved to the device they were saved from. If this fails 2025-09-07T08:19:12.5059603Z (e.g. because the run time system doesn't have certain devices), an exception 2025-09-07T08:19:12.5060266Z is raised. However, storages can be dynamically remapped to an alternative 2025-09-07T08:19:12.5060855Z set of devices using the :attr:`map_location` argument. 2025-09-07T08:19:12.5061134Z 2025-09-07T08:19:12.5061399Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-09-07T08:19:12.5062031Z storage with two arguments: storage and location. The storage argument 2025-09-07T08:19:12.5062677Z will be the initial deserialization of the storage, residing on the CPU. 2025-09-07T08:19:12.5063313Z Each serialized storage has a location tag associated with it which 2025-09-07T08:19:12.5063930Z identifies the device it was saved from, and this tag is the second 2025-09-07T08:19:12.5064536Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-09-07T08:19:12.5065176Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-09-07T08:19:12.5065791Z :attr:`map_location` should return either ``None`` or a storage. If 2025-09-07T08:19:12.5066428Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-09-07T08:19:12.5067105Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-09-07T08:19:12.5067783Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-09-07T08:19:12.5068162Z 2025-09-07T08:19:12.5068406Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-09-07T08:19:12.5069054Z a device tag, it indicates the location where all tensors should be loaded. 2025-09-07T08:19:12.5069528Z 2025-09-07T08:19:12.5069799Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-09-07T08:19:12.5070455Z appearing in the file (keys), to ones that specify where to put the 2025-09-07T08:19:12.5070942Z storages (values). 2025-09-07T08:19:12.5071113Z 2025-09-07T08:19:12.5071331Z User extensions can register their own location tags and tagging and 2025-09-07T08:19:12.5072007Z deserialization methods using :func:`torch.serialization.register_package`. 2025-09-07T08:19:12.5072445Z 2025-09-07T08:19:12.5072703Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-09-07T08:19:12.5073061Z 2025-09-07T08:19:12.5073198Z Args: 2025-09-07T08:19:12.5073796Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-09-07T08:19:12.5074485Z or a string or os.PathLike object containing a file name 2025-09-07T08:19:12.5075168Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-09-07T08:19:12.5075781Z locations 2025-09-07T08:19:12.5076162Z pickle_module: module used for unpickling metadata and objects (has to 2025-09-07T08:19:12.5076814Z match the :attr:`pickle_module` used to serialize file) 2025-09-07T08:19:12.5077383Z weights_only: Indicates whether unpickler should be restricted to 2025-09-07T08:19:12.5077943Z loading only tensors, primitive types, dictionaries 2025-09-07T08:19:12.5078433Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-09-07T08:19:12.5078981Z See :ref:`weights-only` for more details. 2025-09-07T08:19:12.5079632Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-09-07T08:19:12.5080502Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-09-07T08:19:12.5081366Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-09-07T08:19:12.5082290Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-09-07T08:19:12.5083142Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-09-07T08:19:12.5083816Z will be lazily loaded when their data is accessed. 2025-09-07T08:19:12.5084452Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-09-07T08:19:12.5085138Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-09-07T08:19:12.5085586Z :attr:`errors=...`. 2025-09-07T08:19:12.5085835Z 2025-09-07T08:19:12.5085937Z .. warning:: 2025-09-07T08:19:12.5086294Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-09-07T08:19:12.5086901Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-09-07T08:19:12.5087560Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-09-07T08:19:12.5088254Z during unpickling. Never load data that could have come from an untrusted 2025-09-07T08:19:12.5088977Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-09-07T08:19:12.5089404Z 2025-09-07T08:19:12.5089489Z .. note:: 2025-09-07T08:19:12.5089945Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-09-07T08:19:12.5090653Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-09-07T08:19:12.5091364Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-09-07T08:19:12.5091767Z 2025-09-07T08:19:12.5091853Z .. note:: 2025-09-07T08:19:12.5092297Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-09-07T08:19:12.5093036Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-09-07T08:19:12.5093588Z when loading files saved by Python 2 in Python 3. If this default 2025-09-07T08:19:12.5094159Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-09-07T08:19:12.5094851Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-09-07T08:19:12.5095457Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-09-07T08:19:12.5096046Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-09-07T08:19:12.5096392Z 2025-09-07T08:19:12.5096526Z Example: 2025-09-07T08:19:12.5096805Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-09-07T08:19:12.5097193Z >>> torch.load("tensors.pt", weights_only=True) 2025-09-07T08:19:12.5097557Z # Load all tensors onto the CPU 2025-09-07T08:19:12.5097875Z >>> torch.load( 2025-09-07T08:19:12.5098120Z ... "tensors.pt", 2025-09-07T08:19:12.5098436Z ... map_location=torch.device("cpu"), 2025-09-07T08:19:12.5098819Z ... weights_only=True, 2025-09-07T08:19:12.5099105Z ... ) 2025-09-07T08:19:12.5099370Z # Load all tensors onto the CPU, using a function 2025-09-07T08:19:12.5099723Z >>> torch.load( 2025-09-07T08:19:12.5099982Z ... "tensors.pt", 2025-09-07T08:19:12.5100366Z ... map_location=lambda storage, loc: storage, 2025-09-07T08:19:12.5100713Z ... weights_only=True, 2025-09-07T08:19:12.5100997Z ... ) 2025-09-07T08:19:12.5101239Z # Load all tensors onto GPU 1 2025-09-07T08:19:12.5101544Z >>> torch.load( 2025-09-07T08:19:12.5101786Z ... "tensors.pt", 2025-09-07T08:19:12.5102130Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-09-07T08:19:12.5102510Z ... weights_only=True, 2025-09-07T08:19:12.5102822Z ... ) # type: ignore[attr-defined] 2025-09-07T08:19:12.5103207Z # Map tensors from GPU 1 to GPU 0 2025-09-07T08:19:12.5103527Z >>> torch.load( 2025-09-07T08:19:12.5103786Z ... "tensors.pt", 2025-09-07T08:19:12.5104093Z ... map_location={"cuda:1": "cuda:0"}, 2025-09-07T08:19:12.5104419Z ... weights_only=True, 2025-09-07T08:19:12.5104701Z ... ) 2025-09-07T08:19:12.5104953Z # Load tensor from io.BytesIO object 2025-09-07T08:19:12.5105431Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-09-07T08:19:12.5105907Z >>> with open("tensor.pt", "rb") as f: 2025-09-07T08:19:12.5106253Z ... buffer = io.BytesIO(f.read()) 2025-09-07T08:19:12.5106605Z >>> torch.load(buffer, weights_only=False) 2025-09-07T08:19:12.5106999Z # Load a module with 'ascii' encoding for unpickling 2025-09-07T08:19:12.5107563Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-09-07T08:19:12.5108140Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-09-07T08:19:12.5108543Z 2025-09-07T08:19:12.5108914Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.5109280Z 2025-09-07T08:19:12.5632229Z msg = Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py line=1877. 2025-09-07T08:19:12.5633258Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.5633850Z Computes the minimum storage size to hold the given tensor geometry. 2025-09-07T08:19:12.5634197Z 2025-09-07T08:19:12.5634283Z Example 2025-09-07T08:19:12.5634496Z ======= 2025-09-07T08:19:12.5634616Z 2025-09-07T08:19:12.5634855Z This is the size of a newly allocated tensor's storage, in units of elements 2025-09-07T08:19:12.5635343Z 2025-09-07T08:19:12.5635448Z >>> t = torch.empty((10, 20)) 2025-09-07T08:19:12.5635896Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-09-07T08:19:12.5636348Z 200 2025-09-07T08:19:12.5636462Z 2025-09-07T08:19:12.5636575Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:12.5636909Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-09-07T08:19:12.5637266Z >>> size = compute_required_storage_length( 2025-09-07T08:19:12.5637639Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-09-07T08:19:12.5637970Z ... ) 2025-09-07T08:19:12.5638194Z >>> size == t.storage().size() 2025-09-07T08:19:12.5638467Z True 2025-09-07T08:19:12.5638596Z 2025-09-07T08:19:12.5638791Z A valid tensor may have a larger storage size, but never smaller 2025-09-07T08:19:12.5639101Z 2025-09-07T08:19:12.5639220Z >>> slice = torch.empty(100)[20:40] 2025-09-07T08:19:12.5639547Z >>> slice.storage().size() 2025-09-07T08:19:12.5639807Z 100 2025-09-07T08:19:12.5639935Z 2025-09-07T08:19:12.5640046Z >>> compute_required_storage_length( 2025-09-07T08:19:12.5640442Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-09-07T08:19:12.5640867Z ... ) 2025-09-07T08:19:12.5641061Z 40 2025-09-07T08:19:12.5641190Z 2025-09-07T08:19:12.5641269Z 2025-09-07T08:19:12.5641637Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.5642021Z 2025-09-07T08:19:12.6210676Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=66. 2025-09-07T08:19:12.6211707Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:12.6212348Z Check if the current accelerator is available at runtime: it was build, all the 2025-09-07T08:19:12.6212998Z required drivers are available and at least one device is visible. 2025-09-07T08:19:12.6213551Z See :ref:`accelerator` for details. 2025-09-07T08:19:12.6213826Z 2025-09-07T08:19:12.6213912Z Returns: 2025-09-07T08:19:12.6214591Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-09-07T08:19:12.6215073Z 2025-09-07T08:19:12.6215351Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-09-07T08:19:12.6216031Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-09-07T08:19:12.6216733Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-09-07T08:19:12.6217303Z :ref:`multiprocessing-poison-fork-note`. 2025-09-07T08:19:12.6217555Z 2025-09-07T08:19:12.6217675Z Example:: 2025-09-07T08:19:12.6217803Z 2025-09-07T08:19:12.6218152Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T08:19:12.6218611Z 2025-09-07T08:19:12.6219341Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-09-07T08:19:12.6220100Z 2025-09-07T08:19:12.6220429Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T08:19:12.6220908Z ^ 2025-09-07T08:19:12.6227366Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=212. 2025-09-07T08:19:12.6228312Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:12.6228947Z Wait for all kernels in all streams on the given device to complete. 2025-09-07T08:19:12.6229277Z 2025-09-07T08:19:12.6229359Z Args: 2025-09-07T08:19:12.6229863Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-09-07T08:19:12.6230592Z the current :ref:`accelerator` device type. If not given, 2025-09-07T08:19:12.6231200Z use :func:`torch.accelerator.current_device_index` by default. 2025-09-07T08:19:12.6231645Z 2025-09-07T08:19:12.6232033Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-09-07T08:19:12.6232482Z 2025-09-07T08:19:12.6232582Z Example:: 2025-09-07T08:19:12.6232766Z 2025-09-07T08:19:12.6232916Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:12.6233500Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T08:19:12.6234033Z >>> start_event = torch.Event(enable_timing=True) 2025-09-07T08:19:12.6234493Z >>> end_event = torch.Event(enable_timing=True) 2025-09-07T08:19:12.6234849Z >>> start_event.record() 2025-09-07T08:19:12.6235348Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-09-07T08:19:12.6235875Z >>> sum = torch.sum(tensor) 2025-09-07T08:19:12.6236171Z >>> end_event.record() 2025-09-07T08:19:12.6236499Z >>> torch.accelerator.synchronize() 2025-09-07T08:19:12.6236953Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-09-07T08:19:12.6237375Z 2025-09-07T08:19:12.6238167Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-09-07T08:19:12.6238878Z 2025-09-07T08:19:12.6239176Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T08:19:12.6239706Z ^ 2025-09-07T08:19:12.6505664Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=434. 2025-09-07T08:19:12.6506654Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:12.6507143Z Retrieves the CUDA runtime API module. 2025-09-07T08:19:12.6507411Z 2025-09-07T08:19:12.6507418Z 2025-09-07T08:19:12.6507688Z This function initializes the CUDA runtime environment if it is not already 2025-09-07T08:19:12.6508532Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-09-07T08:19:12.6509187Z runtime API module provides access to various CUDA runtime functions. 2025-09-07T08:19:12.6509527Z 2025-09-07T08:19:12.6509621Z Args: 2025-09-07T08:19:12.6509877Z ``None`` 2025-09-07T08:19:12.6510025Z 2025-09-07T08:19:12.6510111Z Returns: 2025-09-07T08:19:12.6510383Z module: The CUDA runtime API module (_cudart). 2025-09-07T08:19:12.6510699Z 2025-09-07T08:19:12.6510793Z Raises: 2025-09-07T08:19:12.6511150Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-09-07T08:19:12.6511916Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-09-07T08:19:12.6512471Z 2025-09-07T08:19:12.6512603Z Example of CUDA operations with profiling: 2025-09-07T08:19:12.6513001Z >>> import torch 2025-09-07T08:19:12.6513308Z >>> from torch.cuda import cudart, check_error 2025-09-07T08:19:12.6513676Z >>> import os 2025-09-07T08:19:12.6513939Z >>> 2025-09-07T08:19:12.6514180Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-09-07T08:19:12.6514547Z >>> 2025-09-07T08:19:12.6514797Z >>> def perform_cuda_operations_with_streams(): 2025-09-07T08:19:12.6515181Z >>> stream = torch.cuda.Stream() 2025-09-07T08:19:12.6515570Z >>> with torch.cuda.stream(stream): 2025-09-07T08:19:12.6515939Z >>> x = torch.randn(100, 100, device='cuda') 2025-09-07T08:19:12.6516351Z >>> y = torch.randn(100, 100, device='cuda') 2025-09-07T08:19:12.6516696Z >>> z = torch.mul(x, y) 2025-09-07T08:19:12.6517063Z >>> return z 2025-09-07T08:19:12.6517307Z >>> 2025-09-07T08:19:12.6517561Z >>> torch.cuda.synchronize() 2025-09-07T08:19:12.6517928Z >>> print("====== Start nsys profiling ======") 2025-09-07T08:19:12.6518451Z >>> check_error(cudart().cudaProfilerStart()) 2025-09-07T08:19:12.6518851Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-09-07T08:19:12.6519309Z >>> result = perform_cuda_operations_with_streams() 2025-09-07T08:19:12.6519712Z >>> print("CUDA operations completed.") 2025-09-07T08:19:12.6520180Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-09-07T08:19:12.6520589Z >>> print("====== End nsys profiling ======") 2025-09-07T08:19:12.6520894Z 2025-09-07T08:19:12.6521103Z To run this example and save the profiling information, execute: 2025-09-07T08:19:12.6521838Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T08:19:12.6522386Z 2025-09-07T08:19:12.6522629Z This command profiles the CUDA operations in the provided script and saves 2025-09-07T08:19:12.6523249Z the profiling information to a file named `trace_name.prof`. 2025-09-07T08:19:12.6523841Z The `--profile-from-start off` option ensures that profiling starts only 2025-09-07T08:19:12.6524449Z after the `cudaProfilerStart` call in the script. 2025-09-07T08:19:12.6525056Z The `--csv` and `--print-summary` options format the profiling output as a 2025-09-07T08:19:12.6525603Z CSV file and print a summary, respectively. 2025-09-07T08:19:12.6526116Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-09-07T08:19:12.6526690Z overwrite of the output file if it already exists. 2025-09-07T08:19:12.6527088Z 2025-09-07T08:19:12.6527933Z 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)) 2025-09-07T08:19:12.6528768Z 2025-09-07T08:19:12.6529114Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T08:19:12.6529735Z ^ 2025-09-07T08:19:12.6669297Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2025-09-07T08:19:12.6670639Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.6671029Z 2025-09-07T08:19:12.6671272Z Append the given callback function to this ``Future``, which will be run 2025-09-07T08:19:12.6671822Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-09-07T08:19:12.6672354Z the same ``Future``, but the order in which they will be executed cannot 2025-09-07T08:19:12.6672864Z be guaranteed (to enforce a certain order consider chaining: 2025-09-07T08:19:12.6673521Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-09-07T08:19:12.6674044Z is the reference to this ``Future``. The callback function can use the 2025-09-07T08:19:12.6674581Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-09-07T08:19:12.6675140Z already completed, the given callback will be run immediately inline. 2025-09-07T08:19:12.6675474Z 2025-09-07T08:19:12.6675684Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-09-07T08:19:12.6676223Z callback might be invoked while the async kernels that are populating 2025-09-07T08:19:12.6676779Z those tensors haven't yet finished executing on the device. However, the 2025-09-07T08:19:12.6677336Z callback will be invoked with some dedicated streams set as current 2025-09-07T08:19:12.6677868Z (fetched from a global pool) which will be synchronized with those 2025-09-07T08:19:12.6678416Z kernels. Hence any operation performed by the callback on these tensors 2025-09-07T08:19:12.6679061Z will be scheduled on the device after the kernels complete. In other 2025-09-07T08:19:12.6679651Z words, as long as the callback doesn't switch streams, it can safely 2025-09-07T08:19:12.6680320Z manipulate the result without any additional synchronization. This is 2025-09-07T08:19:12.6680889Z similar to the non-blocking behavior of :meth:`wait`. 2025-09-07T08:19:12.6681223Z 2025-09-07T08:19:12.6681451Z Similarly, if the callback returns a value that contains tensors that 2025-09-07T08:19:12.6681970Z reside on a GPU, it can do so even if the kernels that are producing 2025-09-07T08:19:12.6682500Z these tensors are still running on the device, as long as the callback 2025-09-07T08:19:12.6683038Z didn't change streams during its execution. If one wants to change 2025-09-07T08:19:12.6683573Z streams, one must be careful to re-synchronize them with the original 2025-09-07T08:19:12.6684173Z streams, that is, those that were current when the callback was invoked. 2025-09-07T08:19:12.6684519Z 2025-09-07T08:19:12.6684600Z Args: 2025-09-07T08:19:12.6684926Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-09-07T08:19:12.6685367Z the only argument. 2025-09-07T08:19:12.6685583Z 2025-09-07T08:19:12.6685678Z Returns: 2025-09-07T08:19:12.6685952Z A new ``Future`` object that holds the return value of the 2025-09-07T08:19:12.6686415Z ``callback`` and will be marked as completed when the given 2025-09-07T08:19:12.6686808Z ``callback`` finishes. 2025-09-07T08:19:12.6687026Z 2025-09-07T08:19:12.6687224Z .. note:: Note that if the callback function throws, either 2025-09-07T08:19:12.6687708Z through the original future being completed with an exception and 2025-09-07T08:19:12.6688231Z calling ``fut.wait()``, or through other code in the callback, the 2025-09-07T08:19:12.6688748Z future returned by ``then`` will be marked appropriately with the 2025-09-07T08:19:12.6689266Z encountered error. However, if this callback later completes 2025-09-07T08:19:12.6689792Z additional futures, those futures are not marked as completed with 2025-09-07T08:19:12.6690322Z an error and the user is responsible for handling completion/waiting 2025-09-07T08:19:12.6690760Z on those futures independently. 2025-09-07T08:19:12.6690981Z 2025-09-07T08:19:12.6691070Z Example:: 2025-09-07T08:19:12.6691188Z 2025-09-07T08:19:12.6691347Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T08:19:12.6691761Z >>> def callback(fut): 2025-09-07T08:19:12.6692076Z ... print(f"RPC return value is {fut.wait()}.") 2025-09-07T08:19:12.6692438Z >>> fut = torch.futures.Future() 2025-09-07T08:19:12.6692818Z >>> # The inserted callback will print the return value when 2025-09-07T08:19:12.6693214Z >>> # receiving the response from "worker1" 2025-09-07T08:19:12.6693556Z >>> cb_fut = fut.then(callback) 2025-09-07T08:19:12.6693872Z >>> chain_cb_fut = cb_fut.then( 2025-09-07T08:19:12.6694222Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-09-07T08:19:12.6694636Z ... ) 2025-09-07T08:19:12.6694859Z >>> fut.set_result(5) 2025-09-07T08:19:12.6695131Z RPC return value is 5. 2025-09-07T08:19:12.6695403Z Chained cb done. None 2025-09-07T08:19:12.6695568Z 2025-09-07T08:19:12.6695829Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.6696194Z 2025-09-07T08:19:12.6696757Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=211. 2025-09-07T08:19:12.6697649Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.6698035Z 2025-09-07T08:19:12.6698241Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-09-07T08:19:12.6698783Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-09-07T08:19:12.6699229Z cannot be marked completed twice. 2025-09-07T08:19:12.6699430Z 2025-09-07T08:19:12.6699645Z If the result contains tensors that reside on GPUs, this method can be 2025-09-07T08:19:12.6700184Z called even if the asynchronous kernels that are populating those 2025-09-07T08:19:12.6700724Z tensors haven't yet completed running on the device, provided that the 2025-09-07T08:19:12.6701284Z streams on which those kernels were enqueued are set as the current ones 2025-09-07T08:19:12.6701879Z when this method is called. Put simply, it's safe to call this method 2025-09-07T08:19:12.6702418Z immediately after launching those kernels, without any additional 2025-09-07T08:19:12.6702976Z synchronization, as long as one doesn't change streams in between. This 2025-09-07T08:19:12.6703540Z method will record events on all the relevant current streams and will 2025-09-07T08:19:12.6704080Z use them to ensure proper scheduling for all the consumers of this 2025-09-07T08:19:12.6704472Z ``Future``. 2025-09-07T08:19:12.6704606Z 2025-09-07T08:19:12.6704686Z Args: 2025-09-07T08:19:12.6704967Z result (object): the result object of this ``Future``. 2025-09-07T08:19:12.6705237Z 2025-09-07T08:19:12.6705339Z Example:: 2025-09-07T08:19:12.6705459Z 2025-09-07T08:19:12.6705617Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T08:19:12.6705959Z >>> import threading 2025-09-07T08:19:12.6706217Z >>> import time 2025-09-07T08:19:12.6706474Z >>> def slow_set_future(fut, value): 2025-09-07T08:19:12.6706792Z ... time.sleep(0.5) 2025-09-07T08:19:12.6707062Z ... fut.set_result(value) 2025-09-07T08:19:12.6707400Z >>> fut = torch.futures.Future() 2025-09-07T08:19:12.6707711Z >>> t = threading.Thread( 2025-09-07T08:19:12.6707999Z ... target=slow_set_future, 2025-09-07T08:19:12.6708297Z ... args=(fut, torch.ones(2) * 3) 2025-09-07T08:19:12.6708600Z ... ) 2025-09-07T08:19:12.6708810Z >>> t.start() 2025-09-07T08:19:12.6709055Z >>> print(fut.wait()) 2025-09-07T08:19:12.6709300Z tensor([3., 3.]) 2025-09-07T08:19:12.6709539Z >>> t.join() 2025-09-07T08:19:12.6709675Z 2025-09-07T08:19:12.6709939Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.6710307Z 2025-09-07T08:19:12.6815234Z msg = Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=145. 2025-09-07T08:19:12.6816132Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.6816860Z Compiles compute shader from source and allows one to invoke kernels 2025-09-07T08:19:12.6817358Z defined there from the comfort of Python runtime 2025-09-07T08:19:12.6817727Z Example:: 2025-09-07T08:19:12.6817856Z 2025-09-07T08:19:12.6818006Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-09-07T08:19:12.6818365Z >>> lib = torch.mps.compile_shader( 2025-09-07T08:19:12.6818995Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-09-07T08:19:12.6819580Z ... ) 2025-09-07T08:19:12.6819836Z >>> x = torch.zeros(16, device="mps") 2025-09-07T08:19:12.6820162Z >>> lib.full(x, 3.14) 2025-09-07T08:19:12.6820416Z 2025-09-07T08:19:12.6820786Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.6821156Z 2025-09-07T08:19:12.7043249Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-09-07T08:19:12.7044226Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.7044785Z Return the sum of each row of the given sparse tensor. 2025-09-07T08:19:12.7045054Z 2025-09-07T08:19:12.7045278Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-09-07T08:19:12.7045829Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-09-07T08:19:12.7046355Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-09-07T08:19:12.7046834Z returns a dense tensor instead of a sparse tensor. 2025-09-07T08:19:12.7047092Z 2025-09-07T08:19:12.7047360Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-09-07T08:19:12.7047923Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-09-07T08:19:12.7048330Z 2025-09-07T08:19:12.7048553Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-09-07T08:19:12.7049139Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-09-07T08:19:12.7049497Z 2025-09-07T08:19:12.7049596Z Args: 2025-09-07T08:19:12.7049846Z input (Tensor): the input sparse tensor 2025-09-07T08:19:12.7050347Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-09-07T08:19:12.7050839Z over all dims. 2025-09-07T08:19:12.7051264Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-09-07T08:19:12.7051758Z Default: dtype of :attr:`input`. 2025-09-07T08:19:12.7052025Z 2025-09-07T08:19:12.7052159Z Example:: 2025-09-07T08:19:12.7052352Z 2025-09-07T08:19:12.7052462Z >>> nnz = 3 2025-09-07T08:19:12.7052698Z >>> dims = [5, 5, 2, 3] 2025-09-07T08:19:12.7053039Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-09-07T08:19:12.7053569Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-09-07T08:19:12.7054062Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-09-07T08:19:12.7054405Z >>> size = torch.Size(dims) 2025-09-07T08:19:12.7054753Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:12.7055135Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-09-07T08:19:12.7055446Z >>> S 2025-09-07T08:19:12.7055567Z tensor(indices=tensor([[2, 0, 3], 2025-09-07T08:19:12.7055663Z [2, 4, 1]]), 2025-09-07T08:19:12.7055838Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-09-07T08:19:12.7055949Z [ 0.3411, 0.0918, -0.2312]], 2025-09-07T08:19:12.7055953Z 2025-09-07T08:19:12.7056060Z [[ 0.5348, 0.0634, -2.0494], 2025-09-07T08:19:12.7056181Z [-0.7125, -1.0646, 2.1844]], 2025-09-07T08:19:12.7056188Z 2025-09-07T08:19:12.7056292Z [[ 0.1276, 0.1874, -0.6334], 2025-09-07T08:19:12.7056490Z [-1.9682, -0.5340, 0.7483]]]), 2025-09-07T08:19:12.7056635Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-09-07T08:19:12.7056640Z 2025-09-07T08:19:12.7056842Z # when sum over only part of sparse_dims, return a sparse tensor 2025-09-07T08:19:12.7056951Z >>> torch.sparse.sum(S, [1, 3]) 2025-09-07T08:19:12.7057061Z tensor(indices=tensor([[0, 2, 3]]), 2025-09-07T08:19:12.7057188Z values=tensor([[-1.4512, 0.4073], 2025-09-07T08:19:12.7057288Z [-0.8901, 0.2017], 2025-09-07T08:19:12.7057402Z [-0.3183, -1.7539]]), 2025-09-07T08:19:12.7057535Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-09-07T08:19:12.7057539Z 2025-09-07T08:19:12.7057690Z # when sum over all sparse dim, return a dense tensor 2025-09-07T08:19:12.7057805Z # with summed dims squeezed 2025-09-07T08:19:12.7057919Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-09-07T08:19:12.7058028Z tensor([-2.6596, -1.1450]) 2025-09-07T08:19:12.7058130Z 2025-09-07T08:19:12.7058384Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.7058388Z 2025-09-07T08:19:12.7058932Z msg = Cannot scrape callname=as_sparse_gradcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=550. 2025-09-07T08:19:12.7059193Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:12.7059396Z Decorate function, to extend gradcheck for sparse tensors. 2025-09-07T08:19:12.7059400Z 2025-09-07T08:19:12.7059613Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-09-07T08:19:12.7059827Z variants that extends the gradcheck function with support to input 2025-09-07T08:19:12.7060040Z functions that operate on or/and return sparse tensors. 2025-09-07T08:19:12.7060044Z 2025-09-07T08:19:12.7060254Z The specified gradcheck function itself is guaranteed to operate 2025-09-07T08:19:12.7060376Z on strided tensors only. 2025-09-07T08:19:12.7060380Z 2025-09-07T08:19:12.7060469Z For example: 2025-09-07T08:19:12.7060473Z 2025-09-07T08:19:12.7060723Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-09-07T08:19:12.7060806Z >>> x = ( 2025-09-07T08:19:12.7060960Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-09-07T08:19:12.7061075Z ... .to_sparse_coo() 2025-09-07T08:19:12.7061177Z ... .requires_grad_(True) 2025-09-07T08:19:12.7061259Z ... ) 2025-09-07T08:19:12.7061402Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-09-07T08:19:12.7061484Z True 2025-09-07T08:19:12.7061578Z 2025-09-07T08:19:12.7061829Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:12.7061835Z 2025-09-07T08:19:13.4476315Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-09-07T08:19:13.4477517Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:13.4477912Z 2025-09-07T08:19:13.4478130Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-09-07T08:19:13.4478666Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-09-07T08:19:13.4479203Z pushes the map into PyTorch operations called by ``func``, effectively 2025-09-07T08:19:13.4479651Z vectorizing those operations. 2025-09-07T08:19:13.4479843Z 2025-09-07T08:19:13.4480054Z vmap is useful for handling batch dimensions: one can write a function 2025-09-07T08:19:13.4480581Z ``func`` that runs on examples and then lift it to a function that can 2025-09-07T08:19:13.4481109Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-09-07T08:19:13.4481612Z compute batched gradients when composed with autograd. 2025-09-07T08:19:13.4481887Z 2025-09-07T08:19:13.4482001Z .. note:: 2025-09-07T08:19:13.4482404Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-09-07T08:19:13.4482842Z convenience. Use whichever one you'd like. 2025-09-07T08:19:13.4483080Z 2025-09-07T08:19:13.4483174Z Args: 2025-09-07T08:19:13.4483541Z func (function): A Python function that takes one or more arguments. 2025-09-07T08:19:13.4483979Z Must return one or more Tensors. 2025-09-07T08:19:13.4484485Z in_dims (int or nested structure): Specifies which dimension of the 2025-09-07T08:19:13.4485022Z inputs should be mapped over. ``in_dims`` should have a 2025-09-07T08:19:13.4485843Z structure like the inputs. If the ``in_dim`` for a particular 2025-09-07T08:19:13.4486549Z input is None, then that indicates there is no map dimension. 2025-09-07T08:19:13.4487135Z Default: 0. 2025-09-07T08:19:13.4487559Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-09-07T08:19:13.4488441Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-09-07T08:19:13.4489133Z it should have one element per output. Default: 0. 2025-09-07T08:19:13.4489595Z randomness (str): Specifies whether the randomness in this 2025-09-07T08:19:13.4490105Z vmap should be the same or different across batches. If 'different', 2025-09-07T08:19:13.4490624Z the randomness for each batch will be different. If 'same', the 2025-09-07T08:19:13.4491156Z randomness will be the same across batches. If 'error', any calls to 2025-09-07T08:19:13.4491699Z random functions will error. Default: 'error'. WARNING: this flag 2025-09-07T08:19:13.4492232Z only applies to random PyTorch operations and does not apply to 2025-09-07T08:19:13.4492676Z Python's random module or numpy randomness. 2025-09-07T08:19:13.4493148Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-09-07T08:19:13.4493816Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-09-07T08:19:13.4494416Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-09-07T08:19:13.4495066Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-09-07T08:19:13.4495451Z 2025-09-07T08:19:13.4495536Z Returns: 2025-09-07T08:19:13.4495847Z Returns a new "batched" function. It takes the same inputs as 2025-09-07T08:19:13.4496331Z ``func``, except each input has an extra dimension at the index 2025-09-07T08:19:13.4496823Z specified by ``in_dims``. It takes returns the same outputs as 2025-09-07T08:19:13.4497303Z ``func``, except each output has an extra dimension at the index 2025-09-07T08:19:13.4497711Z specified by ``out_dims``. 2025-09-07T08:19:13.4497911Z 2025-09-07T08:19:13.4497995Z .. warning: 2025-09-07T08:19:13.4498320Z :func:`vmap` works best with functional-style code. Please do not 2025-09-07T08:19:13.4498826Z perform any side-effects in ``func``, with the exception of 2025-09-07T08:19:13.4499353Z in-place PyTorch operations. Examples of side-effects include mutating 2025-09-07T08:19:13.4499982Z Python data structures and assigning values to variables not captured 2025-09-07T08:19:13.4500413Z in ``func``. 2025-09-07T08:19:13.4500549Z 2025-09-07T08:19:13.4500793Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-09-07T08:19:13.4501351Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-09-07T08:19:13.4501912Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-09-07T08:19:13.4502256Z 2025-09-07T08:19:13.4502375Z >>> torch.dot # [D], [D] -> [] 2025-09-07T08:19:13.4502783Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-09-07T08:19:13.4503230Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T08:19:13.4503557Z >>> batched_dot(x, y) 2025-09-07T08:19:13.4503735Z 2025-09-07T08:19:13.4504030Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-09-07T08:19:13.4504491Z model authoring experience. 2025-09-07T08:19:13.4504673Z 2025-09-07T08:19:13.4504799Z >>> batch_size, feature_size = 3, 5 2025-09-07T08:19:13.4505181Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T08:19:13.4505561Z >>> 2025-09-07T08:19:13.4505784Z >>> def model(feature_vec): 2025-09-07T08:19:13.4506111Z >>> # Very simple linear model with activation 2025-09-07T08:19:13.4506466Z >>> return feature_vec.dot(weights).relu() 2025-09-07T08:19:13.4506789Z >>> 2025-09-07T08:19:13.4507054Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T08:19:13.4507432Z >>> result = torch.vmap(model)(examples) 2025-09-07T08:19:13.4507654Z 2025-09-07T08:19:13.4507913Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-09-07T08:19:13.4508502Z or impossible to batch. One example is higher-order gradient computation. 2025-09-07T08:19:13.4509083Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-09-07T08:19:13.4509659Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-09-07T08:19:13.4510249Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-09-07T08:19:13.4510837Z we can vectorize the whole computation, computing the Jacobian in a single 2025-09-07T08:19:13.4511295Z call to ``autograd.grad``. 2025-09-07T08:19:13.4511479Z 2025-09-07T08:19:13.4511564Z >>> # Setup 2025-09-07T08:19:13.4511788Z >>> N = 5 2025-09-07T08:19:13.4512005Z >>> f = lambda x: x**2 2025-09-07T08:19:13.4512300Z >>> x = torch.randn(N, requires_grad=True) 2025-09-07T08:19:13.4512618Z >>> y = f(x) 2025-09-07T08:19:13.4512853Z >>> I_N = torch.eye(N) 2025-09-07T08:19:13.4513094Z >>> 2025-09-07T08:19:13.4513314Z >>> # Sequential approach 2025-09-07T08:19:13.4513752Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-09-07T08:19:13.4514202Z >>> for v in I_N.unbind()] 2025-09-07T08:19:13.4514540Z >>> jacobian = torch.stack(jacobian_rows) 2025-09-07T08:19:13.4514857Z >>> 2025-09-07T08:19:13.4515095Z >>> # vectorized gradient computation 2025-09-07T08:19:13.4515419Z >>> def get_vjp(v): 2025-09-07T08:19:13.4515696Z >>> return torch.autograd.grad(y, x, v) 2025-09-07T08:19:13.4516051Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-09-07T08:19:13.4516285Z 2025-09-07T08:19:13.4516543Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-09-07T08:19:13.4516921Z 2025-09-07T08:19:13.4517034Z >>> torch.dot # [D], [D] -> [] 2025-09-07T08:19:13.4517344Z >>> batched_dot = torch.vmap( 2025-09-07T08:19:13.4517635Z ... torch.vmap(torch.dot) 2025-09-07T08:19:13.4517945Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-09-07T08:19:13.4518312Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-09-07T08:19:13.4518684Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-09-07T08:19:13.4518922Z 2025-09-07T08:19:13.4519214Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-09-07T08:19:13.4519724Z the dimension that each inputs are batched along as 2025-09-07T08:19:13.4519998Z 2025-09-07T08:19:13.4520099Z >>> torch.dot # [N], [N] -> [] 2025-09-07T08:19:13.4520524Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-09-07T08:19:13.4520988Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T08:19:13.4521310Z >>> batched_dot( 2025-09-07T08:19:13.4521550Z ... x, y 2025-09-07T08:19:13.4521879Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-09-07T08:19:13.4522183Z 2025-09-07T08:19:13.4522451Z If there are multiple inputs each of which is batched along different dimensions, 2025-09-07T08:19:13.4523019Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-09-07T08:19:13.4523341Z 2025-09-07T08:19:13.4523500Z >>> torch.dot # [D], [D] -> [] 2025-09-07T08:19:13.4523936Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-09-07T08:19:13.4524504Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T08:19:13.4524813Z >>> batched_dot( 2025-09-07T08:19:13.4525049Z ... x, y 2025-09-07T08:19:13.4525377Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-09-07T08:19:13.4525682Z 2025-09-07T08:19:13.4525927Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-09-07T08:19:13.4526389Z matching the shape of the input: 2025-09-07T08:19:13.4526585Z 2025-09-07T08:19:13.4526723Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-09-07T08:19:13.4527093Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T08:19:13.4527424Z >>> input = {"x": x, "y": y} 2025-09-07T08:19:13.4527792Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-09-07T08:19:13.4528173Z >>> batched_dot(input) 2025-09-07T08:19:13.4528358Z 2025-09-07T08:19:13.4528630Z By default, the output is batched along the first dimension. However, it can be batched 2025-09-07T08:19:13.4529147Z along any dimension by using ``out_dims`` 2025-09-07T08:19:13.4529376Z 2025-09-07T08:19:13.4529482Z >>> f = lambda x: x**2 2025-09-07T08:19:13.4529756Z >>> x = torch.randn(2, 5) 2025-09-07T08:19:13.4530054Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-09-07T08:19:13.4530388Z >>> batched_pow(x) # [5, 2] 2025-09-07T08:19:13.4530574Z 2025-09-07T08:19:13.4530869Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-09-07T08:19:13.4531371Z accept kwargs 2025-09-07T08:19:13.4531502Z 2025-09-07T08:19:13.4531600Z >>> x = torch.randn([2, 5]) 2025-09-07T08:19:13.4531887Z >>> def fn(x, scale=4.): 2025-09-07T08:19:13.4532202Z >>> return x * scale 2025-09-07T08:19:13.4532455Z >>> 2025-09-07T08:19:13.4532667Z >>> batched_pow = torch.vmap(fn) 2025-09-07T08:19:13.4533032Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-09-07T08:19:13.4533515Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-09-07T08:19:13.4533859Z 2025-09-07T08:19:13.4533968Z .. note:: 2025-09-07T08:19:13.4534299Z vmap does not provide general autobatching or handle variable-length 2025-09-07T08:19:13.4534738Z sequences out of the box. 2025-09-07T08:19:13.4534934Z 2025-09-07T08:19:13.4535184Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:13.4535546Z 2025-09-07T08:19:13.4536058Z msg = Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-09-07T08:19:13.4536908Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:13.4537531Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-09-07T08:19:13.4538095Z input(s) specified by ``argnums``. This operator can be nested to 2025-09-07T08:19:13.4538544Z compute higher-order gradients. 2025-09-07T08:19:13.4538764Z 2025-09-07T08:19:13.4538845Z Args: 2025-09-07T08:19:13.4539176Z func (Callable): A Python function that takes one or more arguments. 2025-09-07T08:19:13.4539766Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-09-07T08:19:13.4540399Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-09-07T08:19:13.4540881Z ``(output, aux)``. 2025-09-07T08:19:13.4541360Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-09-07T08:19:13.4541966Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-09-07T08:19:13.4542510Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-09-07T08:19:13.4543019Z auxiliary objects: ``(output, aux)``. Default: False. 2025-09-07T08:19:13.4543296Z 2025-09-07T08:19:13.4543442Z Returns: 2025-09-07T08:19:13.4543862Z Function to compute gradients with respect to its inputs. By default, the output of 2025-09-07T08:19:13.4544511Z the function is the gradient tensor(s) with respect to the first argument. 2025-09-07T08:19:13.4545145Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-09-07T08:19:13.4545775Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-09-07T08:19:13.4546296Z respect to each ``argnums`` value is returned. 2025-09-07T08:19:13.4546561Z 2025-09-07T08:19:13.4546663Z Example of using ``grad``: 2025-09-07T08:19:13.4546848Z 2025-09-07T08:19:13.4546961Z >>> # xdoctest: +SKIP 2025-09-07T08:19:13.4547261Z >>> from torch.func import grad 2025-09-07T08:19:13.4547573Z >>> x = torch.randn([]) 2025-09-07T08:19:13.4547889Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-09-07T08:19:13.4548253Z >>> assert torch.allclose(cos_x, x.cos()) 2025-09-07T08:19:13.4548577Z >>> 2025-09-07T08:19:13.4548791Z >>> # Second-order gradients 2025-09-07T08:19:13.4549145Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-09-07T08:19:13.4549550Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-09-07T08:19:13.4549796Z 2025-09-07T08:19:13.4550060Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-09-07T08:19:13.4550423Z 2025-09-07T08:19:13.4550518Z >>> # xdoctest: +SKIP 2025-09-07T08:19:13.4550821Z >>> from torch.func import grad, vmap 2025-09-07T08:19:13.4551160Z >>> batch_size, feature_size = 3, 5 2025-09-07T08:19:13.4551463Z >>> 2025-09-07T08:19:13.4551688Z >>> def model(weights, feature_vec): 2025-09-07T08:19:13.4552073Z >>> # Very simple linear model with activation 2025-09-07T08:19:13.4552430Z >>> assert feature_vec.dim() == 1 2025-09-07T08:19:13.4552785Z >>> return feature_vec.dot(weights).relu() 2025-09-07T08:19:13.4553108Z >>> 2025-09-07T08:19:13.4553355Z >>> def compute_loss(weights, example, target): 2025-09-07T08:19:13.4553715Z >>> y = model(weights, example) 2025-09-07T08:19:13.4554072Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-09-07T08:19:13.4554406Z >>> 2025-09-07T08:19:13.4554698Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T08:19:13.4555144Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T08:19:13.4555525Z >>> targets = torch.randn(batch_size) 2025-09-07T08:19:13.4555872Z >>> inputs = (weights, examples, targets) 2025-09-07T08:19:13.4556328Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-09-07T08:19:13.4556774Z ... *inputs 2025-09-07T08:19:13.4557014Z ... ) 2025-09-07T08:19:13.4557137Z 2025-09-07T08:19:13.4557327Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-09-07T08:19:13.4557643Z 2025-09-07T08:19:13.4557739Z >>> # xdoctest: +SKIP 2025-09-07T08:19:13.4558032Z >>> from torch.func import grad 2025-09-07T08:19:13.4558353Z >>> def my_loss_func(y, y_pred): 2025-09-07T08:19:13.4558699Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-09-07T08:19:13.4559047Z >>> loss = loss_per_sample.mean() 2025-09-07T08:19:13.4559403Z >>> return loss, (y_pred, loss_per_sample) 2025-09-07T08:19:13.4559727Z >>> 2025-09-07T08:19:13.4560001Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-09-07T08:19:13.4560369Z >>> y_true = torch.rand(4) 2025-09-07T08:19:13.4560688Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-09-07T08:19:13.4561040Z >>> out = fn(y_true, y_preds) 2025-09-07T08:19:13.4561491Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-09-07T08:19:13.4561849Z 2025-09-07T08:19:13.4562012Z .. note:: 2025-09-07T08:19:13.4562306Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-09-07T08:19:13.4562595Z 2025-09-07T08:19:13.4562738Z Case 1: Using ``torch.no_grad`` inside a function: 2025-09-07T08:19:13.4563003Z 2025-09-07T08:19:13.4563099Z >>> # xdoctest: +SKIP 2025-09-07T08:19:13.4563381Z >>> def f(x): 2025-09-07T08:19:13.4563637Z >>> with torch.no_grad(): 2025-09-07T08:19:13.4563947Z >>> c = x ** 2 2025-09-07T08:19:13.4564336Z >>> return x - c 2025-09-07T08:19:13.4564522Z 2025-09-07T08:19:13.4564731Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-09-07T08:19:13.4565044Z 2025-09-07T08:19:13.4565239Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-09-07T08:19:13.4565540Z 2025-09-07T08:19:13.4565637Z >>> # xdoctest: +SKIP 2025-09-07T08:19:13.4565937Z >>> with torch.no_grad(): 2025-09-07T08:19:13.4566239Z >>> grad(f)(x) 2025-09-07T08:19:13.4566414Z 2025-09-07T08:19:13.4566643Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-09-07T08:19:13.4567202Z outer one. This is because ``grad`` is a "function transform": its result 2025-09-07T08:19:13.4567747Z should not depend on the result of a context manager outside of ``f``. 2025-09-07T08:19:13.4568091Z 2025-09-07T08:19:13.4568170Z 2025-09-07T08:19:13.4568537Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:13.4568900Z 2025-09-07T08:19:15.3853504Z msg = Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py line=397. 2025-09-07T08:19:15.3854492Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:15.3855243Z Register a FakeTensor implementation for this custom op. 2025-09-07T08:19:15.3855549Z 2025-09-07T08:19:15.3855819Z This is necessary to get the operator to work efficiently with torch.compile. 2025-09-07T08:19:15.3856195Z 2025-09-07T08:19:15.3856427Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-09-07T08:19:15.3856994Z specifies the behavior of this operator on Tensors that carry no data. 2025-09-07T08:19:15.3857479Z Given some input Tensors with certain properties 2025-09-07T08:19:15.3857997Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-09-07T08:19:15.3858476Z the output Tensors are. 2025-09-07T08:19:15.3858663Z 2025-09-07T08:19:15.3858877Z Please see :func:`torch.library.register_fake` for more details. 2025-09-07T08:19:15.3859193Z 2025-09-07T08:19:15.3859288Z Args: 2025-09-07T08:19:15.3859581Z fn (Callable): The function to register as the FakeTensor 2025-09-07T08:19:15.3859974Z implementation. 2025-09-07T08:19:15.3860159Z 2025-09-07T08:19:15.3860260Z Examples: 2025-09-07T08:19:15.3860558Z >>> import torch 2025-09-07T08:19:15.3860829Z >>> import numpy as np 2025-09-07T08:19:15.3861141Z >>> from torch import Tensor 2025-09-07T08:19:15.3861445Z >>> 2025-09-07T08:19:15.3861777Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T08:19:15.3862286Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-09-07T08:19:15.3862790Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T08:19:15.3863223Z >>> return (x @ weight.t()) + bias 2025-09-07T08:19:15.3863561Z >>> 2025-09-07T08:19:15.3863802Z >>> @linear.register_fake 2025-09-07T08:19:15.3864104Z >>> def _(x, weight, bias): 2025-09-07T08:19:15.3864418Z >>> assert x.dim() == 2 2025-09-07T08:19:15.3864740Z >>> assert weight.dim() == 2 2025-09-07T08:19:15.3865165Z >>> assert bias.dim() == 1 2025-09-07T08:19:15.3865502Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T08:19:15.3865879Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T08:19:15.3866254Z >>> assert x.device == weight.device 2025-09-07T08:19:15.3866641Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-09-07T08:19:15.3866981Z >>> 2025-09-07T08:19:15.3867224Z >>> x = torch.randn(2, 2) 2025-09-07T08:19:15.3867543Z >>> weight = torch.randn(2, 2) 2025-09-07T08:19:15.3867865Z >>> bias = torch.randn(2) 2025-09-07T08:19:15.3868194Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T08:19:15.3868642Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-09-07T08:19:15.3869094Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T08:19:15.3869596Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-09-07T08:19:15.3870051Z >>> 2025-09-07T08:19:15.3870356Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T08:19:15.3870852Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-09-07T08:19:15.3871287Z >>> def nonzero(x: Tensor) -> Tensor: 2025-09-07T08:19:15.3871626Z >>> x_np = x.cpu().numpy() 2025-09-07T08:19:15.3871964Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T08:19:15.3872350Z >>> return torch.tensor(res, device=x.device) 2025-09-07T08:19:15.3872684Z >>> 2025-09-07T08:19:15.3872928Z >>> @nonzero.register_fake 2025-09-07T08:19:15.3873220Z >>> def _(x): 2025-09-07T08:19:15.3873696Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T08:19:15.3874221Z >>> # Since we cannot peek at the data in an abstract impl, 2025-09-07T08:19:15.3874674Z >>> # we use the ctx object to construct a new symint that 2025-09-07T08:19:15.3875075Z >>> # represents the data-dependent size. 2025-09-07T08:19:15.3875444Z >>> ctx = torch.library.get_ctx() 2025-09-07T08:19:15.3875798Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T08:19:15.3876132Z >>> shape = [nnz, x.dim()] 2025-09-07T08:19:15.3876497Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T08:19:15.3876848Z >>> return result 2025-09-07T08:19:15.3877123Z >>> 2025-09-07T08:19:15.3877371Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-09-07T08:19:15.3877738Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T08:19:15.3878122Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-09-07T08:19:15.3878523Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T08:19:15.3878905Z >>> assert torch.allclose(out, x.nonzero()) 2025-09-07T08:19:15.3879148Z 2025-09-07T08:19:15.3879288Z 2025-09-07T08:19:15.3879926Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-09-07T08:19:15.3880575Z 2025-09-07T08:19:15.3880656Z _._ = None 2025-09-07T08:19:15.3880859Z ^ 2025-09-07T08:19:15.3995657Z msg = Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_profile.py line=94. 2025-09-07T08:19:15.3996666Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.3997047Z 2025-09-07T08:19:15.3997310Z Registers a fake kernel based on the given operator profiles. This fake 2025-09-07T08:19:15.3997907Z kernel registration will override any existing fake kernel registrations. 2025-09-07T08:19:15.3998282Z 2025-09-07T08:19:15.3998492Z The input is a dictionary mapping operator names to a set of operator 2025-09-07T08:19:15.3999216Z profiles, which we will use to generate fake kernels. The operator profiles 2025-09-07T08:19:15.3999780Z are a record of the input and output tensor metadata. Based on this 2025-09-07T08:19:15.4000338Z information we will match a given input to the recorded profile, and return 2025-09-07T08:19:15.4000912Z an output with the same metadata as in the recorded profile. If a profile 2025-09-07T08:19:15.4001397Z doesn't exist then an exception will be thrown. 2025-09-07T08:19:15.4001656Z 2025-09-07T08:19:15.4001884Z The fake kernel generation is considered unsafe because it relies on the 2025-09-07T08:19:15.4002465Z rigid, pre-defined operator profiles that do not account for potential 2025-09-07T08:19:15.4003058Z variations in output behavior. Specifically, the generated kernels assume a 2025-09-07T08:19:15.4003668Z fixed relationship between input and output ranks. However, in reality, it's 2025-09-07T08:19:15.4004366Z possible that data-dependent operations may produce outputs of different 2025-09-07T08:19:15.4004953Z ranks even when given inputs of the same rank. The generated fake kernels 2025-09-07T08:19:15.4005515Z are inflexible and unable to accommodate these nuances, making them 2025-09-07T08:19:15.4005932Z potentially unsafe. 2025-09-07T08:19:15.4006093Z 2025-09-07T08:19:15.4006173Z Args: 2025-09-07T08:19:15.4006508Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-09-07T08:19:15.4007045Z name to a set of operator profiles from which we will generate fake 2025-09-07T08:19:15.4007450Z kernels. 2025-09-07T08:19:15.4007584Z 2025-09-07T08:19:15.4007670Z Examples: 2025-09-07T08:19:15.4007798Z 2025-09-07T08:19:15.4007966Z >>> # Example: Registering an op-profile from draft-export 2025-09-07T08:19:15.4008341Z >>> import torch 2025-09-07T08:19:15.4008658Z >>> from torch.export._draft_export import draft_export 2025-09-07T08:19:15.4009057Z >>> 2025-09-07T08:19:15.4009369Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-09-07T08:19:15.4009794Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T08:19:15.4010132Z >>> return x + y 2025-09-07T08:19:15.4010370Z >>> 2025-09-07T08:19:15.4010593Z >>> class M(torch.nn.Module): 2025-09-07T08:19:15.4010894Z >>> def forward(self, a, b): 2025-09-07T08:19:15.4011259Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-09-07T08:19:15.4011617Z >>> return res 2025-09-07T08:19:15.4011873Z >>> 2025-09-07T08:19:15.4012153Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-09-07T08:19:15.4012526Z >>> 2025-09-07T08:19:15.4012945Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-09-07T08:19:15.4013489Z >>> decomp = ep.run_decompositions() 2025-09-07T08:19:15.4013717Z 2025-09-07T08:19:15.4013721Z 2025-09-07T08:19:15.4013976Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.4014360Z 2025-09-07T08:19:15.4071822Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=96. 2025-09-07T08:19:15.4072833Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.4073610Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-09-07T08:19:15.4073994Z 2025-09-07T08:19:15.4074202Z This is a more structured way of using triton kernels with PyTorch. 2025-09-07T08:19:15.4074788Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-09-07T08:19:15.4075407Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-09-07T08:19:15.4075886Z that is simpler; 2025-09-07T08:19:15.4076303Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-09-07T08:19:15.4076913Z want to create an operator that behaves like PyTorch built-in operators. 2025-09-07T08:19:15.4077566Z For example, you may use a ``torch.library`` wrapper API to define the 2025-09-07T08:19:15.4078104Z behavior of the triton kernel when passed a tensor subclass or under 2025-09-07T08:19:15.4078807Z a TorchDispatchMode. 2025-09-07T08:19:15.4079037Z 2025-09-07T08:19:15.4079430Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-09-07T08:19:15.4079995Z when the implementation 2025-09-07T08:19:15.4080385Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-09-07T08:19:15.4080889Z custom operators as opaque (:func:`torch.compile` and 2025-09-07T08:19:15.4081400Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-09-07T08:19:15.4081975Z makes the implementation visible to these subsystems, allowing them 2025-09-07T08:19:15.4082432Z to optimize the triton kernel(s). 2025-09-07T08:19:15.4082648Z 2025-09-07T08:19:15.4082834Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-09-07T08:19:15.4083369Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-09-07T08:19:15.4083912Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-09-07T08:19:15.4084290Z 2025-09-07T08:19:15.4084387Z Args: 2025-09-07T08:19:15.4084732Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-09-07T08:19:15.4085285Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-09-07T08:19:15.4085788Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-09-07T08:19:15.4086309Z To avoid name collisions, please use your project name as the namespace; 2025-09-07T08:19:15.4086877Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-09-07T08:19:15.4087468Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-09-07T08:19:15.4088182Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-09-07T08:19:15.4088809Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-09-07T08:19:15.4089383Z schema (None | str): A schema string for the operator. If None 2025-09-07T08:19:15.4089896Z (recommended) we'll infer a schema for the operator from its type 2025-09-07T08:19:15.4090417Z annotations. We recommend letting us infer a schema unless you 2025-09-07T08:19:15.4090856Z have a specific reason not to. 2025-09-07T08:19:15.4091229Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-09-07T08:19:15.4091488Z 2025-09-07T08:19:15.4091588Z Example:: 2025-09-07T08:19:15.4091717Z 2025-09-07T08:19:15.4091855Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:15.4092201Z >>> import torch 2025-09-07T08:19:15.4092525Z >>> from torch.library import triton_op, wrap_triton 2025-09-07T08:19:15.4092877Z >>> 2025-09-07T08:19:15.4093089Z >>> import triton 2025-09-07T08:19:15.4093417Z >>> from triton import language as tl 2025-09-07T08:19:15.4093732Z >>> 2025-09-07T08:19:15.4093946Z >>> @triton.jit 2025-09-07T08:19:15.4094184Z >>> def add_kernel( 2025-09-07T08:19:15.4094446Z >>> in_ptr0, 2025-09-07T08:19:15.4094698Z >>> in_ptr1, 2025-09-07T08:19:15.4094949Z >>> out_ptr, 2025-09-07T08:19:15.4095186Z >>> n_elements, 2025-09-07T08:19:15.4095470Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T08:19:15.4095775Z >>> ): 2025-09-07T08:19:15.4096046Z >>> pid = tl.program_id(axis=0) 2025-09-07T08:19:15.4096382Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T08:19:15.4096747Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T08:19:15.4097125Z >>> mask = offsets < n_elements 2025-09-07T08:19:15.4097476Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T08:19:15.4097912Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T08:19:15.4098256Z >>> output = x + y 2025-09-07T08:19:15.4098569Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T08:19:15.4098912Z >>> 2025-09-07T08:19:15.4099166Z >>> @triton_op("mylib::add", mutates_args={}) 2025-09-07T08:19:15.4099597Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-09-07T08:19:15.4099999Z >>> output = torch.empty_like(x) 2025-09-07T08:19:15.4100339Z >>> n_elements = output.numel() 2025-09-07T08:19:15.4100649Z >>> 2025-09-07T08:19:15.4100871Z >>> def grid(meta): 2025-09-07T08:19:15.4101216Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T08:19:15.4101592Z >>> 2025-09-07T08:19:15.4101898Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-09-07T08:19:15.4102395Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-09-07T08:19:15.4102795Z >>> return output 2025-09-07T08:19:15.4103063Z >>> 2025-09-07T08:19:15.4103287Z >>> @torch.compile 2025-09-07T08:19:15.4103553Z >>> def f(x, y): 2025-09-07T08:19:15.4103802Z >>> return add(x, y) 2025-09-07T08:19:15.4104070Z >>> 2025-09-07T08:19:15.4112317Z >>> x = torch.randn(3, device="cuda") 2025-09-07T08:19:15.4112752Z >>> y = torch.randn(3, device="cuda") 2025-09-07T08:19:15.4113079Z >>> 2025-09-07T08:19:15.4113304Z >>> z = f(x, y) 2025-09-07T08:19:15.4113590Z >>> assert torch.allclose(z, x + y) 2025-09-07T08:19:15.4113817Z 2025-09-07T08:19:15.4113903Z 2025-09-07T08:19:15.4114278Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.4114662Z 2025-09-07T08:19:15.4115174Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=296. 2025-09-07T08:19:15.4116172Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.4116759Z Allows capture of a triton kernel into a graph via make_fx or 2025-09-07T08:19:15.4117166Z non-strict ``torch.export``. 2025-09-07T08:19:15.4117376Z 2025-09-07T08:19:15.4117560Z These technologies perform Dispatcher-based tracing (via 2025-09-07T08:19:15.4118058Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-09-07T08:19:15.4118574Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-09-07T08:19:15.4118991Z can actually be traced into a graph. 2025-09-07T08:19:15.4119225Z 2025-09-07T08:19:15.4119434Z Please use this API together with :func:`torch.library.triton_op`. 2025-09-07T08:19:15.4119769Z 2025-09-07T08:19:15.4119855Z Examples: 2025-09-07T08:19:15.4119986Z 2025-09-07T08:19:15.4120101Z >>> # xdoctest: +SKIP 2025-09-07T08:19:15.4120383Z >>> import torch 2025-09-07T08:19:15.4120635Z >>> import triton 2025-09-07T08:19:15.4120931Z >>> from triton import language as tl 2025-09-07T08:19:15.4121380Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T08:19:15.4121808Z >>> from torch.library import wrap_triton 2025-09-07T08:19:15.4122122Z >>> 2025-09-07T08:19:15.4122335Z >>> @triton.jit 2025-09-07T08:19:15.4122586Z >>> def add_kernel( 2025-09-07T08:19:15.4122852Z >>> in_ptr0, 2025-09-07T08:19:15.4123089Z >>> in_ptr1, 2025-09-07T08:19:15.4123336Z >>> out_ptr, 2025-09-07T08:19:15.4123586Z >>> n_elements, 2025-09-07T08:19:15.4123873Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T08:19:15.4124241Z >>> ): 2025-09-07T08:19:15.4124486Z >>> pid = tl.program_id(axis=0) 2025-09-07T08:19:15.4124825Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T08:19:15.4125209Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T08:19:15.4125644Z >>> mask = offsets < n_elements 2025-09-07T08:19:15.4126001Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T08:19:15.4126375Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T08:19:15.4126721Z >>> output = x + y 2025-09-07T08:19:15.4127039Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T08:19:15.4127386Z >>> 2025-09-07T08:19:15.4127610Z >>> def add(x, y): 2025-09-07T08:19:15.4127900Z >>> output = torch.empty_like(x) 2025-09-07T08:19:15.4128230Z >>> n_elements = output.numel() 2025-09-07T08:19:15.4128538Z >>> 2025-09-07T08:19:15.4128764Z >>> def grid_fn(meta): 2025-09-07T08:19:15.4129135Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T08:19:15.4129495Z >>> 2025-09-07T08:19:15.4129815Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-09-07T08:19:15.4130231Z >>> return output 2025-09-07T08:19:15.4130505Z >>> 2025-09-07T08:19:15.4130734Z >>> x = torch.randn(3, device="cuda") 2025-09-07T08:19:15.4131078Z >>> y = torch.randn(3, device="cuda") 2025-09-07T08:19:15.4131404Z >>> gm = make_fx(add)(x, y) 2025-09-07T08:19:15.4131706Z >>> print(gm.code) 2025-09-07T08:19:15.4131977Z >>> # def forward(self, x_1, y_1): 2025-09-07T08:19:15.4132429Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-09-07T08:19:15.4133034Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-09-07T08:19:15.4133531Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-09-07T08:19:15.4133893Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-09-07T08:19:15.4134260Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-09-07T08:19:15.4134697Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-09-07T08:19:15.4135031Z >>> # }) 2025-09-07T08:19:15.4135298Z >>> # return empty_like 2025-09-07T08:19:15.4135498Z 2025-09-07T08:19:15.4135577Z 2025-09-07T08:19:15.4135955Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.4136337Z 2025-09-07T08:19:15.4912699Z msg = Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=286. 2025-09-07T08:19:15.4913832Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.4914325Z 2025-09-07T08:19:15.4914675Z Test if two objects are equal, and print an error message if test fails. 2025-09-07T08:19:15.4915093Z 2025-09-07T08:19:15.4915254Z The test is performed with ``actual == desired``. 2025-09-07T08:19:15.4915510Z 2025-09-07T08:19:15.4915616Z Parameters 2025-09-07T08:19:15.4915828Z ---------- 2025-09-07T08:19:15.4916052Z test_string : str 2025-09-07T08:19:15.4916332Z The message supplied to AssertionError. 2025-09-07T08:19:15.4916817Z actual : object 2025-09-07T08:19:15.4917110Z The object to test for equality against `desired`. 2025-09-07T08:19:15.4917571Z desired : object 2025-09-07T08:19:15.4917860Z The expected result. 2025-09-07T08:19:15.4918106Z 2025-09-07T08:19:15.4918239Z Examples 2025-09-07T08:19:15.4918510Z -------- 2025-09-07T08:19:15.4918822Z >>> np.testing.print_assert_equal( 2025-09-07T08:19:15.4919153Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-09-07T08:19:15.4919467Z ... ) # doctest: +SKIP 2025-09-07T08:19:15.4919745Z >>> np.testing.print_assert_equal( 2025-09-07T08:19:15.4920072Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-09-07T08:19:15.4920393Z ... ) # doctest: +SKIP 2025-09-07T08:19:15.4920654Z Traceback (most recent call last): 2025-09-07T08:19:15.4920947Z ... 2025-09-07T08:19:15.4921194Z AssertionError: Test XYZ of func xyz failed 2025-09-07T08:19:15.4921517Z ACTUAL: 2025-09-07T08:19:15.4921703Z [0, 1] 2025-09-07T08:19:15.4921907Z DESIRED: 2025-09-07T08:19:15.4922267Z [0, 2] 2025-09-07T08:19:15.4922462Z 2025-09-07T08:19:15.4922469Z 2025-09-07T08:19:15.4922857Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.4923442Z 2025-09-07T08:19:15.4924289Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=331. 2025-09-07T08:19:15.4925308Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.4925754Z 2025-09-07T08:19:15.4925976Z Raises an AssertionError if two items are not equal up to desired 2025-09-07T08:19:15.4926443Z precision. 2025-09-07T08:19:15.4926580Z 2025-09-07T08:19:15.4926769Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T08:19:15.4927366Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T08:19:15.4928117Z instead of this function for more consistent floating point 2025-09-07T08:19:15.4928643Z comparisons. 2025-09-07T08:19:15.4928807Z 2025-09-07T08:19:15.4929021Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-09-07T08:19:15.4929493Z 2025-09-07T08:19:15.4929653Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-09-07T08:19:15.4929937Z 2025-09-07T08:19:15.4930205Z That is a looser test than originally documented, but agrees with what the 2025-09-07T08:19:15.4930809Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-09-07T08:19:15.4931465Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-09-07T08:19:15.4931995Z delegates to assert_array_almost_equal 2025-09-07T08:19:15.4932226Z 2025-09-07T08:19:15.4932322Z Parameters 2025-09-07T08:19:15.4932597Z ---------- 2025-09-07T08:19:15.4932824Z actual : array_like 2025-09-07T08:19:15.4933161Z The object to check. 2025-09-07T08:19:15.4933492Z desired : array_like 2025-09-07T08:19:15.4933748Z The expected object. 2025-09-07T08:19:15.4934023Z decimal : int, optional 2025-09-07T08:19:15.4934353Z Desired precision, default is 7. 2025-09-07T08:19:15.4934665Z err_msg : str, optional 2025-09-07T08:19:15.4935048Z The error message to be printed in case of failure. 2025-09-07T08:19:15.4935413Z verbose : bool, optional 2025-09-07T08:19:15.4935838Z If True, the conflicting values are appended to the error message. 2025-09-07T08:19:15.4936167Z 2025-09-07T08:19:15.4936248Z Raises 2025-09-07T08:19:15.4936509Z ------ 2025-09-07T08:19:15.4936723Z AssertionError 2025-09-07T08:19:15.4937045Z If actual and desired are not equal up to specified precision. 2025-09-07T08:19:15.4937428Z 2025-09-07T08:19:15.4937511Z See Also 2025-09-07T08:19:15.4937717Z -------- 2025-09-07T08:19:15.4938146Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T08:19:15.4938627Z relative and/or absolute precision. 2025-09-07T08:19:15.4939142Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T08:19:15.4939485Z 2025-09-07T08:19:15.4939666Z Examples 2025-09-07T08:19:15.4939873Z -------- 2025-09-07T08:19:15.4940147Z >>> from torch._numpy.testing import assert_almost_equal 2025-09-07T08:19:15.4940634Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-09-07T08:19:15.4941074Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-09-07T08:19:15.4941534Z Traceback (most recent call last): 2025-09-07T08:19:15.4941829Z ... 2025-09-07T08:19:15.4942088Z AssertionError: 2025-09-07T08:19:15.4942357Z Arrays are not almost equal to 10 decimals 2025-09-07T08:19:15.4942735Z ACTUAL: 2.3333333333333 2025-09-07T08:19:15.4943002Z DESIRED: 2.33333334 2025-09-07T08:19:15.4943148Z 2025-09-07T08:19:15.4943243Z >>> assert_almost_equal( 2025-09-07T08:19:15.4943672Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-09-07T08:19:15.4944082Z ... ) 2025-09-07T08:19:15.4944371Z Traceback (most recent call last): 2025-09-07T08:19:15.4944651Z ... 2025-09-07T08:19:15.4944938Z AssertionError: 2025-09-07T08:19:15.4945267Z Arrays are not almost equal to 9 decimals 2025-09-07T08:19:15.4945582Z 2025-09-07T08:19:15.4945844Z Mismatched elements: 1 / 2 (50%) 2025-09-07T08:19:15.4946186Z Max absolute difference: 6.666699636781459e-09 2025-09-07T08:19:15.4946566Z Max relative difference: 2.8571569790287484e-09 2025-09-07T08:19:15.4946990Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T08:19:15.4947406Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T08:19:15.4947679Z 2025-09-07T08:19:15.4947683Z 2025-09-07T08:19:15.4947933Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.4948380Z 2025-09-07T08:19:15.4949000Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=457. 2025-09-07T08:19:15.4950002Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.4950385Z 2025-09-07T08:19:15.4950619Z Raises an AssertionError if two items are not equal up to significant 2025-09-07T08:19:15.4951029Z digits. 2025-09-07T08:19:15.4951152Z 2025-09-07T08:19:15.4951385Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T08:19:15.4951854Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T08:19:15.4952344Z instead of this function for more consistent floating point 2025-09-07T08:19:15.4952740Z comparisons. 2025-09-07T08:19:15.4952895Z 2025-09-07T08:19:15.4953075Z Given two numbers, check that they are approximately equal. 2025-09-07T08:19:15.4953662Z Approximately equal is defined as the number of significant digits 2025-09-07T08:19:15.4954083Z that agree. 2025-09-07T08:19:15.4954205Z 2025-09-07T08:19:15.4954300Z Parameters 2025-09-07T08:19:15.4954541Z ---------- 2025-09-07T08:19:15.4954753Z actual : scalar 2025-09-07T08:19:15.4954990Z The object to check. 2025-09-07T08:19:15.4955248Z desired : scalar 2025-09-07T08:19:15.4955487Z The expected object. 2025-09-07T08:19:15.4955761Z significant : int, optional 2025-09-07T08:19:15.4956060Z Desired precision, default is 7. 2025-09-07T08:19:15.4956368Z err_msg : str, optional 2025-09-07T08:19:15.4956670Z The error message to be printed in case of failure. 2025-09-07T08:19:15.4957034Z verbose : bool, optional 2025-09-07T08:19:15.4957476Z If True, the conflicting values are appended to the error message. 2025-09-07T08:19:15.4957797Z 2025-09-07T08:19:15.4957889Z Raises 2025-09-07T08:19:15.4958080Z ------ 2025-09-07T08:19:15.4958290Z AssertionError 2025-09-07T08:19:15.4958619Z If actual and desired are not equal up to specified precision. 2025-09-07T08:19:15.4958922Z 2025-09-07T08:19:15.4959017Z See Also 2025-09-07T08:19:15.4959206Z -------- 2025-09-07T08:19:15.4959560Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T08:19:15.4960044Z relative and/or absolute precision. 2025-09-07T08:19:15.4960496Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T08:19:15.4960859Z 2025-09-07T08:19:15.4960942Z Examples 2025-09-07T08:19:15.4961144Z -------- 2025-09-07T08:19:15.4961375Z >>> np.testing.assert_approx_equal( 2025-09-07T08:19:15.4961690Z ... 0.12345677777777e-20, 0.1234567e-20 2025-09-07T08:19:15.4961985Z ... ) # doctest: +SKIP 2025-09-07T08:19:15.4962263Z >>> np.testing.assert_approx_equal( 2025-09-07T08:19:15.4962562Z ... 0.12345670e-20, 2025-09-07T08:19:15.4962823Z ... 0.12345671e-20, # doctest: +SKIP 2025-09-07T08:19:15.4963119Z ... significant=8, 2025-09-07T08:19:15.4963356Z ... ) 2025-09-07T08:19:15.4963580Z >>> np.testing.assert_approx_equal( 2025-09-07T08:19:15.4963877Z ... 0.12345670e-20, 2025-09-07T08:19:15.4964254Z ... 0.12345672e-20, # doctest: +SKIP 2025-09-07T08:19:15.4964612Z ... significant=8, 2025-09-07T08:19:15.4964852Z ... ) 2025-09-07T08:19:15.4965076Z Traceback (most recent call last): 2025-09-07T08:19:15.4965353Z ... 2025-09-07T08:19:15.4965664Z AssertionError: 2025-09-07T08:19:15.4965944Z Items are not equal to 8 significant digits: 2025-09-07T08:19:15.4966275Z ACTUAL: 1.234567e-21 2025-09-07T08:19:15.4966515Z DESIRED: 1.2345672e-21 2025-09-07T08:19:15.4966686Z 2025-09-07T08:19:15.4966843Z the evaluated condition that raises the exception is 2025-09-07T08:19:15.4967123Z 2025-09-07T08:19:15.4967316Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-09-07T08:19:15.4967715Z True 2025-09-07T08:19:15.4967823Z 2025-09-07T08:19:15.4967827Z 2025-09-07T08:19:15.4968077Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.4968455Z 2025-09-07T08:19:15.4969017Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=744. 2025-09-07T08:19:15.4969961Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.4970353Z 2025-09-07T08:19:15.4970563Z Raises an AssertionError if two array_like objects are not equal. 2025-09-07T08:19:15.4970884Z 2025-09-07T08:19:15.4971097Z Given two array_like objects, check that the shape is equal and all 2025-09-07T08:19:15.4971641Z elements of these objects are equal (but see the Notes for the special 2025-09-07T08:19:15.4972171Z handling of a scalar). An exception is raised at shape mismatch or 2025-09-07T08:19:15.4972714Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-09-07T08:19:15.4973432Z are compared like numbers, no assertion is raised if both objects have 2025-09-07T08:19:15.4973885Z NaNs in the same positions. 2025-09-07T08:19:15.4974066Z 2025-09-07T08:19:15.4974293Z The usual caution for verifying equality with floating point numbers is 2025-09-07T08:19:15.4974731Z advised. 2025-09-07T08:19:15.4974929Z 2025-09-07T08:19:15.4975018Z Parameters 2025-09-07T08:19:15.4975233Z ---------- 2025-09-07T08:19:15.4975433Z x : array_like 2025-09-07T08:19:15.4975681Z The actual object to check. 2025-09-07T08:19:15.4975968Z y : array_like 2025-09-07T08:19:15.4976213Z The desired, expected object. 2025-09-07T08:19:15.4976505Z err_msg : str, optional 2025-09-07T08:19:15.4976823Z The error message to be printed in case of failure. 2025-09-07T08:19:15.4977188Z verbose : bool, optional 2025-09-07T08:19:15.4977566Z If True, the conflicting values are appended to the error message. 2025-09-07T08:19:15.4978065Z strict : bool, optional 2025-09-07T08:19:15.4978437Z If True, raise an AssertionError when either the shape or the data 2025-09-07T08:19:15.4979077Z type of the array_like objects does not match. The special 2025-09-07T08:19:15.4979774Z handling for scalars mentioned in the Notes section is disabled. 2025-09-07T08:19:15.4980156Z 2025-09-07T08:19:15.4980258Z Raises 2025-09-07T08:19:15.4980481Z ------ 2025-09-07T08:19:15.4980696Z AssertionError 2025-09-07T08:19:15.4980969Z If actual and desired objects are not equal. 2025-09-07T08:19:15.4981211Z 2025-09-07T08:19:15.4981310Z See Also 2025-09-07T08:19:15.4981572Z -------- 2025-09-07T08:19:15.4981931Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T08:19:15.4982417Z relative and/or absolute precision. 2025-09-07T08:19:15.4982866Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T08:19:15.4983185Z 2025-09-07T08:19:15.4983262Z Notes 2025-09-07T08:19:15.4983459Z ----- 2025-09-07T08:19:15.4983757Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-09-07T08:19:15.4984279Z function checks that each element of the array_like object is equal to 2025-09-07T08:19:15.4984830Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-09-07T08:19:15.4985180Z 2025-09-07T08:19:15.4985262Z Examples 2025-09-07T08:19:15.4985466Z -------- 2025-09-07T08:19:15.4985725Z The first assert does not raise an exception: 2025-09-07T08:19:15.4985963Z 2025-09-07T08:19:15.4986075Z >>> np.testing.assert_array_equal( 2025-09-07T08:19:15.4986513Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-09-07T08:19:15.4986857Z ... ) 2025-09-07T08:19:15.4986969Z 2025-09-07T08:19:15.4987206Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-09-07T08:19:15.4987662Z functions for these cases instead: 2025-09-07T08:19:15.4987864Z 2025-09-07T08:19:15.4987968Z >>> np.testing.assert_allclose( 2025-09-07T08:19:15.4988361Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-09-07T08:19:15.4988758Z ... ) 2025-09-07T08:19:15.4988868Z 2025-09-07T08:19:15.4989082Z As mentioned in the Notes section, `assert_array_equal` has special 2025-09-07T08:19:15.4989616Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-09-07T08:19:15.4989964Z 2025-09-07T08:19:15.4990067Z >>> x = np.full((2, 5), fill_value=3) 2025-09-07T08:19:15.4990396Z >>> np.testing.assert_array_equal(x, 3) 2025-09-07T08:19:15.4990614Z 2025-09-07T08:19:15.4990841Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-09-07T08:19:15.4991265Z array: 2025-09-07T08:19:15.4991378Z 2025-09-07T08:19:15.4991527Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-09-07T08:19:15.4991900Z Traceback (most recent call last): 2025-09-07T08:19:15.4992190Z ... 2025-09-07T08:19:15.4992397Z AssertionError: 2025-09-07T08:19:15.4992623Z Arrays are not equal 2025-09-07T08:19:15.4992924Z 2025-09-07T08:19:15.4993137Z (shapes (2, 5), () mismatch) 2025-09-07T08:19:15.4993426Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-09-07T08:19:15.4993723Z [3, 3, 3, 3, 3]]) 2025-09-07T08:19:15.4993983Z y: torch.ndarray(3) 2025-09-07T08:19:15.4994132Z 2025-09-07T08:19:15.4994339Z The `strict` parameter also ensures that the array data types match: 2025-09-07T08:19:15.4994677Z 2025-09-07T08:19:15.4994804Z >>> x = np.array([2, 2, 2]) 2025-09-07T08:19:15.4995110Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-09-07T08:19:15.4995501Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-09-07T08:19:15.4995864Z Traceback (most recent call last): 2025-09-07T08:19:15.4996161Z ... 2025-09-07T08:19:15.4996373Z AssertionError: 2025-09-07T08:19:15.4996614Z Arrays are not equal 2025-09-07T08:19:15.4996845Z 2025-09-07T08:19:15.4997109Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-09-07T08:19:15.4997467Z x: torch.ndarray([2, 2, 2]) 2025-09-07T08:19:15.4997748Z y: torch.ndarray([2., 2., 2.]) 2025-09-07T08:19:15.4997931Z 2025-09-07T08:19:15.4998181Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.4998558Z 2025-09-07T08:19:15.4999142Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=851. 2025-09-07T08:19:15.5000102Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.5000494Z 2025-09-07T08:19:15.5000710Z Raises an AssertionError if two objects are not equal up to desired 2025-09-07T08:19:15.5001157Z precision. 2025-09-07T08:19:15.5001281Z 2025-09-07T08:19:15.5001470Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T08:19:15.5001923Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T08:19:15.5002407Z instead of this function for more consistent floating point 2025-09-07T08:19:15.5002807Z comparisons. 2025-09-07T08:19:15.5002965Z 2025-09-07T08:19:15.5003215Z The test verifies identical shapes and that the elements of ``actual`` and 2025-09-07T08:19:15.5003657Z ``desired`` satisfy. 2025-09-07T08:19:15.5003831Z 2025-09-07T08:19:15.5004021Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-09-07T08:19:15.5004381Z 2025-09-07T08:19:15.5004606Z That is a looser test than originally documented, but agrees with what the 2025-09-07T08:19:15.5005201Z actual implementation did up to rounding vagaries. An exception is raised 2025-09-07T08:19:15.5005857Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-09-07T08:19:15.5006421Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-09-07T08:19:15.5006880Z objects have NaNs in the same positions. 2025-09-07T08:19:15.5007117Z 2025-09-07T08:19:15.5007204Z Parameters 2025-09-07T08:19:15.5007419Z ---------- 2025-09-07T08:19:15.5007621Z x : array_like 2025-09-07T08:19:15.5007861Z The actual object to check. 2025-09-07T08:19:15.5008141Z y : array_like 2025-09-07T08:19:15.5008384Z The desired, expected object. 2025-09-07T08:19:15.5008673Z decimal : int, optional 2025-09-07T08:19:15.5008948Z Desired precision, default is 6. 2025-09-07T08:19:15.5009256Z err_msg : str, optional 2025-09-07T08:19:15.5009573Z The error message to be printed in case of failure. 2025-09-07T08:19:15.5009924Z verbose : bool, optional 2025-09-07T08:19:15.5010303Z If True, the conflicting values are appended to the error message. 2025-09-07T08:19:15.5010632Z 2025-09-07T08:19:15.5010717Z Raises 2025-09-07T08:19:15.5010920Z ------ 2025-09-07T08:19:15.5011123Z AssertionError 2025-09-07T08:19:15.5011462Z If actual and desired are not equal up to specified precision. 2025-09-07T08:19:15.5011779Z 2025-09-07T08:19:15.5011860Z See Also 2025-09-07T08:19:15.5012062Z -------- 2025-09-07T08:19:15.5012408Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T08:19:15.5012892Z relative and/or absolute precision. 2025-09-07T08:19:15.5013340Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T08:19:15.5013660Z 2025-09-07T08:19:15.5013753Z Examples 2025-09-07T08:19:15.5013955Z -------- 2025-09-07T08:19:15.5014187Z the first assert does not raise an exception 2025-09-07T08:19:15.5014431Z 2025-09-07T08:19:15.5014678Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-09-07T08:19:15.5015093Z 2025-09-07T08:19:15.5015210Z >>> np.testing.assert_array_almost_equal( 2025-09-07T08:19:15.5015596Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-09-07T08:19:15.5015936Z ... ) 2025-09-07T08:19:15.5016171Z Traceback (most recent call last): 2025-09-07T08:19:15.5016467Z ... 2025-09-07T08:19:15.5016682Z AssertionError: 2025-09-07T08:19:15.5016931Z Arrays are not almost equal to 5 decimals 2025-09-07T08:19:15.5017247Z 2025-09-07T08:19:15.5017481Z Mismatched elements: 1 / 3 (33.3%) 2025-09-07T08:19:15.5017812Z Max absolute difference: 5.999999999994898e-05 2025-09-07T08:19:15.5018165Z Max relative difference: 2.5713661239633743e-05 2025-09-07T08:19:15.5018567Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T08:19:15.5019001Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-09-07T08:19:15.5019269Z 2025-09-07T08:19:15.5019399Z >>> np.testing.assert_array_almost_equal( 2025-09-07T08:19:15.5019750Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-09-07T08:19:15.5020078Z ... ) 2025-09-07T08:19:15.5020304Z Traceback (most recent call last): 2025-09-07T08:19:15.5020626Z ... 2025-09-07T08:19:15.5020823Z AssertionError: 2025-09-07T08:19:15.5021083Z Arrays are not almost equal to 5 decimals 2025-09-07T08:19:15.5021395Z 2025-09-07T08:19:15.5021627Z x and y nan location mismatch: 2025-09-07T08:19:15.5021968Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T08:19:15.5022397Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-09-07T08:19:15.5022675Z 2025-09-07T08:19:15.5022679Z 2025-09-07T08:19:15.5022927Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.5023296Z 2025-09-07T08:19:15.5023886Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1848. 2025-09-07T08:19:15.5024840Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.5025427Z Context manager that resets warning registry for catching warnings 2025-09-07T08:19:15.5025840Z 2025-09-07T08:19:15.5026083Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-09-07T08:19:15.5026672Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-09-07T08:19:15.5027254Z it impossible to retrigger the warning in this module, whatever you put in 2025-09-07T08:19:15.5027853Z the warnings filters. This context manager accepts a sequence of `modules` 2025-09-07T08:19:15.5028342Z as a keyword argument to its constructor and: 2025-09-07T08:19:15.5028598Z 2025-09-07T08:19:15.5028822Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-09-07T08:19:15.5029272Z on entry; 2025-09-07T08:19:15.5029600Z * resets ``__warningregistry__`` to its previous state on exit. 2025-09-07T08:19:15.5029897Z 2025-09-07T08:19:15.5030120Z This makes it possible to trigger any warning afresh inside the context 2025-09-07T08:19:15.5030655Z manager without disturbing the state of warnings outside. 2025-09-07T08:19:15.5030964Z 2025-09-07T08:19:15.5031197Z For compatibility with Python 3.0, please consider all arguments to be 2025-09-07T08:19:15.5031646Z keyword-only. 2025-09-07T08:19:15.5031790Z 2025-09-07T08:19:15.5031894Z Parameters 2025-09-07T08:19:15.5032108Z ---------- 2025-09-07T08:19:15.5032347Z record : bool, optional 2025-09-07T08:19:15.5032719Z Specifies whether warnings should be captured by a custom 2025-09-07T08:19:15.5033264Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-09-07T08:19:15.5033818Z returned by the context manager. Otherwise None is returned by the 2025-09-07T08:19:15.5034376Z context manager. The objects appended to the list are arguments whose 2025-09-07T08:19:15.5034898Z attributes mirror the arguments to ``showwarning()``. 2025-09-07T08:19:15.5035319Z modules : sequence, optional 2025-09-07T08:19:15.5035735Z Sequence of modules for which to reset warnings registry on entry and 2025-09-07T08:19:15.5036273Z restore on exit. To work correctly, all 'ignore' filters should 2025-09-07T08:19:15.5036696Z filter by one of these modules. 2025-09-07T08:19:15.5036905Z 2025-09-07T08:19:15.5037004Z Examples 2025-09-07T08:19:15.5037231Z -------- 2025-09-07T08:19:15.5037448Z >>> import warnings 2025-09-07T08:19:15.5037796Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-09-07T08:19:15.5038215Z ... modules=[np.core.fromnumeric] 2025-09-07T08:19:15.5038518Z ... ): 2025-09-07T08:19:15.5038745Z ... warnings.simplefilter("always") 2025-09-07T08:19:15.5039193Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-09-07T08:19:15.5039709Z ... # do something that raises a warning but ignore those in 2025-09-07T08:19:15.5040103Z ... # np.core.fromnumeric 2025-09-07T08:19:15.5040371Z 2025-09-07T08:19:15.5040741Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.5041167Z 2025-09-07T08:19:15.6968378Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2025-09-07T08:19:15.6969453Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.6970048Z Applies a 1D convolution over a quantized input signal composed of 2025-09-07T08:19:15.6970554Z several quantized input planes. 2025-09-07T08:19:15.6970775Z 2025-09-07T08:19:15.6971053Z For details on input arguments, parameters, and implementation see 2025-09-07T08:19:15.6971501Z :class:`~torch.nn.Conv1d`. 2025-09-07T08:19:15.6971712Z 2025-09-07T08:19:15.6971860Z .. note:: 2025-09-07T08:19:15.6972194Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-09-07T08:19:15.6972582Z 2025-09-07T08:19:15.6972668Z .. note:: 2025-09-07T08:19:15.6973206Z Only `torch.quint8` is supported for the input data type. 2025-09-07T08:19:15.6973701Z 2025-09-07T08:19:15.6973740Z 2025-09-07T08:19:15.6973831Z Attributes: 2025-09-07T08:19:15.6974255Z weight (Tensor): packed tensor derived from the learnable weight 2025-09-07T08:19:15.6974687Z parameter. 2025-09-07T08:19:15.6975088Z scale (Tensor): scalar for the output scale 2025-09-07T08:19:15.6975509Z zero_point (Tensor): scalar for the output zero point 2025-09-07T08:19:15.6975848Z 2025-09-07T08:19:15.6976012Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-09-07T08:19:15.6976270Z 2025-09-07T08:19:15.6976375Z Examples:: 2025-09-07T08:19:15.6976569Z 2025-09-07T08:19:15.6976716Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-09-07T08:19:15.6977116Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-09-07T08:19:15.6977543Z >>> input = torch.randn(20, 16, 100) 2025-09-07T08:19:15.6977875Z >>> # quantize input to quint8 2025-09-07T08:19:15.6978262Z >>> # xdoctest: +SKIP 2025-09-07T08:19:15.6978651Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-09-07T08:19:15.6979182Z ... dtype=torch.quint8) 2025-09-07T08:19:15.6979547Z >>> output = m(q_input) 2025-09-07T08:19:15.6979778Z 2025-09-07T08:19:15.6979870Z 2025-09-07T08:19:15.6980225Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.6980669Z 2025-09-07T08:19:15.7190312Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=12. 2025-09-07T08:19:15.7191307Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.7191887Z A quantized long short-term memory (LSTM). 2025-09-07T08:19:15.7192241Z 2025-09-07T08:19:15.7192597Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-09-07T08:19:15.7193010Z 2025-09-07T08:19:15.7193156Z Attributes: 2025-09-07T08:19:15.7193436Z layers : instances of the `_LSTMLayer` 2025-09-07T08:19:15.7193674Z 2025-09-07T08:19:15.7193765Z .. note:: 2025-09-07T08:19:15.7194168Z To access the weights and biases, you need to access them per layer. 2025-09-07T08:19:15.7194726Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-09-07T08:19:15.7195020Z 2025-09-07T08:19:15.7195110Z Examples:: 2025-09-07T08:19:15.7195345Z >>> # xdoctest: +SKIP 2025-09-07T08:19:15.7195697Z >>> custom_module_config = { 2025-09-07T08:19:15.7196050Z ... 'float_to_observed_custom_module_class': { 2025-09-07T08:19:15.7196480Z ... nn.LSTM: nn.quantizable.LSTM, 2025-09-07T08:19:15.7196802Z ... }, 2025-09-07T08:19:15.7197152Z ... 'observed_to_quantized_custom_module_class': { 2025-09-07T08:19:15.7197563Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-09-07T08:19:15.7197956Z ... } 2025-09-07T08:19:15.7198251Z ... } 2025-09-07T08:19:15.7198664Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-09-07T08:19:15.7199217Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-09-07T08:19:15.7199689Z 2025-09-07T08:19:15.7200062Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.7200515Z 2025-09-07T08:19:15.7943748Z msg = Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-09-07T08:19:15.7945096Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.7945482Z 2025-09-07T08:19:15.7945829Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-09-07T08:19:15.7946648Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-09-07T08:19:15.7947242Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-09-07T08:19:15.7947777Z input by the user. 2025-09-07T08:19:15.7948216Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-09-07T08:19:15.7948720Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-09-07T08:19:15.7949048Z 2025-09-07T08:19:15.7949152Z Note:: 2025-09-07T08:19:15.7949579Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-09-07T08:19:15.7950098Z 2025-09-07T08:19:15.7950177Z Args: 2025-09-07T08:19:15.7950404Z model (nn.Module): 2025-09-07T08:19:15.7950845Z The model whose layers will be sparsified. The layers that needs to be 2025-09-07T08:19:15.7951486Z sparsified should be added separately using the register_layer() function 2025-09-07T08:19:15.7951998Z aggregate_fn (Optional, Callable): 2025-09-07T08:19:15.7952525Z default aggregate_fn that is used if not specified while registering the layer. 2025-09-07T08:19:15.7953146Z specifies how inputs should be aggregated over time. 2025-09-07T08:19:15.7953804Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-09-07T08:19:15.7954295Z Example 2025-09-07T08:19:15.7954672Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-09-07T08:19:15.7955104Z reduce_fn (Optional, Callable): 2025-09-07T08:19:15.7955632Z default reduce_fn that is used if not specified while registering the layer. 2025-09-07T08:19:15.7956324Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-09-07T08:19:15.7956866Z calling agg_fn() on all inputs. 2025-09-07T08:19:15.7957195Z Example 2025-09-07T08:19:15.7957678Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-09-07T08:19:15.7958117Z mask_fn (Optional, Callable): 2025-09-07T08:19:15.7958703Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-09-07T08:19:15.7959457Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-09-07T08:19:15.7959997Z register_layer(). 2025-09-07T08:19:15.7960546Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-09-07T08:19:15.7961165Z arguments. 2025-09-07T08:19:15.7961507Z features (Optional, list): 2025-09-07T08:19:15.7961856Z default selected features to sparsify. 2025-09-07T08:19:15.7962426Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-09-07T08:19:15.7962931Z For example, 2025-09-07T08:19:15.7963394Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-09-07T08:19:15.7963950Z feature_dim (Optional, int): 2025-09-07T08:19:15.7964645Z default dimension of input features. Again, features along this dim will be chosen 2025-09-07T08:19:15.7965154Z for sparsification. 2025-09-07T08:19:15.7965517Z sparse_config (Dict): 2025-09-07T08:19:15.7965934Z Default configuration for the mask_fn. This config will be passed 2025-09-07T08:19:15.7966451Z with the mask_fn() 2025-09-07T08:19:15.7966645Z 2025-09-07T08:19:15.7966743Z Example: 2025-09-07T08:19:15.7967013Z >>> # xdoctest: +SKIP 2025-09-07T08:19:15.7967287Z >>> model = SomeModel() 2025-09-07T08:19:15.7967779Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-09-07T08:19:15.7968251Z >>> # Initialize aggregate_fn 2025-09-07T08:19:15.7968600Z >>> def agg_fn(x, y): 2025-09-07T08:19:15.7968864Z >>> return x + y 2025-09-07T08:19:15.7969116Z >>> 2025-09-07T08:19:15.7969469Z >>> # Initialize reduce_fn 2025-09-07T08:19:15.7969744Z >>> def reduce_fn(x): 2025-09-07T08:19:15.7970088Z >>> return torch.mean(x, dim=0) 2025-09-07T08:19:15.7970497Z >>> 2025-09-07T08:19:15.7970768Z >>> # Initialize mask_fn 2025-09-07T08:19:15.7971171Z >>> def mask_fn(data): 2025-09-07T08:19:15.7971579Z >>> return torch.eye(data.shape).to(data.device) 2025-09-07T08:19:15.7971984Z >>> 2025-09-07T08:19:15.7972189Z >>> 2025-09-07T08:19:15.7972409Z >>> act_sparsifier.register_layer( 2025-09-07T08:19:15.7972796Z ... model.some_layer, 2025-09-07T08:19:15.7973084Z ... aggregate_fn=agg_fn, 2025-09-07T08:19:15.7973646Z ... reduce_fn=reduce_fn, 2025-09-07T08:19:15.7973926Z ... mask_fn=mask_fn, 2025-09-07T08:19:15.7974191Z ... ) 2025-09-07T08:19:15.7974460Z >>> 2025-09-07T08:19:15.7974686Z >>> # start training process 2025-09-07T08:19:15.7974962Z >>> for _ in [...]: 2025-09-07T08:19:15.7975278Z >>> # epoch starts 2025-09-07T08:19:15.7975609Z >>> # model.forward(), compute_loss() and model.backwards() 2025-09-07T08:19:15.7976049Z >>> # epoch ends 2025-09-07T08:19:15.7976292Z >>> act_sparsifier.step() 2025-09-07T08:19:15.7976593Z >>> # end training process 2025-09-07T08:19:15.7976954Z >>> sparsifier.squash_mask() 2025-09-07T08:19:15.7977147Z 2025-09-07T08:19:15.7977428Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.7977840Z 2025-09-07T08:19:15.7978766Z msg = Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-09-07T08:19:15.7980091Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.7980484Z 2025-09-07T08:19:15.7980815Z Abstract method that needs to be implemented by the child class. 2025-09-07T08:19:15.7981466Z The expected return type should is a dictionary of name to schedule_param value 2025-09-07T08:19:15.7982114Z The returned values will be updated in sparsifier when the scheduler step() function 2025-09-07T08:19:15.7982658Z is called. 2025-09-07T08:19:15.7982779Z 2025-09-07T08:19:15.7982861Z Example: 2025-09-07T08:19:15.7983091Z >>> def get_schedule_param(self): 2025-09-07T08:19:15.7983398Z ... new_param = {} 2025-09-07T08:19:15.7983784Z ... for name in self.sparsifier.data_groups.keys(): 2025-09-07T08:19:15.7984143Z ... new_param[name] = ( 2025-09-07T08:19:15.7984549Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-09-07T08:19:15.7984959Z ... ) 2025-09-07T08:19:15.7985196Z ... return new_param 2025-09-07T08:19:15.7985372Z 2025-09-07T08:19:15.7985704Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-09-07T08:19:15.7986329Z would be halved 2025-09-07T08:19:15.7986481Z 2025-09-07T08:19:15.7986733Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.7987159Z 2025-09-07T08:19:15.8293578Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=229. 2025-09-07T08:19:15.8294771Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.8295390Z Squashes the sparse masks into the appropriate tensors. 2025-09-07T08:19:15.8295672Z 2025-09-07T08:19:15.8295907Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-09-07T08:19:15.8296475Z the module will have a `sparse_params` dict attached to it. 2025-09-07T08:19:15.8296842Z 2025-09-07T08:19:15.8296928Z Args: 2025-09-07T08:19:15.8297249Z params_to_keep: List of keys to save in the module or a dict 2025-09-07T08:19:15.8297787Z representing the modules and keys that will have 2025-09-07T08:19:15.8298367Z sparsity parameters saved 2025-09-07T08:19:15.8298852Z params_to_keep_per_layer: Dict to specify the params that should be 2025-09-07T08:19:15.8299414Z saved for specific layers. The keys in the dict 2025-09-07T08:19:15.8299880Z should be the module fqn, while the values should 2025-09-07T08:19:15.8300342Z be a list of strings with the names of the variables 2025-09-07T08:19:15.8300811Z to save in the `sparse_params` 2025-09-07T08:19:15.8301060Z 2025-09-07T08:19:15.8301149Z Examples: 2025-09-07T08:19:15.8301461Z >>> # xdoctest: +SKIP("locals are undefined") 2025-09-07T08:19:15.8301859Z >>> # Don't save any sparse params 2025-09-07T08:19:15.8302226Z >>> sparsifier.squash_mask() 2025-09-07T08:19:15.8302626Z >>> hasattr(model.submodule1, "sparse_params") 2025-09-07T08:19:15.8302988Z False 2025-09-07T08:19:15.8303173Z 2025-09-07T08:19:15.8303306Z >>> # Keep sparse params per layer 2025-09-07T08:19:15.8303636Z >>> sparsifier.squash_mask( 2025-09-07T08:19:15.8304037Z ... params_to_keep_per_layer={ 2025-09-07T08:19:15.8304401Z ... "submodule1.linear1": ("foo", "bar"), 2025-09-07T08:19:15.8304850Z ... "submodule2.linear42": ("baz",), 2025-09-07T08:19:15.8305166Z ... } 2025-09-07T08:19:15.8305471Z ... ) 2025-09-07T08:19:15.8305776Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T08:19:15.8306195Z {'foo': 42, 'bar': 24} 2025-09-07T08:19:15.8306550Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T08:19:15.8306959Z {'baz': 0.1} 2025-09-07T08:19:15.8307222Z 2025-09-07T08:19:15.8307344Z >>> # Keep sparse params for all layers 2025-09-07T08:19:15.8307826Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-09-07T08:19:15.8308276Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T08:19:15.8308695Z {'foo': 42, 'bar': 24} 2025-09-07T08:19:15.8309046Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T08:19:15.8309478Z {'foo': 42, 'bar': 24} 2025-09-07T08:19:15.8309664Z 2025-09-07T08:19:15.8309870Z >>> # Keep some sparse params for all layers, and specific ones for 2025-09-07T08:19:15.8310340Z >>> # some other layers 2025-09-07T08:19:15.8310650Z >>> sparsifier.squash_mask( 2025-09-07T08:19:15.8311048Z ... params_to_keep=("foo", "bar"), 2025-09-07T08:19:15.8311472Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-09-07T08:19:15.8311924Z ... ) 2025-09-07T08:19:15.8312227Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T08:19:15.8312666Z {'foo': 42, 'bar': 24} 2025-09-07T08:19:15.8313026Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T08:19:15.8313528Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-09-07T08:19:15.8313818Z 2025-09-07T08:19:15.8314248Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.8314615Z 2025-09-07T08:19:15.9236295Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2025-09-07T08:19:15.9237364Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:15.9237759Z 2025-09-07T08:19:15.9238011Z Config object that specifies the supported data types passed as arguments to 2025-09-07T08:19:15.9238625Z quantize ops in the reference model spec, for input and output activations, 2025-09-07T08:19:15.9239087Z weights, and biases. 2025-09-07T08:19:15.9239240Z 2025-09-07T08:19:15.9239397Z For example, consider the following reference model: 2025-09-07T08:19:15.9239837Z 2025-09-07T08:19:15.9240005Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-09-07T08:19:15.9240281Z 2025-09-07T08:19:15.9240495Z The pattern in the square brackets refers to the reference pattern of 2025-09-07T08:19:15.9241068Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-09-07T08:19:15.9241662Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-09-07T08:19:15.9242229Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-09-07T08:19:15.9242800Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-09-07T08:19:15.9243257Z the second quantize op (quant2). 2025-09-07T08:19:15.9243455Z 2025-09-07T08:19:15.9243703Z Note that the dtype here does not refer to the interface dtypes of the 2025-09-07T08:19:15.9244321Z op. For example, the "input dtype" here is not the dtype of the input 2025-09-07T08:19:15.9244859Z tensor passed to the quantized linear op. Though it can still be the 2025-09-07T08:19:15.9245398Z same as the interface dtype, this is not always the case, e.g. the 2025-09-07T08:19:15.9245937Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-09-07T08:19:15.9246494Z specified in the DTypeConfig would still be quint8. The semantics of 2025-09-07T08:19:15.9247040Z dtypes here are the same as the semantics of the dtypes specified in 2025-09-07T08:19:15.9247448Z the observers. 2025-09-07T08:19:15.9247591Z 2025-09-07T08:19:15.9247794Z These dtypes are matched against the ones specified in the user's 2025-09-07T08:19:15.9248333Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-09-07T08:19:15.9248892Z specified in the DTypeConfig (if any), then we will quantize the given 2025-09-07T08:19:15.9249442Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-09-07T08:19:15.9249946Z the pattern will not be quantized. 2025-09-07T08:19:15.9250159Z 2025-09-07T08:19:15.9250264Z Example usage:: 2025-09-07T08:19:15.9250405Z 2025-09-07T08:19:15.9250522Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:15.9250833Z >>> dtype_config1 = DTypeConfig( 2025-09-07T08:19:15.9251141Z ... input_dtype=torch.quint8, 2025-09-07T08:19:15.9251462Z ... output_dtype=torch.quint8, 2025-09-07T08:19:15.9251782Z ... weight_dtype=torch.qint8, 2025-09-07T08:19:15.9252097Z ... bias_dtype=torch.float) 2025-09-07T08:19:15.9252295Z 2025-09-07T08:19:15.9252398Z >>> dtype_config2 = DTypeConfig( 2025-09-07T08:19:15.9252732Z ... input_dtype=DTypeWithConstraints( 2025-09-07T08:19:15.9253071Z ... dtype=torch.quint8, 2025-09-07T08:19:15.9253375Z ... quant_min_lower_bound=0, 2025-09-07T08:19:15.9253687Z ... quant_max_upper_bound=255, 2025-09-07T08:19:15.9253989Z ... ), 2025-09-07T08:19:15.9254250Z ... output_dtype=DTypeWithConstraints( 2025-09-07T08:19:15.9254591Z ... dtype=torch.quint8, 2025-09-07T08:19:15.9254886Z ... quant_min_lower_bound=0, 2025-09-07T08:19:15.9255255Z ... quant_max_upper_bound=255, 2025-09-07T08:19:15.9255559Z ... ), 2025-09-07T08:19:15.9255821Z ... weight_dtype=DTypeWithConstraints( 2025-09-07T08:19:15.9256149Z ... dtype=torch.qint8, 2025-09-07T08:19:15.9256457Z ... quant_min_lower_bound=-128, 2025-09-07T08:19:15.9256788Z ... quant_max_upper_bound=127, 2025-09-07T08:19:15.9257091Z ... ), 2025-09-07T08:19:15.9257312Z ... bias_dtype=torch.float) 2025-09-07T08:19:15.9257524Z 2025-09-07T08:19:15.9257628Z >>> dtype_config1.input_dtype 2025-09-07T08:19:15.9257919Z torch.quint8 2025-09-07T08:19:15.9258056Z 2025-09-07T08:19:15.9258171Z >>> dtype_config2.input_dtype 2025-09-07T08:19:15.9258449Z torch.quint8 2025-09-07T08:19:15.9258599Z 2025-09-07T08:19:15.9258736Z >>> dtype_config2.input_dtype_with_constraints 2025-09-07T08:19:15.9259592Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-09-07T08:19:15.9260262Z 2025-09-07T08:19:15.9260517Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:15.9260886Z 2025-09-07T08:19:16.0347471Z msg = Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-09-07T08:19:16.0348539Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.0348925Z 2025-09-07T08:19:16.0349234Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-09-07T08:19:16.0349957Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-09-07T08:19:16.0350690Z though the requirements on the type of model more-so depends on the specific report the user 2025-09-07T08:19:16.0351424Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-09-07T08:19:16.0352124Z a set of Detector classes, each of which generate reports on quantization configuration 2025-09-07T08:19:16.0352609Z issues a use might have. 2025-09-07T08:19:16.0352793Z 2025-09-07T08:19:16.0352917Z Currently supports generating reports on: 2025-09-07T08:19:16.0353420Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-09-07T08:19:16.0354033Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-09-07T08:19:16.0354716Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-09-07T08:19:16.0355341Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-09-07T08:19:16.0355664Z 2025-09-07T08:19:16.0356059Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-09-07T08:19:16.0357124Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-09-07T08:19:16.0358000Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-09-07T08:19:16.0358712Z has the capability to remove all the observers it inserted as well. 2025-09-07T08:19:16.0359038Z 2025-09-07T08:19:16.0359330Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-09-07T08:19:16.0359730Z 2025-09-07T08:19:16.0360116Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-09-07T08:19:16.0360912Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-09-07T08:19:16.0361352Z 2025-09-07T08:19:16.0361636Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-09-07T08:19:16.0362275Z This set is generated by calling the get_detector_name() of each detector 2025-09-07T08:19:16.0362616Z 2025-09-07T08:19:16.0362965Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-09-07T08:19:16.0363802Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-09-07T08:19:16.0364419Z can be removed at the end if desired 2025-09-07T08:19:16.0364650Z 2025-09-07T08:19:16.0364967Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-09-07T08:19:16.0365643Z This is to ensure we only insert observers once with the ModelReport instance 2025-09-07T08:19:16.0365996Z 2025-09-07T08:19:16.0366263Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-09-07T08:19:16.0366947Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-09-07T08:19:16.0367672Z instance. This also allows the functionality where we can generate the report multiple times 2025-09-07T08:19:16.0368361Z as long as we haven't removed the observers yet. 2025-09-07T08:19:16.0368630Z 2025-09-07T08:19:16.0368711Z Note: 2025-09-07T08:19:16.0369138Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-09-07T08:19:16.0369874Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-09-07T08:19:16.0370599Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-09-07T08:19:16.0371131Z the QuantizationTracer class. 2025-09-07T08:19:16.0371344Z 2025-09-07T08:19:16.0371447Z General Flow for Fx workflow: 2025-09-07T08:19:16.0372032Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-09-07T08:19:16.0372652Z 2.) Prepare your model with prepare_fx 2025-09-07T08:19:16.0373121Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-09-07T08:19:16.0373769Z 4.) Calibrate your model with data 2025-09-07T08:19:16.0374340Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-09-07T08:19:16.0374906Z Optional 2025-09-07T08:19:16.0375297Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-09-07T08:19:16.0375944Z 7.) To help in parsing report information and debugging, view report info as a: 2025-09-07T08:19:16.0376403Z - Table 2025-09-07T08:19:16.0376635Z - Histogram 2025-09-07T08:19:16.0376860Z - Line plot 2025-09-07T08:19:16.0377325Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-09-07T08:19:16.0377770Z 2025-09-07T08:19:16.0377883Z Example (with QuantizationTracer): 2025-09-07T08:19:16.0378194Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.0378517Z >>> # get the necessary qconfig 2025-09-07T08:19:16.0378838Z >>> config = PrepareCustomConfig() 2025-09-07T08:19:16.0379213Z >>> skipped_module_names, skipped_module_classes = ( 2025-09-07T08:19:16.0379645Z ... get_skipped_module_name_and_classes(config, False) 2025-09-07T08:19:16.0379992Z ... ) 2025-09-07T08:19:16.0380123Z 2025-09-07T08:19:16.0380250Z >>> # initialize our model and get GraphModule 2025-09-07T08:19:16.0380593Z >>> model = SomeModel() 2025-09-07T08:19:16.0381026Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-09-07T08:19:16.0381570Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-09-07T08:19:16.0381849Z 2025-09-07T08:19:16.0381998Z >>> # get our set of detectors and ModelReport instance 2025-09-07T08:19:16.0382367Z >>> detector_set = set( 2025-09-07T08:19:16.0382628Z ... [ 2025-09-07T08:19:16.0382895Z ... DynamicStaticDetector(tolerance=0.5), 2025-09-07T08:19:16.0383328Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-09-07T08:19:16.0383722Z ... ] 2025-09-07T08:19:16.0383932Z ... ) 2025-09-07T08:19:16.0384266Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-09-07T08:19:16.0384632Z 2025-09-07T08:19:16.0384785Z >>> # now we insert the observers and calibrate the model 2025-09-07T08:19:16.0385317Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-09-07T08:19:16.0385834Z >>> for i in range(num_callibration_batches): 2025-09-07T08:19:16.0386216Z >>> example_input = get_callibration_input() 2025-09-07T08:19:16.0386589Z >>> tracer_model_with_observers(example_input) 2025-09-07T08:19:16.0386846Z 2025-09-07T08:19:16.0387103Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-09-07T08:19:16.0387642Z >>> reports = tracer_reporter.generate_model_report( 2025-09-07T08:19:16.0388039Z ... remove_inserted_observers=True 2025-09-07T08:19:16.0388356Z ... ) 2025-09-07T08:19:16.0388472Z 2025-09-07T08:19:16.0388862Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-09-07T08:19:16.0389374Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-09-07T08:19:16.0389645Z 2025-09-07T08:19:16.0389888Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-09-07T08:19:16.0390439Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-09-07T08:19:16.0390746Z 2025-09-07T08:19:16.0391027Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-09-07T08:19:16.0391651Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-09-07T08:19:16.0391977Z 2025-09-07T08:19:16.0391981Z 2025-09-07T08:19:16.0392247Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.0392616Z 2025-09-07T08:19:16.0431287Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2025-09-07T08:19:16.0432834Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.0433339Z 2025-09-07T08:19:16.0433616Z Takes in optional filter values and generates two tables with desired information. 2025-09-07T08:19:16.0434082Z 2025-09-07T08:19:16.0434294Z The generated tables are presented in both a list-of-lists format 2025-09-07T08:19:16.0434681Z 2025-09-07T08:19:16.0434900Z The reason for the two tables are that they handle different things: 2025-09-07T08:19:16.0435456Z 1.) the first table handles all tensor level information 2025-09-07T08:19:16.0435943Z 2.) the second table handles and displays all channel based information 2025-09-07T08:19:16.0436355Z 2025-09-07T08:19:16.0436671Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T08:19:16.0437734Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T08:19:16.0438755Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T08:19:16.0439230Z 2025-09-07T08:19:16.0439339Z Tensor table columns: 2025-09-07T08:19:16.0439752Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T08:19:16.0440290Z ---- --------- --------- --------- --------- --------- 2025-09-07T08:19:16.0440587Z 2025-09-07T08:19:16.0440732Z Per-Channel table columns: 2025-09-07T08:19:16.0441281Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T08:19:16.0441853Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T08:19:16.0442136Z 2025-09-07T08:19:16.0442217Z Args: 2025-09-07T08:19:16.0442674Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T08:19:16.0443183Z contain this filter substring 2025-09-07T08:19:16.0443631Z Default = "", results in all the features being printed 2025-09-07T08:19:16.0444365Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T08:19:16.0445083Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T08:19:16.0445463Z 2025-09-07T08:19:16.0445647Z Returns a dictionary with two keys: 2025-09-07T08:19:16.0446069Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-09-07T08:19:16.0446553Z "tensor_level_info", "channel_level_info" 2025-09-07T08:19:16.0446900Z Each key maps to a tuple with: 2025-09-07T08:19:16.0447269Z A list of the headers of each table 2025-09-07T08:19:16.0447702Z A list of lists containing the table information row by row 2025-09-07T08:19:16.0448242Z The 0th index row will contain the headers of the columns 2025-09-07T08:19:16.0448661Z The rest of the rows will contain data 2025-09-07T08:19:16.0448968Z 2025-09-07T08:19:16.0449056Z Example Use: 2025-09-07T08:19:16.0449398Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:16.0449857Z >>> mod_report_visualizer.generate_filtered_tables( 2025-09-07T08:19:16.0450315Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T08:19:16.0450983Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-09-07T08:19:16.0451446Z 2025-09-07T08:19:16.0451699Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.0452155Z 2025-09-07T08:19:16.0453143Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=399. 2025-09-07T08:19:16.0454573Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.0454967Z 2025-09-07T08:19:16.0455313Z Takes in optional filter values and prints out formatted tables of the information. 2025-09-07T08:19:16.0455707Z 2025-09-07T08:19:16.0456127Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-09-07T08:19:16.0456777Z 1.) the first table handles all tensor level information 2025-09-07T08:19:16.0457302Z 2.) the second table handles and displays all channel based information 2025-09-07T08:19:16.0457710Z 2025-09-07T08:19:16.0458025Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T08:19:16.0458867Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T08:19:16.0459746Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T08:19:16.0460290Z 2025-09-07T08:19:16.0460450Z Tensor table columns: 2025-09-07T08:19:16.0460856Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T08:19:16.0461320Z ---- --------- --------- --------- --------- --------- 2025-09-07T08:19:16.0461650Z 2025-09-07T08:19:16.0461767Z Per-Channel table columns: 2025-09-07T08:19:16.0461944Z 2025-09-07T08:19:16.0462174Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T08:19:16.0462740Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T08:19:16.0463031Z 2025-09-07T08:19:16.0463139Z Args: 2025-09-07T08:19:16.0463568Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T08:19:16.0464125Z contain this filter substring 2025-09-07T08:19:16.0464503Z Default = "", results in all the features being printed 2025-09-07T08:19:16.0465093Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T08:19:16.0465795Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T08:19:16.0466170Z 2025-09-07T08:19:16.0466261Z Example Use: 2025-09-07T08:19:16.0466619Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:16.0467008Z >>> mod_report_visualizer.generate_table_visualization( 2025-09-07T08:19:16.0467555Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T08:19:16.0468017Z ... ) 2025-09-07T08:19:16.0468331Z >>> # prints out neatly formatted table with per_channel_min info 2025-09-07T08:19:16.0468814Z >>> # for all modules in block 1 of the model 2025-09-07T08:19:16.0469059Z 2025-09-07T08:19:16.0469309Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.0469763Z 2025-09-07T08:19:16.0470747Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=564. 2025-09-07T08:19:16.0472097Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.0472534Z 2025-09-07T08:19:16.0472787Z Takes in a feature and optional module_filter and plots of the desired data. 2025-09-07T08:19:16.0473138Z 2025-09-07T08:19:16.0473594Z For per channel features, it averages the value across the channels and plots a point 2025-09-07T08:19:16.0474321Z per module. The reason for this is that for models with hundreds of channels, it can 2025-09-07T08:19:16.0474982Z be hard to differentiate one channel line from another, and so the point of generating 2025-09-07T08:19:16.0475651Z a single average point per module is to give a sense of general trends that encourage 2025-09-07T08:19:16.0476139Z further deep dives. 2025-09-07T08:19:16.0476286Z 2025-09-07T08:19:16.0476378Z Note: 2025-09-07T08:19:16.0476749Z Only features in the report that have tensor value data are plottable by this class 2025-09-07T08:19:16.0487120Z When the tensor information is plotted, it will plot: 2025-09-07T08:19:16.0487643Z idx as the x val, feature value as the y_val 2025-09-07T08:19:16.0488077Z When the channel information is plotted, it will plot: 2025-09-07T08:19:16.0488641Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-09-07T08:19:16.0489262Z The reason for this is that we want to be able to compare values across the 2025-09-07T08:19:16.0489851Z channels for same layer, and it will be hard if values are staggered by idx 2025-09-07T08:19:16.0490381Z This means each module is represented by only 1 x value 2025-09-07T08:19:16.0490742Z Args: 2025-09-07T08:19:16.0491092Z feature_filter (str): Filters the features presented to only those that 2025-09-07T08:19:16.0491549Z contain this filter substring 2025-09-07T08:19:16.0492026Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T08:19:16.0492756Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T08:19:16.0493140Z 2025-09-07T08:19:16.0493235Z Example Use: 2025-09-07T08:19:16.0493502Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:16.0493907Z >>> mod_report_visualizer.generate_plot_visualization( 2025-09-07T08:19:16.0494370Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T08:19:16.0494768Z ... ) 2025-09-07T08:19:16.0495075Z >>> # outputs line plot of per_channel_min information for all 2025-09-07T08:19:16.0495555Z >>> # modules in block1 of model each channel gets it's own line, 2025-09-07T08:19:16.0496031Z >>> # and it's plotted across the in-order modules on the x-axis 2025-09-07T08:19:16.0496316Z 2025-09-07T08:19:16.0496572Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.0496950Z 2025-09-07T08:19:16.0497893Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=643. 2025-09-07T08:19:16.0499366Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.0499763Z 2025-09-07T08:19:16.0500038Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-09-07T08:19:16.0500430Z 2025-09-07T08:19:16.0500526Z Note: 2025-09-07T08:19:16.0500898Z Only features in the report that have tensor value data can be viewed as a histogram 2025-09-07T08:19:16.0501547Z If you want to plot a histogram from all the channel values of a specific feature for 2025-09-07T08:19:16.0502181Z a specific model, make sure to specify both the model and the feature properly 2025-09-07T08:19:16.0502794Z in the filters and you should be able to see a distribution of the channel data 2025-09-07T08:19:16.0503155Z 2025-09-07T08:19:16.0503249Z Args: 2025-09-07T08:19:16.0503619Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T08:19:16.0504199Z contain this filter substring 2025-09-07T08:19:16.0504586Z Default = "", results in all the features being printed 2025-09-07T08:19:16.0505132Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T08:19:16.0505762Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T08:19:16.0506343Z num_bins (int, optional): The number of bins to create the histogram with 2025-09-07T08:19:16.0506880Z Default = 10, the values will be split into 10 equal sized bins 2025-09-07T08:19:16.0507194Z 2025-09-07T08:19:16.0507284Z Example Use: 2025-09-07T08:19:16.0507521Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.0507987Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-09-07T08:19:16.0508610Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T08:19:16.0509013Z ... ) 2025-09-07T08:19:16.0509420Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-09-07T08:19:16.0510068Z information is gathered across all channels for all modules in block 1 for the 2025-09-07T08:19:16.0510669Z per_channel_min and is displayed in a histogram of equally sized bins 2025-09-07T08:19:16.0511014Z 2025-09-07T08:19:16.0511266Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.0511643Z 2025-09-07T08:19:16.1819824Z msg = Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py line=734. 2025-09-07T08:19:16.1820853Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.1821697Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-09-07T08:19:16.1822631Z Label will only appear if CPU activity tracing is enabled. 2025-09-07T08:19:16.1822999Z 2025-09-07T08:19:16.1823147Z It is useful when tracing the code profile. 2025-09-07T08:19:16.1823412Z 2025-09-07T08:19:16.1823494Z Args: 2025-09-07T08:19:16.1823827Z name (str): Label assigned to the block of code. 2025-09-07T08:19:16.1824272Z node_id (int): ID of node, for distributed profiling. Unset in 2025-09-07T08:19:16.1824732Z non-distributed cases. 2025-09-07T08:19:16.1824934Z 2025-09-07T08:19:16.1825022Z Example: 2025-09-07T08:19:16.1825398Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-09-07T08:19:16.1825832Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-09-07T08:19:16.1826287Z >>> with torch.autograd.profiler.profile() as prof: 2025-09-07T08:19:16.1826655Z ... y = x**2 2025-09-07T08:19:16.1827041Z ... with torch.autograd.profiler.record_function( 2025-09-07T08:19:16.1827418Z ... "label-z" 2025-09-07T08:19:16.1827746Z ... ): # label the block 2025-09-07T08:19:16.1828051Z ... z = y**3 2025-09-07T08:19:16.1828329Z ... y.backward() 2025-09-07T08:19:16.1828729Z >>> # xdoctest: +IGNORE_WANT 2025-09-07T08:19:16.1829059Z >>> # NOTE: some columns were removed for brevity 2025-09-07T08:19:16.1829588Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-09-07T08:19:16.1830168Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T08:19:16.1830672Z Name Self CPU total % CPU time avg Number of Calls 2025-09-07T08:19:16.1831241Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T08:19:16.1831742Z pow 60.77% 47.470us 3 2025-09-07T08:19:16.1832132Z mul 21.73% 25.465us 2 2025-09-07T08:19:16.1832624Z PowBackward0 12.03% 121.891us 1 2025-09-07T08:19:16.1833328Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-09-07T08:19:16.1833857Z label-z 2.13% 12.421us 1 2025-09-07T08:19:16.1834367Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-09-07T08:19:16.1834948Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T08:19:16.1835376Z Self CPU time total: 234.344us 2025-09-07T08:19:16.1835760Z CUDA time total: 0.000us 2025-09-07T08:19:16.1835952Z 2025-09-07T08:19:16.1836044Z 2025-09-07T08:19:16.1836468Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.1836849Z 2025-09-07T08:19:16.3515437Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=701. 2025-09-07T08:19:16.3516549Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:16.3516939Z 2025-09-07T08:19:16.3517223Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-09-07T08:19:16.3517960Z The submesh created consists of the dimensions and the communicators indicated by 2025-09-07T08:19:16.3518502Z ``mesh_dim_names`` 2025-09-07T08:19:16.3518664Z 2025-09-07T08:19:16.3518748Z Args: 2025-09-07T08:19:16.3519165Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-09-07T08:19:16.3519771Z mesh dimension of the DeviceMesh to create the submesh for. 2025-09-07T08:19:16.3520146Z Returns: 2025-09-07T08:19:16.3520392Z A :class:`DeviceMesh` object 2025-09-07T08:19:16.3520645Z 2025-09-07T08:19:16.3520930Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-09-07T08:19:16.3521685Z In the first example: 2025-09-07T08:19:16.3522174Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-09-07T08:19:16.3522841Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-09-07T08:19:16.3523440Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-09-07T08:19:16.3524257Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-09-07T08:19:16.3524908Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-09-07T08:19:16.3525548Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-09-07T08:19:16.3525925Z 2025-09-07T08:19:16.3526046Z In the second example: 2025-09-07T08:19:16.3526483Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-09-07T08:19:16.3527213Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-09-07T08:19:16.3527948Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-09-07T08:19:16.3528743Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-09-07T08:19:16.3529186Z 2025-09-07T08:19:16.3529290Z Example:: 2025-09-07T08:19:16.3529444Z 2025-09-07T08:19:16.3529548Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T08:19:16.3529990Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-09-07T08:19:16.3530365Z >>> 2025-09-07T08:19:16.3530741Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-09-07T08:19:16.3531210Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-09-07T08:19:16.3531787Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T08:19:16.3532331Z >>> tp_mesh = mesh_2d["tp"] 2025-09-07T08:19:16.3532627Z >>> dp_mesh = mesh_2d["dp"] 2025-09-07T08:19:16.3532896Z >>> 2025-09-07T08:19:16.3533162Z >>> # Initialize a 3D mesh. 2025-09-07T08:19:16.3533769Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-09-07T08:19:16.3534527Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-09-07T08:19:16.3535069Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-09-07T08:19:16.3535460Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-09-07T08:19:16.3535689Z 2025-09-07T08:19:16.3536425Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2025-09-07T08:19:16.3537275Z 2025-09-07T08:19:16.3537532Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T08:19:16.3538087Z ^ 2025-09-07T08:19:16.3860040Z msg = Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2705. 2025-09-07T08:19:16.3861118Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.3861589Z 2025-09-07T08:19:16.3861844Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-09-07T08:19:16.3862292Z 2025-09-07T08:19:16.3862550Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-09-07T08:19:16.3863184Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-09-07T08:19:16.3863492Z 2025-09-07T08:19:16.3863590Z Args: 2025-09-07T08:19:16.3863989Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-09-07T08:19:16.3864638Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-09-07T08:19:16.3865428Z matters and it needs to match with corresponding isend/irecv on the 2025-09-07T08:19:16.3865929Z remote end. 2025-09-07T08:19:16.3866163Z 2025-09-07T08:19:16.3866278Z Returns: 2025-09-07T08:19:16.3866640Z A list of distributed request objects returned by calling the corresponding 2025-09-07T08:19:16.3867204Z op in the op_list. 2025-09-07T08:19:16.3867374Z 2025-09-07T08:19:16.3867459Z Examples: 2025-09-07T08:19:16.3867691Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T08:19:16.3868079Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-09-07T08:19:16.3868536Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-09-07T08:19:16.3869016Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-09-07T08:19:16.3869453Z >>> recv_op = dist.P2POp( 2025-09-07T08:19:16.3869825Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-09-07T08:19:16.3870224Z ... ) 2025-09-07T08:19:16.3870483Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-09-07T08:19:16.3870832Z >>> for req in reqs: 2025-09-07T08:19:16.3871076Z >>> req.wait() 2025-09-07T08:19:16.3871353Z >>> recv_tensor 2025-09-07T08:19:16.3871681Z tensor([2, 3]) # Rank 0 2025-09-07T08:19:16.3871959Z tensor([0, 1]) # Rank 1 2025-09-07T08:19:16.3872133Z 2025-09-07T08:19:16.3872400Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-09-07T08:19:16.3872982Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-09-07T08:19:16.3873559Z lead to unexpected hang issues. 2025-09-07T08:19:16.3873776Z 2025-09-07T08:19:16.3873981Z In addition, if this API is the first collective call in the ``group`` 2025-09-07T08:19:16.3874531Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-09-07T08:19:16.3875092Z this API call; otherwise, the behavior is undefined. If this API call is 2025-09-07T08:19:16.3875643Z not the first collective call in the ``group``, batched P2P operations 2025-09-07T08:19:16.3876183Z involving only a subset of ranks of the ``group`` are allowed. 2025-09-07T08:19:16.3876641Z 2025-09-07T08:19:16.3876894Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.3877276Z 2025-09-07T08:19:16.3877867Z msg = Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2837. 2025-09-07T08:19:16.3879011Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.3879460Z 2025-09-07T08:19:16.3879741Z Reduces the tensor data across all machines in a way that all get the final result. 2025-09-07T08:19:16.3880137Z 2025-09-07T08:19:16.3880364Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-09-07T08:19:16.3880723Z 2025-09-07T08:19:16.3880941Z Complex tensors are supported. 2025-09-07T08:19:16.3881143Z 2025-09-07T08:19:16.3881227Z Args: 2025-09-07T08:19:16.3881626Z tensor (Tensor): Input and output of the collective. The function 2025-09-07T08:19:16.3882065Z operates in-place. 2025-09-07T08:19:16.3882574Z op (optional): One of the values from 2025-09-07T08:19:16.3882932Z ``torch.distributed.ReduceOp`` 2025-09-07T08:19:16.3883587Z enum. Specifies an operation used for element-wise reductions. 2025-09-07T08:19:16.3884503Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:16.3885140Z the default process group will be used. 2025-09-07T08:19:16.3885931Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T08:19:16.3886447Z 2025-09-07T08:19:16.3886531Z Returns: 2025-09-07T08:19:16.3886795Z Async work handle, if async_op is set to True. 2025-09-07T08:19:16.3887200Z None, if not async_op or if not part of the group 2025-09-07T08:19:16.3887452Z 2025-09-07T08:19:16.3887553Z Examples: 2025-09-07T08:19:16.3887875Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T08:19:16.3888232Z >>> # All tensors below are of torch.int64 type. 2025-09-07T08:19:16.3888609Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:16.3888962Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T08:19:16.3889419Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T08:19:16.3889847Z >>> tensor 2025-09-07T08:19:16.3890094Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T08:19:16.3890427Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T08:19:16.3890776Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T08:19:16.3891089Z >>> tensor 2025-09-07T08:19:16.3891331Z tensor([4, 6], device='cuda:0') # Rank 0 2025-09-07T08:19:16.3891660Z tensor([4, 6], device='cuda:1') # Rank 1 2025-09-07T08:19:16.3891874Z 2025-09-07T08:19:16.3892022Z >>> # All tensors below are of torch.cfloat type. 2025-09-07T08:19:16.3892372Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:16.3892700Z >>> tensor = torch.tensor( 2025-09-07T08:19:16.3893033Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T08:19:16.3893391Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T08:19:16.3893699Z >>> tensor 2025-09-07T08:19:16.3893975Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T08:19:16.3894362Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T08:19:16.3894739Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T08:19:16.3895147Z >>> tensor 2025-09-07T08:19:16.3895420Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-09-07T08:19:16.3895835Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-09-07T08:19:16.3896076Z 2025-09-07T08:19:16.3896080Z 2025-09-07T08:19:16.3896409Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.3896818Z 2025-09-07T08:19:16.3897458Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3201. 2025-09-07T08:19:16.3898509Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.3898893Z 2025-09-07T08:19:16.3899129Z Gathers picklable objects from the whole group in a single process. 2025-09-07T08:19:16.3899464Z 2025-09-07T08:19:16.3899693Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-09-07T08:19:16.3900201Z object must be picklable in order to be gathered. 2025-09-07T08:19:16.3900464Z 2025-09-07T08:19:16.3900544Z Args: 2025-09-07T08:19:16.3900788Z obj (Any): Input object. Must be picklable. 2025-09-07T08:19:16.3901223Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-09-07T08:19:16.3901744Z should be correctly sized as the size of the group for this 2025-09-07T08:19:16.3902266Z collective and will contain the output. Must be ``None`` on non-dst 2025-09-07T08:19:16.3902708Z ranks. (default is ``None``) 2025-09-07T08:19:16.3903241Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-09-07T08:19:16.3903871Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-09-07T08:19:16.3904423Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:16.3904964Z the default process group will be used. Default is ``None``. 2025-09-07T08:19:16.3905618Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-09-07T08:19:16.3906086Z 2025-09-07T08:19:16.3906182Z Returns: 2025-09-07T08:19:16.3906474Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-09-07T08:19:16.3906879Z output of the collective. 2025-09-07T08:19:16.3907065Z 2025-09-07T08:19:16.3907296Z .. note:: Note that this API differs slightly from the gather collective 2025-09-07T08:19:16.3907844Z since it does not provide an async_op handle and thus will be a blocking 2025-09-07T08:19:16.3908298Z call. 2025-09-07T08:19:16.3908427Z 2025-09-07T08:19:16.3908665Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-09-07T08:19:16.3909229Z of objects must be moved to the GPU device before communication takes 2025-09-07T08:19:16.3909715Z place. In this case, the device used is given by 2025-09-07T08:19:16.3910185Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-09-07T08:19:16.3910737Z ensure that this is set so that each rank has an individual GPU, via 2025-09-07T08:19:16.3911179Z ``torch.cuda.set_device()``. 2025-09-07T08:19:16.3911372Z 2025-09-07T08:19:16.3911471Z .. warning:: 2025-09-07T08:19:16.3911848Z Object collectives have a number of serious performance and scalability 2025-09-07T08:19:16.3912369Z limitations. See :ref:`object_collectives` for details. 2025-09-07T08:19:16.3912664Z 2025-09-07T08:19:16.3912749Z .. warning:: 2025-09-07T08:19:16.3913080Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-09-07T08:19:16.3913617Z known to be insecure. It is possible to construct malicious pickle data 2025-09-07T08:19:16.3914193Z which will execute arbitrary code during unpickling. Only call this 2025-09-07T08:19:16.3914634Z function with data you trust. 2025-09-07T08:19:16.3914842Z 2025-09-07T08:19:16.3914927Z .. warning:: 2025-09-07T08:19:16.3915265Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-09-07T08:19:16.3915831Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-09-07T08:19:16.3916333Z pickled. Please consider using :func:`gather` instead. 2025-09-07T08:19:16.3916619Z 2025-09-07T08:19:16.3916704Z Example:: 2025-09-07T08:19:16.3916962Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T08:19:16.3917388Z >>> # Note: Process group initialization omitted on each rank. 2025-09-07T08:19:16.3917785Z >>> import torch.distributed as dist 2025-09-07T08:19:16.3918111Z >>> # Assumes world_size of 3. 2025-09-07T08:19:16.3918485Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-09-07T08:19:16.3918957Z >>> output = [None for _ in gather_objects] 2025-09-07T08:19:16.3919280Z >>> dist.gather_object( 2025-09-07T08:19:16.3919577Z ... gather_objects[dist.get_rank()], 2025-09-07T08:19:16.3919935Z ... output if dist.get_rank() == 0 else None, 2025-09-07T08:19:16.3920264Z ... dst=0 2025-09-07T08:19:16.3920472Z ... ) 2025-09-07T08:19:16.3920677Z >>> # On rank 0 2025-09-07T08:19:16.3920908Z >>> output 2025-09-07T08:19:16.3921131Z ['foo', 12, {1: 2}] 2025-09-07T08:19:16.3921283Z 2025-09-07T08:19:16.3921537Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.3921924Z 2025-09-07T08:19:16.3927263Z msg = Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3849. 2025-09-07T08:19:16.3928293Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.3928692Z 2025-09-07T08:19:16.3928840Z Gathers tensors from the whole group in a list. 2025-09-07T08:19:16.3929093Z 2025-09-07T08:19:16.3929245Z Complex and uneven sized tensors are supported. 2025-09-07T08:19:16.3929495Z 2025-09-07T08:19:16.3929604Z Args: 2025-09-07T08:19:16.3929886Z tensor_list (list[Tensor]): Output list. It should contain 2025-09-07T08:19:16.3930399Z correctly-sized tensors to be used for output of the collective. 2025-09-07T08:19:16.3930853Z Uneven sized tensors are supported. 2025-09-07T08:19:16.3931272Z tensor (Tensor): Tensor to be broadcast from current process. 2025-09-07T08:19:16.3931811Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:16.3932280Z the default process group will be used. 2025-09-07T08:19:16.3932716Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T08:19:16.3933108Z 2025-09-07T08:19:16.3933193Z Returns: 2025-09-07T08:19:16.3933460Z Async work handle, if async_op is set to True. 2025-09-07T08:19:16.3933853Z None, if not async_op or if not part of the group 2025-09-07T08:19:16.3934121Z 2025-09-07T08:19:16.3934207Z Examples: 2025-09-07T08:19:16.3934468Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T08:19:16.3934853Z >>> # All tensors below are of torch.int64 dtype. 2025-09-07T08:19:16.3935204Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:16.3935547Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T08:19:16.3935869Z >>> tensor_list = [ 2025-09-07T08:19:16.3936234Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-09-07T08:19:16.3936641Z ... ] 2025-09-07T08:19:16.3936849Z >>> tensor_list 2025-09-07T08:19:16.3937188Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-09-07T08:19:16.3937707Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-09-07T08:19:16.3938257Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T08:19:16.3938717Z >>> tensor 2025-09-07T08:19:16.3938964Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T08:19:16.3939295Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T08:19:16.3939632Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T08:19:16.3939935Z >>> tensor_list 2025-09-07T08:19:16.3940276Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-09-07T08:19:16.3940787Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-09-07T08:19:16.3941099Z 2025-09-07T08:19:16.3941253Z >>> # All tensors below are of torch.cfloat dtype. 2025-09-07T08:19:16.3941608Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:16.3941924Z >>> tensor_list = [ 2025-09-07T08:19:16.3942298Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-09-07T08:19:16.3942722Z ... ] 2025-09-07T08:19:16.3942917Z >>> tensor_list 2025-09-07T08:19:16.3943372Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-09-07T08:19:16.3943990Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-09-07T08:19:16.3944461Z >>> tensor = torch.tensor( 2025-09-07T08:19:16.3944792Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T08:19:16.3945151Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T08:19:16.3945425Z >>> tensor 2025-09-07T08:19:16.3945696Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T08:19:16.3946069Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T08:19:16.3946435Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T08:19:16.3946750Z >>> tensor_list 2025-09-07T08:19:16.3947145Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-09-07T08:19:16.3947761Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-09-07T08:19:16.3948127Z 2025-09-07T08:19:16.3948131Z 2025-09-07T08:19:16.3948381Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.3948763Z 2025-09-07T08:19:16.3985361Z msg = Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4555. 2025-09-07T08:19:16.3986524Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.3986915Z 2025-09-07T08:19:16.3987168Z Split input tensor and then scatter the split list to all processes in a group. 2025-09-07T08:19:16.3987537Z 2025-09-07T08:19:16.3987811Z Later the received tensors are concatenated from all the processes in the group 2025-09-07T08:19:16.3988317Z and returned as a single output tensor. 2025-09-07T08:19:16.3988642Z 2025-09-07T08:19:16.3988754Z Complex tensors are supported. 2025-09-07T08:19:16.3988961Z 2025-09-07T08:19:16.3989042Z Args: 2025-09-07T08:19:16.3989341Z output (Tensor): Gathered concatenated output tensor. 2025-09-07T08:19:16.3989754Z input (Tensor): Input tensor to scatter. 2025-09-07T08:19:16.3990196Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-09-07T08:19:16.3990743Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-09-07T08:19:16.3991171Z equally by ``world_size``. 2025-09-07T08:19:16.3991586Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-09-07T08:19:16.3992119Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-09-07T08:19:16.3992526Z equally by ``world_size``. 2025-09-07T08:19:16.3992955Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:16.3993427Z the default process group will be used. 2025-09-07T08:19:16.3993863Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T08:19:16.3994170Z 2025-09-07T08:19:16.3994257Z Returns: 2025-09-07T08:19:16.3994578Z Async work handle, if async_op is set to True. 2025-09-07T08:19:16.3994979Z None, if not async_op or if not part of the group. 2025-09-07T08:19:16.3995234Z 2025-09-07T08:19:16.3995342Z .. warning:: 2025-09-07T08:19:16.3995649Z `all_to_all_single` is experimental and subject to change. 2025-09-07T08:19:16.3995929Z 2025-09-07T08:19:16.3996044Z Examples: 2025-09-07T08:19:16.3996415Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T08:19:16.3996885Z >>> input = torch.arange(4) + rank * 4 2025-09-07T08:19:16.3997303Z >>> input 2025-09-07T08:19:16.3997593Z tensor([0, 1, 2, 3]) # Rank 0 2025-09-07T08:19:16.3997967Z tensor([4, 5, 6, 7]) # Rank 1 2025-09-07T08:19:16.3998261Z tensor([8, 9, 10, 11]) # Rank 2 2025-09-07T08:19:16.3998557Z tensor([12, 13, 14, 15]) # Rank 3 2025-09-07T08:19:16.3998891Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T08:19:16.3999316Z >>> dist.all_to_all_single(output, input) 2025-09-07T08:19:16.3999744Z >>> output 2025-09-07T08:19:16.3999981Z tensor([0, 4, 8, 12]) # Rank 0 2025-09-07T08:19:16.4000276Z tensor([1, 5, 9, 13]) # Rank 1 2025-09-07T08:19:16.4000570Z tensor([2, 6, 10, 14]) # Rank 2 2025-09-07T08:19:16.4000852Z tensor([3, 7, 11, 15]) # Rank 3 2025-09-07T08:19:16.4001076Z 2025-09-07T08:19:16.4001237Z >>> # Essentially, it is similar to following operation: 2025-09-07T08:19:16.4001660Z >>> scatter_list = list(input.chunk(world_size)) 2025-09-07T08:19:16.4002051Z >>> gather_list = list(output.chunk(world_size)) 2025-09-07T08:19:16.4002393Z >>> for i in range(world_size): 2025-09-07T08:19:16.4002827Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-09-07T08:19:16.4003191Z 2025-09-07T08:19:16.4003310Z >>> # Another example with uneven split 2025-09-07T08:19:16.4003630Z >>> input 2025-09-07T08:19:16.4003905Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T08:19:16.4004439Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T08:19:16.4004884Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T08:19:16.4005326Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T08:19:16.4005705Z >>> input_splits 2025-09-07T08:19:16.4005975Z [2, 2, 1, 1] # Rank 0 2025-09-07T08:19:16.4006348Z [3, 2, 2, 2] # Rank 1 2025-09-07T08:19:16.4006714Z [2, 1, 1, 1] # Rank 2 2025-09-07T08:19:16.4007077Z [2, 2, 2, 1] # Rank 3 2025-09-07T08:19:16.4007407Z >>> output_splits 2025-09-07T08:19:16.4007727Z [2, 3, 2, 2] # Rank 0 2025-09-07T08:19:16.4008098Z [2, 2, 1, 2] # Rank 1 2025-09-07T08:19:16.4008467Z [1, 2, 1, 2] # Rank 2 2025-09-07T08:19:16.4008837Z [1, 2, 1, 1] # Rank 3 2025-09-07T08:19:16.4009159Z >>> output = ... 2025-09-07T08:19:16.4009521Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-09-07T08:19:16.4009926Z >>> output 2025-09-07T08:19:16.4010214Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-09-07T08:19:16.4010635Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-09-07T08:19:16.4011065Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-09-07T08:19:16.4011493Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-09-07T08:19:16.4011765Z 2025-09-07T08:19:16.4011769Z 2025-09-07T08:19:16.4011943Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T08:19:16.4012349Z >>> input = torch.tensor( 2025-09-07T08:19:16.4012662Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T08:19:16.4013020Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T08:19:16.4013291Z >>> input 2025-09-07T08:19:16.4013602Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-09-07T08:19:16.4014055Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-09-07T08:19:16.4014528Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-09-07T08:19:16.4015015Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-09-07T08:19:16.4015563Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T08:19:16.4015928Z >>> dist.all_to_all_single(output, input) 2025-09-07T08:19:16.4016246Z >>> output 2025-09-07T08:19:16.4016556Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-09-07T08:19:16.4017080Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-09-07T08:19:16.4017543Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-09-07T08:19:16.4018012Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-09-07T08:19:16.4018312Z 2025-09-07T08:19:16.4018561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.4018941Z 2025-09-07T08:19:16.4019505Z msg = Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4697. 2025-09-07T08:19:16.4020440Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.4020816Z 2025-09-07T08:19:16.4021192Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-09-07T08:19:16.4021676Z 2025-09-07T08:19:16.4021787Z Complex tensors are supported. 2025-09-07T08:19:16.4021996Z 2025-09-07T08:19:16.4022077Z Args: 2025-09-07T08:19:16.4022415Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-09-07T08:19:16.4022840Z per rank. 2025-09-07T08:19:16.4023201Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-09-07T08:19:16.4023781Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:16.4024257Z the default process group will be used. 2025-09-07T08:19:16.4024691Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T08:19:16.4025001Z 2025-09-07T08:19:16.4025095Z Returns: 2025-09-07T08:19:16.4025341Z Async work handle, if async_op is set to True. 2025-09-07T08:19:16.4025736Z None, if not async_op or if not part of the group. 2025-09-07T08:19:16.4026001Z 2025-09-07T08:19:16.4026126Z .. warning:: 2025-09-07T08:19:16.4026407Z `all_to_all` is experimental and subject to change. 2025-09-07T08:19:16.4026659Z 2025-09-07T08:19:16.4026748Z Examples: 2025-09-07T08:19:16.4026989Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T08:19:16.4027330Z >>> input = torch.arange(4) + rank * 4 2025-09-07T08:19:16.4027662Z >>> input = list(input.chunk(4)) 2025-09-07T08:19:16.4027942Z >>> input 2025-09-07T08:19:16.4028241Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-09-07T08:19:16.4028690Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-09-07T08:19:16.4029135Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-09-07T08:19:16.4029584Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-09-07T08:19:16.4030037Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T08:19:16.4030445Z >>> dist.all_to_all(output, input) 2025-09-07T08:19:16.4030745Z >>> output 2025-09-07T08:19:16.4031041Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-09-07T08:19:16.4031479Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-09-07T08:19:16.4031955Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-09-07T08:19:16.4032401Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-09-07T08:19:16.4032678Z 2025-09-07T08:19:16.4032851Z >>> # Essentially, it is similar to following operation: 2025-09-07T08:19:16.4033217Z >>> scatter_list = input 2025-09-07T08:19:16.4033496Z >>> gather_list = output 2025-09-07T08:19:16.4033784Z >>> for i in range(world_size): 2025-09-07T08:19:16.4034220Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-09-07T08:19:16.4034559Z 2025-09-07T08:19:16.4034653Z >>> input 2025-09-07T08:19:16.4034928Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T08:19:16.4035365Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T08:19:16.4035856Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T08:19:16.4036294Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T08:19:16.4036654Z >>> input_splits 2025-09-07T08:19:16.4036934Z [2, 2, 1, 1] # Rank 0 2025-09-07T08:19:16.4037302Z [3, 2, 2, 2] # Rank 1 2025-09-07T08:19:16.4037670Z [2, 1, 1, 1] # Rank 2 2025-09-07T08:19:16.4038024Z [2, 2, 2, 1] # Rank 3 2025-09-07T08:19:16.4038364Z >>> output_splits 2025-09-07T08:19:16.4038645Z [2, 3, 2, 2] # Rank 0 2025-09-07T08:19:16.4039013Z [2, 2, 1, 2] # Rank 1 2025-09-07T08:19:16.4039389Z [1, 2, 1, 2] # Rank 2 2025-09-07T08:19:16.4039748Z [1, 2, 1, 1] # Rank 3 2025-09-07T08:19:16.4040130Z >>> input = list(input.split(input_splits)) 2025-09-07T08:19:16.4040461Z >>> input 2025-09-07T08:19:16.4040799Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-09-07T08:19:16.4041315Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-09-07T08:19:16.4041845Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-09-07T08:19:16.4042384Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-09-07T08:19:16.4042808Z >>> output = ... 2025-09-07T08:19:16.4043079Z >>> dist.all_to_all(output, input) 2025-09-07T08:19:16.4043375Z >>> output 2025-09-07T08:19:16.4043746Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-09-07T08:19:16.4044354Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-09-07T08:19:16.4044885Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-09-07T08:19:16.4045400Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-09-07T08:19:16.4045736Z 2025-09-07T08:19:16.4045894Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T08:19:16.4046275Z >>> input = torch.tensor( 2025-09-07T08:19:16.4046607Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T08:19:16.4046953Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T08:19:16.4047231Z >>> input = list(input.chunk(4)) 2025-09-07T08:19:16.4047526Z >>> input 2025-09-07T08:19:16.4047873Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-09-07T08:19:16.4048489Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-09-07T08:19:16.4049355Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-09-07T08:19:16.4050261Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-09-07T08:19:16.4051234Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T08:19:16.4051662Z >>> dist.all_to_all(output, input) 2025-09-07T08:19:16.4051957Z >>> output 2025-09-07T08:19:16.4052306Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-09-07T08:19:16.4052853Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-09-07T08:19:16.4053399Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-09-07T08:19:16.4053942Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-09-07T08:19:16.4054277Z 2025-09-07T08:19:16.4054281Z 2025-09-07T08:19:16.4054533Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.4055002Z 2025-09-07T08:19:16.4055544Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-09-07T08:19:16.4056397Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.4056772Z 2025-09-07T08:19:16.4056888Z Module ``torch.distributed.launch``. 2025-09-07T08:19:16.4057114Z 2025-09-07T08:19:16.4057363Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-09-07T08:19:16.4057883Z training processes on each of the training nodes. 2025-09-07T08:19:16.4058141Z 2025-09-07T08:19:16.4058246Z .. warning:: 2025-09-07T08:19:16.4058372Z 2025-09-07T08:19:16.4058634Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-09-07T08:19:16.4059006Z 2025-09-07T08:19:16.4059243Z The utility can be used for single-node distributed training, in which one or 2025-09-07T08:19:16.4059841Z more processes per node will be spawned. The utility can be used for either 2025-09-07T08:19:16.4060422Z CPU training or GPU training. If the utility is used for GPU training, 2025-09-07T08:19:16.4061003Z each distributed process will be operating on a single GPU. This can achieve 2025-09-07T08:19:16.4061605Z well-improved single-node training performance. It can also be used in 2025-09-07T08:19:16.4062213Z multi-node distributed training, by spawning up multiple processes on each node 2025-09-07T08:19:16.4062833Z for well-improved multi-node distributed training performance as well. 2025-09-07T08:19:16.4063412Z This will especially be beneficial for systems with multiple Infiniband 2025-09-07T08:19:16.4064012Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-09-07T08:19:16.4064490Z aggregated communication bandwidth. 2025-09-07T08:19:16.4064765Z 2025-09-07T08:19:16.4064999Z In both cases of single-node distributed training or multi-node distributed 2025-09-07T08:19:16.4065596Z training, this utility will launch the given number of processes per node 2025-09-07T08:19:16.4066182Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-09-07T08:19:16.4066749Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-09-07T08:19:16.4067282Z and each process will be operating on a single GPU from *GPU 0 to 2025-09-07T08:19:16.4067709Z GPU (nproc_per_node - 1)*. 2025-09-07T08:19:16.4067898Z 2025-09-07T08:19:16.4067996Z **How to use this module:** 2025-09-07T08:19:16.4068170Z 2025-09-07T08:19:16.4068331Z 1. Single-Node multi-process distributed training 2025-09-07T08:19:16.4068589Z 2025-09-07T08:19:16.4068684Z :: 2025-09-07T08:19:16.4068789Z 2025-09-07T08:19:16.4069024Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T08:19:16.4069568Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-09-07T08:19:16.4070004Z arguments of your training script) 2025-09-07T08:19:16.4070234Z 2025-09-07T08:19:16.4070462Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-09-07T08:19:16.4070818Z 2025-09-07T08:19:16.4070822Z 2025-09-07T08:19:16.4070975Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-09-07T08:19:16.4071225Z 2025-09-07T08:19:16.4071306Z :: 2025-09-07T08:19:16.4071422Z 2025-09-07T08:19:16.4071653Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T08:19:16.4072165Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-09-07T08:19:16.4072649Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T08:19:16.4073118Z and all other arguments of your training script) 2025-09-07T08:19:16.4073554Z 2025-09-07T08:19:16.4073637Z Node 2: 2025-09-07T08:19:16.4073764Z 2025-09-07T08:19:16.4073844Z :: 2025-09-07T08:19:16.4073952Z 2025-09-07T08:19:16.4074200Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T08:19:16.4074814Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-09-07T08:19:16.4075286Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T08:19:16.4075765Z and all other arguments of your training script) 2025-09-07T08:19:16.4076039Z 2025-09-07T08:19:16.4076200Z 3. To look up what optional arguments this module offers: 2025-09-07T08:19:16.4076470Z 2025-09-07T08:19:16.4076567Z :: 2025-09-07T08:19:16.4076676Z 2025-09-07T08:19:16.4076821Z python -m torch.distributed.launch --help 2025-09-07T08:19:16.4077063Z 2025-09-07T08:19:16.4077067Z 2025-09-07T08:19:16.4077161Z **Important Notices:** 2025-09-07T08:19:16.4077333Z 2025-09-07T08:19:16.4077516Z 1. This utility and multi-process distributed (single-node or 2025-09-07T08:19:16.4078065Z multi-node) GPU training currently only achieves the best performance using 2025-09-07T08:19:16.4078684Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-09-07T08:19:16.4079144Z use for GPU training. 2025-09-07T08:19:16.4079314Z 2025-09-07T08:19:16.4079528Z 2. In your training program, you must parse the command-line argument: 2025-09-07T08:19:16.4080092Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-09-07T08:19:16.4080671Z If your training program uses GPUs, you should ensure that your code only 2025-09-07T08:19:16.4081221Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-09-07T08:19:16.4081527Z 2025-09-07T08:19:16.4081634Z Parsing the local_rank argument 2025-09-07T08:19:16.4081838Z 2025-09-07T08:19:16.4081922Z :: 2025-09-07T08:19:16.4082042Z 2025-09-07T08:19:16.4082137Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.4082404Z >>> import argparse 2025-09-07T08:19:16.4082682Z >>> parser = argparse.ArgumentParser() 2025-09-07T08:19:16.4083114Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-09-07T08:19:16.4083574Z >>> args = parser.parse_args() 2025-09-07T08:19:16.4083774Z 2025-09-07T08:19:16.4083913Z Set your device to local rank using either 2025-09-07T08:19:16.4084214Z 2025-09-07T08:19:16.4084312Z :: 2025-09-07T08:19:16.4084418Z 2025-09-07T08:19:16.4084621Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-09-07T08:19:16.4084955Z 2025-09-07T08:19:16.4085039Z or 2025-09-07T08:19:16.4085158Z 2025-09-07T08:19:16.4085240Z :: 2025-09-07T08:19:16.4085344Z 2025-09-07T08:19:16.4085489Z >>> with torch.cuda.device(args.local_rank): 2025-09-07T08:19:16.4085819Z >>> # your code to run 2025-09-07T08:19:16.4086093Z >>> ... 2025-09-07T08:19:16.4086238Z 2025-09-07T08:19:16.4086337Z .. versionchanged:: 2.0.0 2025-09-07T08:19:16.4086509Z 2025-09-07T08:19:16.4086764Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-09-07T08:19:16.4087375Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-09-07T08:19:16.4087864Z previously used underscored ``--local_rank``. 2025-09-07T08:19:16.4088131Z 2025-09-07T08:19:16.4088417Z For backward compatibility, it may be necessary for users to handle both 2025-09-07T08:19:16.4089049Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-09-07T08:19:16.4089655Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-09-07T08:19:16.4090239Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-09-07T08:19:16.4090829Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-09-07T08:19:16.4091329Z including ``"--local-rank"`` should be sufficient. 2025-09-07T08:19:16.4091597Z 2025-09-07T08:19:16.4091830Z 3. In your training program, you are supposed to call the following function 2025-09-07T08:19:16.4092419Z at the beginning to start the distributed backend. It is strongly recommended 2025-09-07T08:19:16.4093006Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-09-07T08:19:16.4093595Z but ``env://`` is the one that is officially supported by this module. 2025-09-07T08:19:16.4093911Z 2025-09-07T08:19:16.4093993Z :: 2025-09-07T08:19:16.4094100Z 2025-09-07T08:19:16.4094314Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-09-07T08:19:16.4094775Z >>> init_method='env://') 2025-09-07T08:19:16.4095014Z 2025-09-07T08:19:16.4095253Z 4. In your training program, you can either use regular distributed functions 2025-09-07T08:19:16.4095848Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-09-07T08:19:16.4096415Z training program uses GPUs for training and you would like to use 2025-09-07T08:19:16.4096931Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-09-07T08:19:16.4097336Z here is how to configure it. 2025-09-07T08:19:16.4097522Z 2025-09-07T08:19:16.4097600Z :: 2025-09-07T08:19:16.4097721Z 2025-09-07T08:19:16.4097912Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-09-07T08:19:16.4098362Z >>> device_ids=[args.local_rank], 2025-09-07T08:19:16.4098765Z >>> output_device=args.local_rank) 2025-09-07T08:19:16.4099026Z 2025-09-07T08:19:16.4099267Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-09-07T08:19:16.4099865Z that your code will be operating on. This is generally the local rank of the 2025-09-07T08:19:16.4100452Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-09-07T08:19:16.4101024Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-09-07T08:19:16.4101445Z utility 2025-09-07T08:19:16.4101558Z 2025-09-07T08:19:16.4101799Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-09-07T08:19:16.4102414Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-09-07T08:19:16.4102971Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-09-07T08:19:16.4103510Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-09-07T08:19:16.4103981Z will not pass ``--local-rank`` when you specify this flag. 2025-09-07T08:19:16.4104272Z 2025-09-07T08:19:16.4104358Z .. warning:: 2025-09-07T08:19:16.4104495Z 2025-09-07T08:19:16.4104698Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-09-07T08:19:16.4105204Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-09-07T08:19:16.4105626Z write to a networked filesystem. See 2025-09-07T08:19:16.4106059Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-09-07T08:19:16.4106556Z how things can go wrong if you don't do this correctly. 2025-09-07T08:19:16.4106840Z 2025-09-07T08:19:16.4106844Z 2025-09-07T08:19:16.4106850Z 2025-09-07T08:19:16.4106855Z 2025-09-07T08:19:16.4107105Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.4107485Z 2025-09-07T08:19:16.4751924Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2025-09-07T08:19:16.4753008Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.4753395Z 2025-09-07T08:19:16.4753648Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-09-07T08:19:16.4754163Z Needs to be called on all ranks in an SPMD fashion. 2025-09-07T08:19:16.4754417Z 2025-09-07T08:19:16.4754499Z Args: 2025-09-07T08:19:16.4754888Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-09-07T08:19:16.4755463Z of shards that represent the local shards on this rank. 2025-09-07T08:19:16.4755985Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-09-07T08:19:16.4756442Z shape of the overall sharded tensor. 2025-09-07T08:19:16.4756931Z 2025-09-07T08:19:16.4757022Z Keyword args: 2025-09-07T08:19:16.4757425Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:16.4757941Z the default process group will be used. 2025-09-07T08:19:16.4758353Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T08:19:16.4758836Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T08:19:16.4759364Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T08:19:16.4759776Z Default: ``False``. 2025-09-07T08:19:16.4759950Z 2025-09-07T08:19:16.4760045Z Returns: 2025-09-07T08:19:16.4760303Z A :class:`ShardedTensor` object handle on this rank 2025-09-07T08:19:16.4760577Z 2025-09-07T08:19:16.4760581Z 2025-09-07T08:19:16.4760665Z Examples: 2025-09-07T08:19:16.4761043Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-09-07T08:19:16.4761610Z each shard have a (5, 5) local tensor, we can do it like below: 2025-09-07T08:19:16.4761909Z 2025-09-07T08:19:16.4762008Z on rank 0: 2025-09-07T08:19:16.4762251Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T08:19:16.4762602Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T08:19:16.4762940Z >>> shard_offsets=[0, 0], 2025-09-07T08:19:16.4763233Z >>> shard_lengths=[5, 5], 2025-09-07T08:19:16.4763524Z >>> placement="rank:0/cuda:0" 2025-09-07T08:19:16.4763814Z >>> ) 2025-09-07T08:19:16.4764203Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T08:19:16.4764717Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T08:19:16.4765027Z 2025-09-07T08:19:16.4765111Z on rank 1: 2025-09-07T08:19:16.4765363Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T08:19:16.4765754Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T08:19:16.4766089Z >>> shard_offsets=[5, 0], 2025-09-07T08:19:16.4766381Z >>> shard_lengths=[5, 5], 2025-09-07T08:19:16.4766701Z >>> placement="rank:1/cuda:1" 2025-09-07T08:19:16.4766996Z >>> ) 2025-09-07T08:19:16.4767312Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T08:19:16.4767810Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T08:19:16.4768133Z 2025-09-07T08:19:16.4768382Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.4768768Z 2025-09-07T08:19:16.4880608Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=835. 2025-09-07T08:19:16.4881753Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.4882146Z 2025-09-07T08:19:16.4882415Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-09-07T08:19:16.4882916Z size and sharding spec on each rank. 2025-09-07T08:19:16.4883132Z 2025-09-07T08:19:16.4883324Z Args: 2025-09-07T08:19:16.4883659Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-09-07T08:19:16.4884325Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-09-07T08:19:16.4884884Z The specification describing how to shard the Tensor. 2025-09-07T08:19:16.4885343Z global_size (Sequence[int]): Size of the sharded tensor. 2025-09-07T08:19:16.4885872Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-09-07T08:19:16.4886339Z Default: None 2025-09-07T08:19:16.4886669Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T08:19:16.4887349Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T08:19:16.4887901Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T08:19:16.4888383Z Default: ``False``. 2025-09-07T08:19:16.4888611Z 2025-09-07T08:19:16.4888856Z Returns: 2025-09-07T08:19:16.4889228Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-09-07T08:19:16.4889712Z tensor stored in the current rank. 2025-09-07T08:19:16.4889932Z 2025-09-07T08:19:16.4890017Z Examples: 2025-09-07T08:19:16.4890241Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.4890549Z >>> # All tensors below are of torch.int64 type. 2025-09-07T08:19:16.4890915Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:16.4891306Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T08:19:16.4891804Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-09-07T08:19:16.4892216Z >>> local_tensor 2025-09-07T08:19:16.4892474Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-09-07T08:19:16.4892763Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-09-07T08:19:16.4893060Z >>> sharding_dim = 0 2025-09-07T08:19:16.4893357Z >>> sharding_spec = ChunkShardingSpec( 2025-09-07T08:19:16.4893685Z dim=sharding_dim, 2025-09-07T08:19:16.4893953Z placements=[ 2025-09-07T08:19:16.4894215Z "rank:0/cuda:0", 2025-09-07T08:19:16.4894496Z "rank:1/cuda:1", 2025-09-07T08:19:16.4894764Z ], 2025-09-07T08:19:16.4894967Z ) 2025-09-07T08:19:16.4895216Z >>> st = ShardedTensor._init_from_local_tensor( 2025-09-07T08:19:16.4895583Z ... local_tensor, sharding_spec, [2, 4] 2025-09-07T08:19:16.4895898Z ... ) 2025-09-07T08:19:16.4896090Z >>> st 2025-09-07T08:19:16.4896305Z ShardedTensor( 2025-09-07T08:19:16.4896560Z ShardedTensorMetadata( 2025-09-07T08:19:16.4896851Z shards_metadata=[ 2025-09-07T08:19:16.4897292Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-09-07T08:19:16.4897947Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-09-07T08:19:16.4898467Z ], 2025-09-07T08:19:16.4898715Z size=torch.Size([2, 4]) 2025-09-07T08:19:16.4898994Z ) 2025-09-07T08:19:16.4899213Z >>> st.local_tensor() 2025-09-07T08:19:16.4899485Z tensor([1, 2, 3, 4]) # Rank 0 2025-09-07T08:19:16.4899771Z tensor([3, 4, 5, 6]) # Rank 1 2025-09-07T08:19:16.4899953Z 2025-09-07T08:19:16.4900220Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-09-07T08:19:16.4900853Z rank validations, and we only validate the local shard on the current rank. 2025-09-07T08:19:16.4901429Z We fully rely on the user to ensure local tensor is sharded based on the 2025-09-07T08:19:16.4901855Z sharding spec. 2025-09-07T08:19:16.4902010Z 2025-09-07T08:19:16.4902272Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.4902638Z 2025-09-07T08:19:16.4903352Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1076. 2025-09-07T08:19:16.4904408Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.4904818Z 2025-09-07T08:19:16.4905069Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-09-07T08:19:16.4905533Z single local shard. 2025-09-07T08:19:16.4905690Z 2025-09-07T08:19:16.4905908Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-09-07T08:19:16.4906488Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-09-07T08:19:16.4906945Z we swap local shards directly. 2025-09-07T08:19:16.4907403Z For more generic cases, we merge different shards across different ranks and split 2025-09-07T08:19:16.4908037Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-09-07T08:19:16.4908406Z 2025-09-07T08:19:16.4908499Z Args: 2025-09-07T08:19:16.4908904Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-09-07T08:19:16.4909524Z specification describing how the tensor is sharded. 2025-09-07T08:19:16.4909813Z 2025-09-07T08:19:16.4909895Z Returns: 2025-09-07T08:19:16.4910218Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-09-07T08:19:16.4910532Z 2025-09-07T08:19:16.4910628Z Examples: 2025-09-07T08:19:16.4910839Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.4911130Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:16.4911534Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T08:19:16.4911948Z >>> tensor = torch.stack([tensor, tensor]) 2025-09-07T08:19:16.4912257Z >>> tensor 2025-09-07T08:19:16.4912509Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-09-07T08:19:16.4912854Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-09-07T08:19:16.4913191Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-09-07T08:19:16.4913531Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-09-07T08:19:16.4913865Z >>> sharding_dim = 0 2025-09-07T08:19:16.4914145Z >>> spec = ChunkShardingSpec( 2025-09-07T08:19:16.4914444Z dim=sharding_dim, 2025-09-07T08:19:16.4914710Z placements=[ 2025-09-07T08:19:16.4914971Z "rank:0/cuda:0", 2025-09-07T08:19:16.4915272Z "rank:1/cuda:1", 2025-09-07T08:19:16.4915551Z "rank:2/cuda:2", 2025-09-07T08:19:16.4915818Z "rank:3/cuda:3", 2025-09-07T08:19:16.4916091Z ], 2025-09-07T08:19:16.4916310Z ) 2025-09-07T08:19:16.4916537Z >>> current_offsets = [0] * 2 2025-09-07T08:19:16.4916830Z >>> current_offsets[0] = rank * 2 2025-09-07T08:19:16.4917156Z >>> shard_metadata = ShardMetadata( 2025-09-07T08:19:16.4917528Z shard_offsets=copy.deepcopy(current_offsets), 2025-09-07T08:19:16.4917904Z shard_sizes=tensor.size(), 2025-09-07T08:19:16.4918262Z placement=spec.placements[rank], 2025-09-07T08:19:16.4918582Z ) 2025-09-07T08:19:16.4918795Z >>> local_shards = [ 2025-09-07T08:19:16.4919061Z Shard( 2025-09-07T08:19:16.4919294Z tensor=tensor, 2025-09-07T08:19:16.4919586Z metadata=shard_metadata, 2025-09-07T08:19:16.4919887Z ) 2025-09-07T08:19:16.4920098Z ] 2025-09-07T08:19:16.4920436Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-09-07T08:19:16.4920876Z >>> sharding_dim = 1 2025-09-07T08:19:16.4921173Z >>> resharding_spec = ChunkShardingSpec( 2025-09-07T08:19:16.4921505Z dim=sharding_dim, 2025-09-07T08:19:16.4921770Z placements=[ 2025-09-07T08:19:16.4922028Z "rank:0/cuda:0", 2025-09-07T08:19:16.4922305Z "rank:1/cuda:1", 2025-09-07T08:19:16.4922581Z "rank:2/cuda:2", 2025-09-07T08:19:16.4922845Z "rank:3/cuda:3", 2025-09-07T08:19:16.4923111Z ], 2025-09-07T08:19:16.4923323Z ) 2025-09-07T08:19:16.4923549Z >>> st.reshard(resharding_spec) 2025-09-07T08:19:16.4923863Z >>> tensor = st.local_shards()[0].tensor 2025-09-07T08:19:16.4924294Z >>> tensor 2025-09-07T08:19:16.4924568Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-09-07T08:19:16.4924963Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-09-07T08:19:16.4925337Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-09-07T08:19:16.4925736Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-09-07T08:19:16.4926001Z 2025-09-07T08:19:16.4926251Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.4926619Z 2025-09-07T08:19:16.5094142Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2025-09-07T08:19:16.5095259Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.5095647Z 2025-09-07T08:19:16.5096022Z Representation of a sharding plan, describes how to shard a module 2025-09-07T08:19:16.5096619Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-09-07T08:19:16.5097300Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-09-07T08:19:16.5097953Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-09-07T08:19:16.5098317Z 2025-09-07T08:19:16.5098411Z Args: 2025-09-07T08:19:16.5098799Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-09-07T08:19:16.5099347Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-09-07T08:19:16.5099904Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-09-07T08:19:16.5100553Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-09-07T08:19:16.5101057Z a parameter to a `ShardingSpec`. 2025-09-07T08:19:16.5101541Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-09-07T08:19:16.5102027Z to a `Sharder` object. 2025-09-07T08:19:16.5102568Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-09-07T08:19:16.5103280Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-09-07T08:19:16.5103895Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-09-07T08:19:16.5104340Z Default: `None` 2025-09-07T08:19:16.5104753Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-09-07T08:19:16.5105365Z a module's sharded output to be returned as a Tensor from its local shards to 2025-09-07T08:19:16.5105971Z ensure further processing in a data parallel fashion. ("" in list means the 2025-09-07T08:19:16.5106459Z root module). 2025-09-07T08:19:16.5106705Z Default: None 2025-09-07T08:19:16.5106947Z Example: 2025-09-07T08:19:16.5107356Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-09-07T08:19:16.5108119Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-09-07T08:19:16.5108906Z 2025-09-07T08:19:16.5109103Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-09-07T08:19:16.5109499Z >>> class MyModule(nn.Module): 2025-09-07T08:19:16.5109821Z >>> def __init__(self) -> None: 2025-09-07T08:19:16.5110136Z >>> super().__init__() 2025-09-07T08:19:16.5110422Z >>> self.fc1 = nn.Linear() 2025-09-07T08:19:16.5110724Z >>> self.gelu = nn.GELU() 2025-09-07T08:19:16.5111029Z >>> self.fc2 = nn.Linear() 2025-09-07T08:19:16.5111337Z >>> self.relu = nn.Linear() 2025-09-07T08:19:16.5111616Z >>> 2025-09-07T08:19:16.5111837Z >>> def forward(self, input): 2025-09-07T08:19:16.5112217Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-09-07T08:19:16.5112559Z 2025-09-07T08:19:16.5112563Z 2025-09-07T08:19:16.5112706Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-09-07T08:19:16.5113060Z >>> sharding_plan = ShardingPlan( 2025-09-07T08:19:16.5113348Z >>> plan={ 2025-09-07T08:19:16.5113592Z >>> "fc1.weight": spec1, 2025-09-07T08:19:16.5113893Z >>> "fc2.weight": spec2 2025-09-07T08:19:16.5114179Z >>> }, 2025-09-07T08:19:16.5114397Z >>> output_plan={ 2025-09-07T08:19:16.5114665Z >>> "fc2": output_spec 2025-09-07T08:19:16.5114943Z >>> }, 2025-09-07T08:19:16.5115181Z >>> return_local_tensor=["fc2"] 2025-09-07T08:19:16.5115462Z >>> ) 2025-09-07T08:19:16.5115588Z 2025-09-07T08:19:16.5115840Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.5116210Z 2025-09-07T08:19:16.6068274Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2025-09-07T08:19:16.6069432Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6069815Z 2025-09-07T08:19:16.6069940Z Run post-localSGD algorithm. 2025-09-07T08:19:16.6070130Z 2025-09-07T08:19:16.6070364Z This DDP communication hook is used for running post-localSGD algorithm, 2025-09-07T08:19:16.6070884Z by combining with a model averaging component (e.g., 2025-09-07T08:19:16.6071490Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-09-07T08:19:16.6072058Z that runs after the optimizer step. 2025-09-07T08:19:16.6072268Z 2025-09-07T08:19:16.6072348Z Args: 2025-09-07T08:19:16.6072698Z state (PostLocalSGDState): State information to run post-localSGD. 2025-09-07T08:19:16.6073470Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-09-07T08:19:16.6074302Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T08:19:16.6075101Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T08:19:16.6075613Z only exactly one tensor is stored in this bucket. 2025-09-07T08:19:16.6075886Z 2025-09-07T08:19:16.6075973Z Returns: 2025-09-07T08:19:16.6076344Z Future handler of the communication, which updates the gradients in place. 2025-09-07T08:19:16.6076702Z 2025-09-07T08:19:16.6076824Z Example:: 2025-09-07T08:19:16.6077038Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.6077468Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-09-07T08:19:16.6077968Z start_localSGD_iter=10) 2025-09-07T08:19:16.6078379Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T08:19:16.6079059Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-09-07T08:19:16.6079866Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-09-07T08:19:16.6080353Z 2025-09-07T08:19:16.6080606Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6080991Z 2025-09-07T08:19:16.6109595Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2025-09-07T08:19:16.6110684Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6111061Z 2025-09-07T08:19:16.6111182Z Implement PowerSGD algorithm. 2025-09-07T08:19:16.6111374Z 2025-09-07T08:19:16.6111611Z This DDP communication hook implements PowerSGD gradient compression 2025-09-07T08:19:16.6112206Z algorithm described in the `paper `_. 2025-09-07T08:19:16.6112809Z Once gradient tensors are aggregated across all workers, this hook applies 2025-09-07T08:19:16.6113363Z compression as follows: 2025-09-07T08:19:16.6113529Z 2025-09-07T08:19:16.6113979Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-09-07T08:19:16.6114534Z 2025-09-07T08:19:16.6114970Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-09-07T08:19:16.6115505Z 2025-09-07T08:19:16.6115911Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-09-07T08:19:16.6116451Z 2025-09-07T08:19:16.6116561Z 2. Handles uncompressed tensors: 2025-09-07T08:19:16.6116772Z 2025-09-07T08:19:16.6117287Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-09-07T08:19:16.6117927Z 2025-09-07T08:19:16.6118368Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-09-07T08:19:16.6118858Z 2025-09-07T08:19:16.6119092Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-09-07T08:19:16.6119440Z 2025-09-07T08:19:16.6119692Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-09-07T08:19:16.6120372Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-09-07T08:19:16.6120795Z 2025-09-07T08:19:16.6120939Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-09-07T08:19:16.6121206Z 2025-09-07T08:19:16.6121311Z 3.3. Allreduces Ps as a batch; 2025-09-07T08:19:16.6121524Z 2025-09-07T08:19:16.6121638Z 3.4. Orthogonalizes each P in Ps; 2025-09-07T08:19:16.6121851Z 2025-09-07T08:19:16.6122058Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-09-07T08:19:16.6122366Z 2025-09-07T08:19:16.6122481Z 3.6. Allreduces Qs as a batch; 2025-09-07T08:19:16.6122680Z 2025-09-07T08:19:16.6122980Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-09-07T08:19:16.6123401Z 2025-09-07T08:19:16.6123799Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-09-07T08:19:16.6124685Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-09-07T08:19:16.6125546Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-09-07T08:19:16.6126103Z 2025-09-07T08:19:16.6126199Z Args: 2025-09-07T08:19:16.6126756Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-09-07T08:19:16.6127686Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-09-07T08:19:16.6128324Z and ``min_compression_rate``. 2025-09-07T08:19:16.6128971Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T08:19:16.6129770Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T08:19:16.6130285Z only exactly one tensor is stored in this bucket. 2025-09-07T08:19:16.6130559Z 2025-09-07T08:19:16.6130645Z Returns: 2025-09-07T08:19:16.6131015Z Future handler of the communication, which updates the gradients in place. 2025-09-07T08:19:16.6131375Z 2025-09-07T08:19:16.6131483Z Example:: 2025-09-07T08:19:16.6131709Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.6132142Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-09-07T08:19:16.6132704Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-09-07T08:19:16.6133150Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-09-07T08:19:16.6133419Z 2025-09-07T08:19:16.6133687Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6134081Z 2025-09-07T08:19:16.6158250Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38. 2025-09-07T08:19:16.6159356Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6159747Z 2025-09-07T08:19:16.6159934Z Averages parameters periodically after the warm-up stage. 2025-09-07T08:19:16.6160246Z 2025-09-07T08:19:16.6160503Z This can be used for running `post-local SGD `_, 2025-09-07T08:19:16.6161077Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-09-07T08:19:16.6161620Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-09-07T08:19:16.6161974Z 2025-09-07T08:19:16.6162085Z Args: 2025-09-07T08:19:16.6162354Z period (int): The number of steps per model averaging. 2025-09-07T08:19:16.6162981Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-09-07T08:19:16.6163509Z Otherwise, only DDP needs to be used. 2025-09-07T08:19:16.6163961Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-09-07T08:19:16.6164476Z model averaging is skipped. 2025-09-07T08:19:16.6164900Z process_group: The process group to be used for all-reduce. 2025-09-07T08:19:16.6165351Z If ``None``, the default process group, which 2025-09-07T08:19:16.6165802Z is created by :func:`torch.distributed.init_process_group`, 2025-09-07T08:19:16.6166238Z will be used. (default: ``None``) 2025-09-07T08:19:16.6166468Z 2025-09-07T08:19:16.6166562Z Example:: 2025-09-07T08:19:16.6166700Z 2025-09-07T08:19:16.6166825Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:16.6167151Z >>> import torch 2025-09-07T08:19:16.6167428Z >>> import torch.distributed as dist 2025-09-07T08:19:16.6167963Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-09-07T08:19:16.6168675Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T08:19:16.6169174Z >>> import torch.nn as nn 2025-09-07T08:19:16.6169449Z >>> 2025-09-07T08:19:16.6169733Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T08:19:16.6170135Z >>> torch.cuda.set_device(rank) 2025-09-07T08:19:16.6170474Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-09-07T08:19:16.6170874Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T08:19:16.6171267Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T08:19:16.6171608Z >>> ) 2025-09-07T08:19:16.6171882Z >>> # Register a post-localSGD communication hook. 2025-09-07T08:19:16.6172479Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T08:19:16.6173071Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T08:19:16.6173579Z >>> 2025-09-07T08:19:16.6173974Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T08:19:16.6174526Z >>> # After 100 steps, run model averaging every 4 steps. 2025-09-07T08:19:16.6175119Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T08:19:16.6175798Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T08:19:16.6176277Z >>> for step in range(0, 200): 2025-09-07T08:19:16.6176587Z >>> optimizer.zero_grad() 2025-09-07T08:19:16.6176896Z >>> loss = loss_fn(output, labels) 2025-09-07T08:19:16.6177205Z >>> loss.backward() 2025-09-07T08:19:16.6177487Z >>> optimizer.step() 2025-09-07T08:19:16.6177863Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-09-07T08:19:16.6178386Z >>> # inter-node communication only occurs every 4 iterations after 2025-09-07T08:19:16.6178892Z >>> # the initial ``warmup_steps`` period. 2025-09-07T08:19:16.6179451Z >>> averager.average_parameters(model.parameters()) 2025-09-07T08:19:16.6179882Z 2025-09-07T08:19:16.6180155Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6180737Z 2025-09-07T08:19:16.6182002Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19. 2025-09-07T08:19:16.6183235Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6183611Z 2025-09-07T08:19:16.6183963Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-09-07T08:19:16.6184421Z 2025-09-07T08:19:16.6184852Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-09-07T08:19:16.6185478Z by using different periods concurrently after the warm-up stage. 2025-09-07T08:19:16.6186205Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-09-07T08:19:16.6187073Z that supports `post-local SGD `_, which essentially only supports 2025-09-07T08:19:16.6187838Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-09-07T08:19:16.6188621Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-09-07T08:19:16.6189378Z Similarly, the process groups within this class do not have such an intra-machine process 2025-09-07T08:19:16.6190069Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-09-07T08:19:16.6190476Z 2025-09-07T08:19:16.6190557Z Args: 2025-09-07T08:19:16.6190945Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-09-07T08:19:16.6191536Z process group size, used for initializing process groups of 2025-09-07T08:19:16.6192077Z different sizes in a hierarchy to average parameters concurrently. 2025-09-07T08:19:16.6192643Z Particularly, at each iteration, there will be at most a single 2025-09-07T08:19:16.6193216Z process group that runs averaging -- the period of such group should 2025-09-07T08:19:16.6193786Z have the largest period which the current step can be divided by. 2025-09-07T08:19:16.6194295Z For example, if the dict has three keys: 2, 4, and 8, 2025-09-07T08:19:16.6194777Z then this means totally three process groups will be created to 2025-09-07T08:19:16.6195363Z average parameters every 2, 4, and 8 iterations, respectively. 2025-09-07T08:19:16.6195885Z At the 4th iteration, only the second process group will run 2025-09-07T08:19:16.6196362Z averaging, because the first process group should be a 2025-09-07T08:19:16.6196886Z subset of the second process group, and no need to execute the first 2025-09-07T08:19:16.6197353Z process group redundantly. 2025-09-07T08:19:16.6197791Z On the other hand, the third process group can only be triggered 2025-09-07T08:19:16.6198339Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-09-07T08:19:16.6198975Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-09-07T08:19:16.6199850Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-09-07T08:19:16.6200597Z If ``None``, the default process group, which is created 2025-09-07T08:19:16.6201159Z by :func:`torch.distributed.init_process_group`, will be used. 2025-09-07T08:19:16.6201615Z (default: ``None``) 2025-09-07T08:19:16.6201849Z 2025-09-07T08:19:16.6201943Z Example:: 2025-09-07T08:19:16.6202194Z >>> # xdoctest: +SKIP('undefined rank') 2025-09-07T08:19:16.6202543Z >>> from collections import OrderedDict 2025-09-07T08:19:16.6202861Z >>> import torch 2025-09-07T08:19:16.6203117Z >>> import torch.distributed as dist 2025-09-07T08:19:16.6203612Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T08:19:16.6204199Z >>> PostLocalSGDState, 2025-09-07T08:19:16.6204497Z >>> post_localSGD_hook, 2025-09-07T08:19:16.6204759Z >>> ) 2025-09-07T08:19:16.6205319Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-09-07T08:19:16.6205920Z >>> import torch.nn as nn 2025-09-07T08:19:16.6206189Z >>> 2025-09-07T08:19:16.6206472Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T08:19:16.6206880Z >>> torch.cuda.set_device(rank) 2025-09-07T08:19:16.6207221Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T08:19:16.6207619Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T08:19:16.6207998Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T08:19:16.6208323Z >>> ) 2025-09-07T08:19:16.6208593Z >>> # Register a post-localSGD communication hook. 2025-09-07T08:19:16.6209124Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-09-07T08:19:16.6209631Z >>> subgroup, _ = dist.new_subgroups() 2025-09-07T08:19:16.6210175Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-09-07T08:19:16.6210773Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T08:19:16.6211135Z >>> 2025-09-07T08:19:16.6211536Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-09-07T08:19:16.6212050Z >>> # the 16 processes every 16 iterations. 2025-09-07T08:19:16.6212471Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-09-07T08:19:16.6212999Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-09-07T08:19:16.6213673Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T08:19:16.6214366Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T08:19:16.6214914Z >>> # After 100 steps, run model averaging at two levels. 2025-09-07T08:19:16.6215321Z >>> for step in range(0, 200): 2025-09-07T08:19:16.6215628Z >>> optimizer.zero_grad() 2025-09-07T08:19:16.6215934Z >>> loss = loss_fn(output, labels) 2025-09-07T08:19:16.6216246Z >>> loss.backward() 2025-09-07T08:19:16.6216519Z >>> optimizer.step() 2025-09-07T08:19:16.6216848Z >>> # Average parameters after ``optimizer.step()``. 2025-09-07T08:19:16.6217397Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-09-07T08:19:16.6217949Z >>> averager.average_parameters(model.parameters()) 2025-09-07T08:19:16.6218226Z 2025-09-07T08:19:16.6218312Z .. warning :: 2025-09-07T08:19:16.6218693Z The last group size in the dict must be the size of the provided ``process_group``, 2025-09-07T08:19:16.6219296Z which indicates model averaging at the highest level of the hierarchy. 2025-09-07T08:19:16.6219934Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-09-07T08:19:16.6220366Z 2025-09-07T08:19:16.6220451Z .. warning :: 2025-09-07T08:19:16.6220819Z `HierarchicalModelAverager` is experimental and subject to change. 2025-09-07T08:19:16.6221168Z 2025-09-07T08:19:16.6221479Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6221842Z 2025-09-07T08:19:16.6691891Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2025-09-07T08:19:16.6693005Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6693387Z 2025-09-07T08:19:16.6693676Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-09-07T08:19:16.6694480Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-09-07T08:19:16.6694858Z 2025-09-07T08:19:16.6695021Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-09-07T08:19:16.6695456Z 2025-09-07T08:19:16.6695655Z .. warning:: 2025-09-07T08:19:16.6696104Z Current implementation only supports loading Tensors. 2025-09-07T08:19:16.6696551Z 2025-09-07T08:19:16.6696666Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:16.6696983Z >>> sd = {"mode": model} 2025-09-07T08:19:16.6697238Z >>> dcp.load( 2025-09-07T08:19:16.6697455Z >>> sd, 2025-09-07T08:19:16.6697717Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T08:19:16.6698104Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T08:19:16.6698449Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T08:19:16.6698750Z >>> ) 2025-09-07T08:19:16.6698863Z 2025-09-07T08:19:16.6699115Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6699499Z 2025-09-07T08:19:16.6700204Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2025-09-07T08:19:16.6701332Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6701725Z 2025-09-07T08:19:16.6702098Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-09-07T08:19:16.6702927Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-09-07T08:19:16.6703495Z metadata file, like Torch Save files. 2025-09-07T08:19:16.6703708Z 2025-09-07T08:19:16.6703886Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-09-07T08:19:16.6704187Z 2025-09-07T08:19:16.6704274Z .. warning:: 2025-09-07T08:19:16.6704579Z Current implementation only supports loading Tensors. 2025-09-07T08:19:16.6704912Z 2025-09-07T08:19:16.6705097Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:16.6705465Z >>> sd = {"mode": model} 2025-09-07T08:19:16.6705707Z >>> dcp.load( 2025-09-07T08:19:16.6705927Z >>> sd, 2025-09-07T08:19:16.6706206Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T08:19:16.6706647Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T08:19:16.6707004Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T08:19:16.6707319Z >>> ) 2025-09-07T08:19:16.6707434Z 2025-09-07T08:19:16.6707700Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6708067Z 2025-09-07T08:19:16.6805848Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=221. 2025-09-07T08:19:16.6806919Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6807298Z 2025-09-07T08:19:16.6807506Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-09-07T08:19:16.6807838Z 2025-09-07T08:19:16.6808001Z This is the current recommended way to checkpoint FSDP. 2025-09-07T08:19:16.6808373Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.6808688Z >>> import torch.distributed.checkpoint as dist_cp 2025-09-07T08:19:16.6809032Z >>> # Save 2025-09-07T08:19:16.6809257Z >>> model: torch.nn.Model 2025-09-07T08:19:16.6809555Z >>> optim_params = model.parameters() 2025-09-07T08:19:16.6810003Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-09-07T08:19:16.6810334Z >>> # Save 2025-09-07T08:19:16.6810677Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-09-07T08:19:16.6811107Z >>> state_dict = { 2025-09-07T08:19:16.6811424Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-09-07T08:19:16.6811794Z >>> "model": model.state_dict() 2025-09-07T08:19:16.6812095Z >>> } 2025-09-07T08:19:16.6812320Z >>> dist_cp.save_state_dict( 2025-09-07T08:19:16.6812615Z >>> state_dict=optim_state, 2025-09-07T08:19:16.6812987Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-09-07T08:19:16.6813413Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-09-07T08:19:16.6813741Z >>> ) 2025-09-07T08:19:16.6813944Z >>> 2025-09-07T08:19:16.6814127Z >>> # Load 2025-09-07T08:19:16.6814474Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-09-07T08:19:16.6815032Z >>> model_state_dict = model_tp.state_dict() 2025-09-07T08:19:16.6815384Z >>> checkpoint = { 2025-09-07T08:19:16.6815655Z >>> "model": model_state_dict 2025-09-07T08:19:16.6815936Z >>> } 2025-09-07T08:19:16.6816164Z >>> dist_cp.load_state_dict( 2025-09-07T08:19:16.6816466Z >>> state_dict=checkpoint, 2025-09-07T08:19:16.6816859Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-09-07T08:19:16.6817289Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-09-07T08:19:16.6817615Z >>> ) 2025-09-07T08:19:16.6817883Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-09-07T08:19:16.6818229Z >>> 2025-09-07T08:19:16.6818502Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-09-07T08:19:16.6818888Z >>> model_state_dict, 2025-09-07T08:19:16.6819183Z >>> optimizer_key="optimizer", 2025-09-07T08:19:16.6819576Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-09-07T08:19:16.6819942Z >>> ) 2025-09-07T08:19:16.6820145Z >>> 2025-09-07T08:19:16.6820404Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-09-07T08:19:16.6820790Z >>> model, optim, optim_state["optimizer"] 2025-09-07T08:19:16.6821102Z >>> ) 2025-09-07T08:19:16.6821305Z >>> 2025-09-07T08:19:16.6821539Z >>> optim.load_state_dict(flattened_osd) 2025-09-07T08:19:16.6821764Z 2025-09-07T08:19:16.6822022Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6822388Z 2025-09-07T08:19:16.6838347Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-09-07T08:19:16.6839371Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6839764Z 2025-09-07T08:19:16.6840148Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-09-07T08:19:16.6840571Z 2025-09-07T08:19:16.6840869Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-09-07T08:19:16.6841302Z 2025-09-07T08:19:16.6841579Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T08:19:16.6842092Z will be visible to the whole process. 2025-09-07T08:19:16.6842303Z 2025-09-07T08:19:16.6842577Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-09-07T08:19:16.6842982Z 2025-09-07T08:19:16.6843101Z 1) set_up_planner - called on all ranks. 2025-09-07T08:19:16.6843450Z Signals the start of a checkpoint save. 2025-09-07T08:19:16.6843677Z 2025-09-07T08:19:16.6843810Z 2) create_local_plan - called on all ranks. 2025-09-07T08:19:16.6844545Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-09-07T08:19:16.6844955Z 2025-09-07T08:19:16.6845199Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T08:19:16.6845770Z Takes the SavePlan from all ranks and make any global decision. 2025-09-07T08:19:16.6846222Z 2025-09-07T08:19:16.6846383Z 4) finish_plan - called on all ranks. 2025-09-07T08:19:16.6846871Z This gives each rank a chance to adjust to global planning decisions. 2025-09-07T08:19:16.6847216Z 2025-09-07T08:19:16.6847367Z 5) resolve_data - called multiple times on each rank 2025-09-07T08:19:16.6847905Z Lookups a value on the `state_dict` for the storage layer to write. 2025-09-07T08:19:16.6848235Z 2025-09-07T08:19:16.6848601Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-09-07T08:19:16.6849271Z most changes can be expressed by changes in a single method. 2025-09-07T08:19:16.6849563Z 2025-09-07T08:19:16.6849688Z There are 3 usual patterns of extension: 2025-09-07T08:19:16.6849985Z 2025-09-07T08:19:16.6850235Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-09-07T08:19:16.6850901Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-09-07T08:19:16.6851313Z 2025-09-07T08:19:16.6851504Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:16.6851849Z >>> class RenamePlanner(DefaultSavePlanner): 2025-09-07T08:19:16.6852243Z >>> def set_up_planner( 2025-09-07T08:19:16.6852525Z >>> self, 2025-09-07T08:19:16.6852776Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T08:19:16.6853169Z >>> storage_meta: Optional[StorageMeta], 2025-09-07T08:19:16.6853512Z >>> is_coordinator: bool, 2025-09-07T08:19:16.6853863Z >>> ) -> None: 2025-09-07T08:19:16.6854117Z >>> # prefix all keys with `foo_`` 2025-09-07T08:19:16.6854673Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-09-07T08:19:16.6855077Z 2025-09-07T08:19:16.6855474Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-09-07T08:19:16.6855935Z 2025-09-07T08:19:16.6856052Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:16.6856466Z >>> class FP16Planner(DefaultSavePlanner): 2025-09-07T08:19:16.6856808Z >>> def create_local_plan(self): 2025-09-07T08:19:16.6857191Z >>> plan = super().create_local_plan() 2025-09-07T08:19:16.6857519Z >>> for p in plan: 2025-09-07T08:19:16.6857674Z >>> if p.tensor_data is not None: 2025-09-07T08:19:16.6857864Z >>> p.tensor_data.properties.dtype = torch.float16 2025-09-07T08:19:16.6857958Z >>> return plan 2025-09-07T08:19:16.6858051Z >>> 2025-09-07T08:19:16.6858164Z >>> def resolve_data(self, write_item): 2025-09-07T08:19:16.6858302Z >>> item = super().resolve_data(write_item) 2025-09-07T08:19:16.6858642Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-09-07T08:19:16.6858647Z 2025-09-07T08:19:16.6858985Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-09-07T08:19:16.6859027Z 2025-09-07T08:19:16.6859155Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:16.6859330Z >>> from itertools import zip_longest 2025-09-07T08:19:16.6859452Z >>> from dataclasses import replace 2025-09-07T08:19:16.6859618Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-09-07T08:19:16.6859906Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-09-07T08:19:16.6860095Z >>> # This sample doesn't handle ShardedTensors 2025-09-07T08:19:16.6860222Z >>> def create_global_plan(self, all_plans): 2025-09-07T08:19:16.6860381Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-09-07T08:19:16.6860483Z >>> items_per_rank = [ 2025-09-07T08:19:16.6860631Z >>> [item for item in items if item is not None] 2025-09-07T08:19:16.6860819Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-09-07T08:19:16.6860929Z >>> ] 2025-09-07T08:19:16.6861038Z >>> all_plans = [ 2025-09-07T08:19:16.6861148Z >>> replace(plan, items=items) 2025-09-07T08:19:16.6861341Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-09-07T08:19:16.6861468Z >>> ] 2025-09-07T08:19:16.6861666Z >>> return super().create_global_plan(all_plans) 2025-09-07T08:19:16.6861671Z 2025-09-07T08:19:16.6861950Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-09-07T08:19:16.6862222Z accomplished by having each rank contribute their data items in the local plan and 2025-09-07T08:19:16.6862373Z the global planner aggregate them: 2025-09-07T08:19:16.6862380Z 2025-09-07T08:19:16.6862520Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:16.6862673Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-09-07T08:19:16.6862810Z >>> def create_local_plan(self) -> SavePlan: 2025-09-07T08:19:16.6862925Z >>> plan = super().create_local_plan() 2025-09-07T08:19:16.6863102Z >>> return replace(plan, planner_data="per-rank-data") 2025-09-07T08:19:16.6863238Z >>> 2025-09-07T08:19:16.6863594Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-09-07T08:19:16.6863801Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-09-07T08:19:16.6864016Z >>> merged_data = [p.planner_data for p in global_plan] 2025-09-07T08:19:16.6864197Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-09-07T08:19:16.6864304Z >>> return global_plan, metadata 2025-09-07T08:19:16.6864309Z 2025-09-07T08:19:16.6864558Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6864563Z 2025-09-07T08:19:16.6865243Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-09-07T08:19:16.6865555Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.6865566Z 2025-09-07T08:19:16.6865876Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-09-07T08:19:16.6865882Z 2025-09-07T08:19:16.6866165Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-09-07T08:19:16.6866170Z 2025-09-07T08:19:16.6866525Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T08:19:16.6866640Z will be visible to the whole process. 2025-09-07T08:19:16.6866644Z 2025-09-07T08:19:16.6866931Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-09-07T08:19:16.6866935Z 2025-09-07T08:19:16.6867082Z 1) set_up_planner - called on all ranks. 2025-09-07T08:19:16.6867243Z Signals the start of loading a checkpoint. 2025-09-07T08:19:16.6867248Z 2025-09-07T08:19:16.6867380Z 2) create_local_plan - called on all ranks. 2025-09-07T08:19:16.6867721Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-09-07T08:19:16.6867725Z 2025-09-07T08:19:16.6867981Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T08:19:16.6868178Z Takes the LoadPlan from all ranks and make any global decision. 2025-09-07T08:19:16.6868183Z 2025-09-07T08:19:16.6868341Z 4) load_bytes - called multiple times on each rank 2025-09-07T08:19:16.6868507Z This is called once per non-tensor value in state_dict. 2025-09-07T08:19:16.6868511Z 2025-09-07T08:19:16.6868802Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-09-07T08:19:16.6868999Z They are called in pair for each Tensor value in state_dict. 2025-09-07T08:19:16.6869003Z 2025-09-07T08:19:16.6869309Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-09-07T08:19:16.6869563Z most changes can be expressed by changes in a single method. 2025-09-07T08:19:16.6869567Z 2025-09-07T08:19:16.6869698Z There are two usual patterns of extension: 2025-09-07T08:19:16.6869703Z 2025-09-07T08:19:16.6869970Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-09-07T08:19:16.6870320Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-09-07T08:19:16.6870547Z to keep a reference to the original state_dict as load happens in place so 2025-09-07T08:19:16.6870677Z we need to be able to perform it in place 2025-09-07T08:19:16.6870682Z 2025-09-07T08:19:16.6870791Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:16.6870939Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-09-07T08:19:16.6871089Z >>> def set_up_planner( 2025-09-07T08:19:16.6871172Z >>> self, 2025-09-07T08:19:16.6871291Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T08:19:16.6871387Z >>> metadata: Metadata, 2025-09-07T08:19:16.6871499Z >>> is_coordinator: bool, 2025-09-07T08:19:16.6871583Z >>> ) -> None: 2025-09-07T08:19:16.6871718Z >>> self.original_state_dict = state_dict 2025-09-07T08:19:16.6871957Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-09-07T08:19:16.6872093Z >>> 2025-09-07T08:19:16.6872211Z >>> if self.flatten_sharded_tensors: 2025-09-07T08:19:16.6872374Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-09-07T08:19:16.6872454Z >>> 2025-09-07T08:19:16.6872632Z >>> if self.flatten_state_dict: 2025-09-07T08:19:16.6872813Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-09-07T08:19:16.6872891Z >>> 2025-09-07T08:19:16.6873007Z >>> self.state_dict = state_dict 2025-09-07T08:19:16.6873107Z >>> self.metadata = metadata 2025-09-07T08:19:16.6873237Z >>> self.is_coordinator = is_coordinator 2025-09-07T08:19:16.6873545Z >>> 2025-09-07T08:19:16.6873667Z >>> def load_bytes(self, read_item, value): 2025-09-07T08:19:16.6873779Z >>> # Remove the "foo_" prefix 2025-09-07T08:19:16.6874124Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-09-07T08:19:16.6874136Z 2025-09-07T08:19:16.6874143Z 2025-09-07T08:19:16.6874446Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-09-07T08:19:16.6874454Z 2025-09-07T08:19:16.6874563Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:16.6874727Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-09-07T08:19:16.6874846Z >>> def resolve_tensor(self, read_item): 2025-09-07T08:19:16.6875033Z >>> tensor = super().resolve_tensor(read_item) 2025-09-07T08:19:16.6875188Z >>> return torch.empty_like(tensor, device="cpu") 2025-09-07T08:19:16.6875267Z >>> 2025-09-07T08:19:16.6875392Z >>> def commit_tensor(self, read_item, tensor): 2025-09-07T08:19:16.6875556Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-09-07T08:19:16.6875561Z 2025-09-07T08:19:16.6875880Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.6875953Z 2025-09-07T08:19:16.7072729Z msg = Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-09-07T08:19:16.7073063Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.7073068Z 2025-09-07T08:19:16.7073242Z Return the model state_dict and optimizers state_dict. 2025-09-07T08:19:16.7073365Z 2025-09-07T08:19:16.7073588Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-09-07T08:19:16.7073922Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-09-07T08:19:16.7074171Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-09-07T08:19:16.7074388Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-09-07T08:19:16.7074667Z with a different number of trainers and/or different parallelisms. 2025-09-07T08:19:16.7074917Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-09-07T08:19:16.7075036Z these APIs. 2025-09-07T08:19:16.7075168Z 3.) sanity checking the result state_dict. 2025-09-07T08:19:16.7075298Z 2025-09-07T08:19:16.7075523Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-09-07T08:19:16.7075758Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-09-07T08:19:16.7076030Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-09-07T08:19:16.7076278Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-09-07T08:19:16.7076487Z ``module.named_buffers()`` when the module is not distributed by any 2025-09-07T08:19:16.7076769Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-09-07T08:19:16.7077032Z a parameter, there will be a conversion from the parameter IDs to the 2025-09-07T08:19:16.7077148Z canonical FQNs when calling this API. 2025-09-07T08:19:16.7077152Z 2025-09-07T08:19:16.7077387Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-09-07T08:19:16.7077761Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-09-07T08:19:16.7077922Z optimizer parameter IDs to the canonical FQNs. 2025-09-07T08:19:16.7077927Z 2025-09-07T08:19:16.7078012Z Example: 2025-09-07T08:19:16.7078111Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.7078216Z >>> import torch 2025-09-07T08:19:16.7078516Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T08:19:16.7078731Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T08:19:16.7078954Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-09-07T08:19:16.7078958Z 2025-09-07T08:19:16.7079089Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-09-07T08:19:16.7079341Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T08:19:16.7079464Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-09-07T08:19:16.7079661Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T08:19:16.7079665Z 2025-09-07T08:19:16.7079669Z 2025-09-07T08:19:16.7079932Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-09-07T08:19:16.7080148Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-09-07T08:19:16.7080254Z ... fsdp_model, fsdp_optim 2025-09-07T08:19:16.7080337Z ... ) 2025-09-07T08:19:16.7080342Z 2025-09-07T08:19:16.7080571Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-09-07T08:19:16.7080694Z >>> # the asserts will fail. 2025-09-07T08:19:16.7080873Z >>> assert ddp_state_dict == fsdp_state_dict 2025-09-07T08:19:16.7081019Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-09-07T08:19:16.7081024Z 2025-09-07T08:19:16.7081028Z 2025-09-07T08:19:16.7081110Z Args: 2025-09-07T08:19:16.7081260Z model (nn.Module): the nn.Module to the model. 2025-09-07T08:19:16.7081530Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-09-07T08:19:16.7081724Z The optimizers that are used to optimize ``model``. 2025-09-07T08:19:16.7082005Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-09-07T08:19:16.7082115Z that belong to the submodules. 2025-09-07T08:19:16.7082352Z options (StateDictOptions): the options to control how 2025-09-07T08:19:16.7082561Z model state_dict and optimizer state_dict should be returned. See 2025-09-07T08:19:16.7082695Z `StateDictOptions` for the details. 2025-09-07T08:19:16.7082699Z 2025-09-07T08:19:16.7082784Z Returns: 2025-09-07T08:19:16.7082974Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-09-07T08:19:16.7082996Z 2025-09-07T08:19:16.7083278Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-09-07T08:19:16.7083283Z 2025-09-07T08:19:16.7083532Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.7083539Z 2025-09-07T08:19:16.7112300Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=69. 2025-09-07T08:19:16.7112798Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.7112803Z 2025-09-07T08:19:16.7113008Z Load a checkpoint into a distributed state dict in SPMD style. 2025-09-07T08:19:16.7113013Z 2025-09-07T08:19:16.7113230Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-09-07T08:19:16.7113539Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-09-07T08:19:16.7113754Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-09-07T08:19:16.7113848Z costs). 2025-09-07T08:19:16.7113853Z 2025-09-07T08:19:16.7114030Z Each rank will try to read the least amount of data necessary 2025-09-07T08:19:16.7114258Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-09-07T08:19:16.7114518Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-09-07T08:19:16.7114587Z 2025-09-07T08:19:16.7114848Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T08:19:16.7115114Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-09-07T08:19:16.7115280Z ``load_state_dict`` once the deserialization is complete. 2025-09-07T08:19:16.7115532Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-09-07T08:19:16.7115691Z it in the ``state_dict`` with the deserialized object. 2025-09-07T08:19:16.7115695Z 2025-09-07T08:19:16.7115796Z .. warning:: 2025-09-07T08:19:16.7115972Z All tensors in ``state_dict`` must be allocated on their 2025-09-07T08:19:16.7116136Z destination device *prior to* calling this function. 2025-09-07T08:19:16.7116140Z 2025-09-07T08:19:16.7116376Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-09-07T08:19:16.7116490Z on state_dict. 2025-09-07T08:19:16.7116495Z 2025-09-07T08:19:16.7116596Z .. warning:: 2025-09-07T08:19:16.7116798Z Users must call `load_state_dict` on the root module to ensure load 2025-09-07T08:19:16.7116976Z pos-processing and non-tensor data properly propagates. 2025-09-07T08:19:16.7116980Z 2025-09-07T08:19:16.7117073Z .. note: 2025-09-07T08:19:16.7117363Z If no process group is initialized, this function will assume the intent 2025-09-07T08:19:16.7117597Z is to load a checkpoint into the local process. This can be useful in the 2025-09-07T08:19:16.7117840Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-09-07T08:19:16.7117937Z or ShardedTensor) 2025-09-07T08:19:16.7117955Z 2025-09-07T08:19:16.7118035Z .. note: 2025-09-07T08:19:16.7118169Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T08:19:16.7118174Z 2025-09-07T08:19:16.7118307Z Args: 2025-09-07T08:19:16.7118515Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-09-07T08:19:16.7118669Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T08:19:16.7118876Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T08:19:16.7119075Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T08:19:16.7119252Z It can also be a key if the storage is a key-value store. 2025-09-07T08:19:16.7119347Z (Default: ``None``) 2025-09-07T08:19:16.7119486Z storage_reader (Optional[StorageReader]): 2025-09-07T08:19:16.7119686Z Instance of StorageWriter used to perform reads. If this is not 2025-09-07T08:19:16.7119883Z specified, DCP will automatically infer the reader based on the 2025-09-07T08:19:16.7120090Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T08:19:16.7120197Z be raised. (Default: ``None``) 2025-09-07T08:19:16.7120321Z planner (Optional[LoadPlanner]): 2025-09-07T08:19:16.7120516Z Instance of LoadPlanner. If this is not specified, the default 2025-09-07T08:19:16.7120644Z planner will be used. (Default: ``None``) 2025-09-07T08:19:16.7120880Z process_group (Optional[ProcessGroup]): 2025-09-07T08:19:16.7121061Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T08:19:16.7121169Z (Default: ``None``) 2025-09-07T08:19:16.7121381Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-09-07T08:19:16.7121634Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-09-07T08:19:16.7121733Z Returns: 2025-09-07T08:19:16.7121815Z None. 2025-09-07T08:19:16.7121820Z 2025-09-07T08:19:16.7121917Z Examples 2025-09-07T08:19:16.7122013Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.7122112Z >>> my_model = MyModule() 2025-09-07T08:19:16.7122260Z >>> optimizer = Adagrad(my_model.parameters()) 2025-09-07T08:19:16.7122385Z >>> model_state_dict = my_model.state_dict() 2025-09-07T08:19:16.7122613Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-09-07T08:19:16.7122787Z ... "/checkpoint/1" 2025-09-07T08:19:16.7122870Z ... ) 2025-09-07T08:19:16.7122874Z 2025-09-07T08:19:16.7123039Z >>> torch.distributed.checkpoint.load_state_dict( 2025-09-07T08:19:16.7123145Z >>> state_dict=model_state_dict, 2025-09-07T08:19:16.7123259Z >>> storage_reader=fs_storage_reader, 2025-09-07T08:19:16.7123351Z >>> ) 2025-09-07T08:19:16.7123355Z 2025-09-07T08:19:16.7123549Z >>> # module.load_state_dict() function might have customized steps 2025-09-07T08:19:16.7123690Z >>> # to flush the state_dict, must call it to 2025-09-07T08:19:16.7123792Z >>> # ensure correct behavior. 2025-09-07T08:19:16.7123918Z >>> my_model.load_state_dict(model_state_dict) 2025-09-07T08:19:16.7123936Z 2025-09-07T08:19:16.7124020Z .. note:: 2025-09-07T08:19:16.7124363Z load_state_dict uses collectives to coordinate reads across ranks. 2025-09-07T08:19:16.7124594Z For NCCL-based process groups, internal tensor representations of 2025-09-07T08:19:16.7124826Z objects must be moved to the GPU device before communication takes place. 2025-09-07T08:19:16.7125061Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T08:19:16.7125283Z and it is the user's responsibility to ensure that this is set so that each 2025-09-07T08:19:16.7125463Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T08:19:16.7125467Z 2025-09-07T08:19:16.7125729Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.7125734Z 2025-09-07T08:19:16.7150704Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=97. 2025-09-07T08:19:16.7151167Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.7151243Z 2025-09-07T08:19:16.7151361Z Save a distributed model in SPMD style. 2025-09-07T08:19:16.7151365Z 2025-09-07T08:19:16.7151597Z This function is different from ``torch.save()`` as it handles 2025-09-07T08:19:16.7151855Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-09-07T08:19:16.7151860Z 2025-09-07T08:19:16.7152127Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T08:19:16.7152271Z save will call ``state_dict`` before serialization. 2025-09-07T08:19:16.7152276Z 2025-09-07T08:19:16.7152384Z .. warning:: 2025-09-07T08:19:16.7152615Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-09-07T08:19:16.7152714Z for saved state_dicts. 2025-09-07T08:19:16.7152718Z 2025-09-07T08:19:16.7152814Z .. warning:: 2025-09-07T08:19:16.7153018Z If using the `process_group` argument, make sure that only its ranks 2025-09-07T08:19:16.7153229Z call `save_state_dict` and that all data in state_dict belong to it. 2025-09-07T08:19:16.7153236Z 2025-09-07T08:19:16.7153318Z .. note:: 2025-09-07T08:19:16.7153596Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-09-07T08:19:16.7153885Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-09-07T08:19:16.7153991Z group needs to be passed in. 2025-09-07T08:19:16.7153996Z 2025-09-07T08:19:16.7154089Z .. note:: 2025-09-07T08:19:16.7154352Z If no process group is available, this function assumes the intention is to save the 2025-09-07T08:19:16.7154471Z state_dict in the local process. 2025-09-07T08:19:16.7154475Z 2025-09-07T08:19:16.7154555Z .. note: 2025-09-07T08:19:16.7154688Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T08:19:16.7154693Z 2025-09-07T08:19:16.7154696Z 2025-09-07T08:19:16.7154787Z Args: 2025-09-07T08:19:16.7154939Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T08:19:16.7155092Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T08:19:16.7155299Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T08:19:16.7155556Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T08:19:16.7155739Z It can also be a key if the storage is a key-value store. 2025-09-07T08:19:16.7155833Z (Default: ``None``) 2025-09-07T08:19:16.7155973Z storage_writer (Optional[StorageWriter]): 2025-09-07T08:19:16.7156178Z Instance of StorageWriter used to perform writes. If this is not 2025-09-07T08:19:16.7156379Z specified, DCP will automatically infer the writer based on the 2025-09-07T08:19:16.7156590Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T08:19:16.7156699Z be raised. (Default: ``None``) 2025-09-07T08:19:16.7156827Z planner (Optional[SavePlanner]): 2025-09-07T08:19:16.7157053Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T08:19:16.7157244Z planner will be used. (Default: ``None``) 2025-09-07T08:19:16.7157403Z process_group (Optional[ProcessGroup]): 2025-09-07T08:19:16.7157591Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T08:19:16.7157729Z (Default: ``None``) 2025-09-07T08:19:16.7157869Z no_dist (bool): 2025-09-07T08:19:16.7158079Z If ``True``, this function will assume the intent is to load 2025-09-07T08:19:16.7158215Z a checkpoint on a single rank/process. 2025-09-07T08:19:16.7158310Z (Default: ``False``) 2025-09-07T08:19:16.7158585Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-09-07T08:19:16.7158790Z a checkpoint without using cross-rank synchronization. 2025-09-07T08:19:16.7158883Z (Default: ``True``) 2025-09-07T08:19:16.7159116Z This configuration is experimental and should be used with caution. 2025-09-07T08:19:16.7159382Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T08:19:16.7159457Z 2025-09-07T08:19:16.7159553Z Returns: 2025-09-07T08:19:16.7159714Z Metadata: Metadata object for the saved checkpoint. 2025-09-07T08:19:16.7159720Z 2025-09-07T08:19:16.7159802Z Example: 2025-09-07T08:19:16.7159910Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.7160006Z >>> my_model = MyModule() 2025-09-07T08:19:16.7160010Z 2025-09-07T08:19:16.7160129Z >>> state_dict = {"model": my_model} 2025-09-07T08:19:16.7160133Z 2025-09-07T08:19:16.7160362Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T08:19:16.7160475Z ... "/checkpoint/1" 2025-09-07T08:19:16.7160558Z ... ) 2025-09-07T08:19:16.7160686Z >>> torch.distributed.checkpoint.save( 2025-09-07T08:19:16.7160802Z >>> state_dict=state_dict, 2025-09-07T08:19:16.7160917Z >>> storage_writer=fs_storage_writer, 2025-09-07T08:19:16.7161000Z >>> ) 2025-09-07T08:19:16.7161017Z 2025-09-07T08:19:16.7161106Z .. note:: 2025-09-07T08:19:16.7161319Z save_state_dict uses collectives to coordinate writes across ranks. 2025-09-07T08:19:16.7161547Z For NCCL-based process groups, internal tensor representations of 2025-09-07T08:19:16.7161808Z objects must be moved to the GPU device before communication takes place. 2025-09-07T08:19:16.7162040Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T08:19:16.7162245Z and it is the user's responsibility to ensure that this is set so that 2025-09-07T08:19:16.7162438Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T08:19:16.7162442Z 2025-09-07T08:19:16.7162709Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.7162713Z 2025-09-07T08:19:16.7163374Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=230. 2025-09-07T08:19:16.7163639Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.7164048Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-09-07T08:19:16.7164424Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-09-07T08:19:16.7164429Z 2025-09-07T08:19:16.7164521Z .. warning:: 2025-09-07T08:19:16.7164695Z This feature is experimental and subject to change. 2025-09-07T08:19:16.7164839Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-09-07T08:19:16.7164843Z 2025-09-07T08:19:16.7164926Z Args: 2025-09-07T08:19:16.7165096Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T08:19:16.7165238Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T08:19:16.7165461Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T08:19:16.7165663Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T08:19:16.7165844Z It can also be a key if the storage is a key-value store. 2025-09-07T08:19:16.7165940Z (Default: ``None``) 2025-09-07T08:19:16.7166075Z storage_writer (Optional[StorageWriter]): 2025-09-07T08:19:16.7166300Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-09-07T08:19:16.7166535Z this is not specified, DCP will automatically infer the writer based on the 2025-09-07T08:19:16.7166749Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T08:19:16.7166856Z be raised. (Default: ``None``) 2025-09-07T08:19:16.7166972Z planner (Optional[SavePlanner]): 2025-09-07T08:19:16.7167179Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T08:19:16.7167312Z planner will be used. (Default: ``None``) 2025-09-07T08:19:16.7167451Z process_group (Optional[ProcessGroup]): 2025-09-07T08:19:16.7167630Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T08:19:16.7167758Z (Default: ``None``) 2025-09-07T08:19:16.7167930Z async_checkpointer_type (AsyncCheckpointerType): 2025-09-07T08:19:16.7168098Z whether to do checkpoint in separate thread or process 2025-09-07T08:19:16.7168253Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-09-07T08:19:16.7168357Z async_stager (AsyncStager): 2025-09-07T08:19:16.7168611Z provides staging implementation. If storage_writer implements AsyncStager 2025-09-07T08:19:16.7168832Z and async_stager is provided, async_stager will be used for staging 2025-09-07T08:19:16.7168920Z no_dist (bool): 2025-09-07T08:19:16.7169096Z If ``True``, this function will assume the intent is to save 2025-09-07T08:19:16.7169220Z a checkpoint on a single rank/process. 2025-09-07T08:19:16.7169316Z (Default: ``False``) 2025-09-07T08:19:16.7169637Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-09-07T08:19:16.7169865Z This configuration is experimental and should be used with caution. 2025-09-07T08:19:16.7170169Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T08:19:16.7170174Z 2025-09-07T08:19:16.7170259Z Returns: 2025-09-07T08:19:16.7170469Z Future: A future holding the resultant Metadata object from `save`. 2025-09-07T08:19:16.7170473Z 2025-09-07T08:19:16.7170566Z Example: 2025-09-07T08:19:16.7170665Z >>> # xdoctest: +SKIP 2025-09-07T08:19:16.7170775Z >>> my_model = MyModule() 2025-09-07T08:19:16.7170779Z 2025-09-07T08:19:16.7170889Z >>> state_dict = {"model": my_model} 2025-09-07T08:19:16.7170894Z 2025-09-07T08:19:16.7171123Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T08:19:16.7171230Z ... "/checkpoint/1" 2025-09-07T08:19:16.7171311Z ... ) 2025-09-07T08:19:16.7171532Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-09-07T08:19:16.7171684Z >>> state_dict=state_dict, 2025-09-07T08:19:16.7171809Z >>> storage_writer=fs_storage_writer, 2025-09-07T08:19:16.7171902Z >>> ) 2025-09-07T08:19:16.7171982Z >>> 2025-09-07T08:19:16.7172090Z >>> # ... do some work ... 2025-09-07T08:19:16.7172174Z >>> 2025-09-07T08:19:16.7172283Z >>> checkpoint_future.result() 2025-09-07T08:19:16.7172288Z 2025-09-07T08:19:16.7172380Z 2025-09-07T08:19:16.7172633Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.7172638Z 2025-09-07T08:19:16.7498569Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=94. 2025-09-07T08:19:16.7498855Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:16.7498868Z 2025-09-07T08:19:16.7499082Z Initialize rendezvous event object and record its operations. 2025-09-07T08:19:16.7499087Z 2025-09-07T08:19:16.7499173Z Args: 2025-09-07T08:19:16.7499340Z run_id (str): The run id of the rendezvous. 2025-09-07T08:19:16.7499526Z message (str): The message describing the event. 2025-09-07T08:19:16.7499789Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-09-07T08:19:16.7499983Z name (str): Event name. (E.g. Current action being performed). 2025-09-07T08:19:16.7500135Z hostname (str): Hostname of the node. 2025-09-07T08:19:16.7500309Z pid (Optional[int]): The process id of the node. 2025-09-07T08:19:16.7500551Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-09-07T08:19:16.7500819Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-09-07T08:19:16.7501048Z rank (Optional[int]): The rank of the node, if known. 2025-09-07T08:19:16.7501245Z Returns: 2025-09-07T08:19:16.7501339Z None 2025-09-07T08:19:16.7501422Z Example: 2025-09-07T08:19:16.7501551Z >>> # See DynamicRendezvousHandler class 2025-09-07T08:19:16.7501685Z >>> def _record( 2025-09-07T08:19:16.7501800Z ... self, 2025-09-07T08:19:16.7501896Z ... message: str, 2025-09-07T08:19:16.7502050Z ... node_state: NodeState = NodeState.RUNNING, 2025-09-07T08:19:16.7502160Z ... rank: Optional[int] = None, 2025-09-07T08:19:16.7502261Z ... ) -> None: 2025-09-07T08:19:16.7502379Z ... construct_and_record_rdzv_event( 2025-09-07T08:19:16.7502603Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-09-07T08:19:16.7502732Z ... run_id=self._settings.run_id, 2025-09-07T08:19:16.7502828Z ... message=message, 2025-09-07T08:19:16.7502942Z ... node_state=node_state, 2025-09-07T08:19:16.7503058Z ... hostname=self._this_node.addr, 2025-09-07T08:19:16.7503165Z ... pid=self._this_node.pid, 2025-09-07T08:19:16.7503358Z ... local_id=self._this_node.local_id, 2025-09-07T08:19:16.7503452Z ... rank=rank, 2025-09-07T08:19:16.7503594Z ... ) 2025-09-07T08:19:16.7503599Z 2025-09-07T08:19:16.7503851Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:16.7503856Z 2025-09-07T08:19:17.0574718Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=114. 2025-09-07T08:19:17.0575750Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.0576145Z 2025-09-07T08:19:17.0576327Z This configures FSDP-native mixed precision training. 2025-09-07T08:19:17.0576614Z 2025-09-07T08:19:17.0576704Z Attributes: 2025-09-07T08:19:17.0577071Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-09-07T08:19:17.0577634Z parameters during forward and backward and thus the dtype for 2025-09-07T08:19:17.0578191Z forward and backward computation. Outside forward and backward, the 2025-09-07T08:19:17.0578981Z *sharded* parameters are kept in full precision (e.g. for the 2025-09-07T08:19:17.0579516Z optimizer step), and for model checkpointing, the parameters are 2025-09-07T08:19:17.0580008Z always saved in full precision. (Default: ``None``) 2025-09-07T08:19:17.0580491Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T08:19:17.0581099Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-09-07T08:19:17.0581602Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-09-07T08:19:17.0582102Z the ``param_dtype`` value, still running gradient reduction in low 2025-09-07T08:19:17.0582643Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-09-07T08:19:17.0583183Z to force gradient reduction to run in full precision. (Default: 2025-09-07T08:19:17.0583650Z ``None``) 2025-09-07T08:19:17.0584014Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T08:19:17.0584614Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-09-07T08:19:17.0585305Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-09-07T08:19:17.0585824Z dtype thereafter. For model checkpointing, the buffers are saved 2025-09-07T08:19:17.0586334Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-09-07T08:19:17.0586722Z ``None``) 2025-09-07T08:19:17.0587063Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-09-07T08:19:17.0587598Z gradients to full precision after the backward pass in preparation 2025-09-07T08:19:17.0588128Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-09-07T08:19:17.0588664Z in the dtype used for gradient reduction, which can save memory if 2025-09-07T08:19:17.0589275Z using a custom optimizer that supports running in low precision. 2025-09-07T08:19:17.0589714Z (Default: ``False``) 2025-09-07T08:19:17.0590116Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-09-07T08:19:17.0590640Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-09-07T08:19:17.0591182Z that parameter and input dtypes match for forward computation, as 2025-09-07T08:19:17.0591726Z required by many ops. This may need to be set to ``True`` when only 2025-09-07T08:19:17.0592279Z applying mixed precision to some but not all FSDP modules, in which 2025-09-07T08:19:17.0592835Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-09-07T08:19:17.0593253Z (Default: ``False``) 2025-09-07T08:19:17.0593662Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-09-07T08:19:17.0594207Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-09-07T08:19:17.0594723Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-09-07T08:19:17.0595169Z this does not do anything. (Default: ``True``) 2025-09-07T08:19:17.0595695Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-09-07T08:19:17.0596220Z module classes to ignore for mixed precision when using an 2025-09-07T08:19:17.0596711Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-09-07T08:19:17.0597219Z applied to them separately with mixed precision disabled (meaning 2025-09-07T08:19:17.0597757Z that the final FSDP construction would deviate from the specified 2025-09-07T08:19:17.0598279Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-09-07T08:19:17.0598793Z not do anything. This API is experimental and subject to change. 2025-09-07T08:19:17.0599219Z (Default: ``(_BatchNorm,)``) 2025-09-07T08:19:17.0599425Z 2025-09-07T08:19:17.0599610Z .. note:: This API is experimental and subject to change. 2025-09-07T08:19:17.0599895Z 2025-09-07T08:19:17.0600179Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-09-07T08:19:17.0600532Z 2025-09-07T08:19:17.0600714Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-09-07T08:19:17.0601126Z precision, but buffers are not. 2025-09-07T08:19:17.0601331Z 2025-09-07T08:19:17.0601546Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-09-07T08:19:17.0602071Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-09-07T08:19:17.0602633Z Disabling FSDP's mixed precision for those norm modules only means that 2025-09-07T08:19:17.0603199Z the affine parameters are kept in ``float32``. However, this incurs 2025-09-07T08:19:17.0603771Z separate all-gathers and reduce-scatters for those norm modules, which 2025-09-07T08:19:17.0604419Z may be inefficient, so if the workload permits, the user should prefer 2025-09-07T08:19:17.0604915Z to still apply mixed precision to those modules. 2025-09-07T08:19:17.0605186Z 2025-09-07T08:19:17.0605396Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-09-07T08:19:17.0605936Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-09-07T08:19:17.0606491Z modules will have FSDP applied to them separately with mixed precision 2025-09-07T08:19:17.0606997Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-09-07T08:19:17.0607296Z 2025-09-07T08:19:17.0607504Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-09-07T08:19:17.0608044Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-09-07T08:19:17.0608549Z its ``cast_root_forward_inputs`` takes precedence over its 2025-09-07T08:19:17.0609017Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-09-07T08:19:17.0609515Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-09-07T08:19:17.0610113Z sufficient for the typical case where each FSDP instance has the same 2025-09-07T08:19:17.0610690Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-09-07T08:19:17.0611222Z ``param_dtype`` at the beginning of the model's forward pass. 2025-09-07T08:19:17.0611519Z 2025-09-07T08:19:17.0611724Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-09-07T08:19:17.0612294Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-09-07T08:19:17.0612864Z values to configure casting inputs or not before each instance's 2025-09-07T08:19:17.0613379Z forward. In such a case, since the casts happen before each FSDP 2025-09-07T08:19:17.0613912Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-09-07T08:19:17.0614468Z submodules run before its FSDP submodules to avoid the activation dtype 2025-09-07T08:19:17.0615033Z being changed due to a different ``MixedPrecision`` configuration. 2025-09-07T08:19:17.0615373Z 2025-09-07T08:19:17.0615464Z Example:: 2025-09-07T08:19:17.0615595Z 2025-09-07T08:19:17.0615740Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:17.0616172Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-09-07T08:19:17.0616557Z >>> model[1] = FSDP( 2025-09-07T08:19:17.0616829Z >>> model[1], 2025-09-07T08:19:17.0617300Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-09-07T08:19:17.0617809Z >>> ) 2025-09-07T08:19:17.0618020Z >>> model = FSDP( 2025-09-07T08:19:17.0618273Z >>> model, 2025-09-07T08:19:17.0618735Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-09-07T08:19:17.0619245Z >>> ) 2025-09-07T08:19:17.0619370Z 2025-09-07T08:19:17.0619579Z The above shows a working example. On the other hand, if ``model[1]`` 2025-09-07T08:19:17.0620115Z were replaced with ``model[0]``, meaning that the submodule using 2025-09-07T08:19:17.0620712Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-09-07T08:19:17.0621283Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-09-07T08:19:17.0621693Z ones. 2025-09-07T08:19:17.0621822Z 2025-09-07T08:19:17.0621826Z 2025-09-07T08:19:17.0622076Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.0622455Z 2025-09-07T08:19:17.0623063Z msg = Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=295. 2025-09-07T08:19:17.0624023Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.0624415Z 2025-09-07T08:19:17.0624619Z ``FullStateDictConfig`` is a config class meant to be used with 2025-09-07T08:19:17.0625137Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-09-07T08:19:17.0625644Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-09-07T08:19:17.0626188Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-09-07T08:19:17.0626731Z is meant to be used via the :func:`state_dict_type` context manager as 2025-09-07T08:19:17.0627134Z follows: 2025-09-07T08:19:17.0627250Z 2025-09-07T08:19:17.0627387Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:17.0627857Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T08:19:17.0628344Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-09-07T08:19:17.0628797Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-09-07T08:19:17.0629350Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-09-07T08:19:17.0629792Z >>> state = fsdp.state_dict() 2025-09-07T08:19:17.0630219Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-09-07T08:19:17.0630823Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-09-07T08:19:17.0631415Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-09-07T08:19:17.0631875Z >>> if dist.get_rank() == 0: 2025-09-07T08:19:17.0632240Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-09-07T08:19:17.0632679Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-09-07T08:19:17.0633051Z >>> model.load_state_dict(state_dict) 2025-09-07T08:19:17.0633519Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-09-07T08:19:17.0634110Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-09-07T08:19:17.0634569Z >>> fsdp = FSDP( 2025-09-07T08:19:17.0634813Z ... model, 2025-09-07T08:19:17.0635095Z ... device_id=torch.cuda.current_device(), 2025-09-07T08:19:17.0635438Z ... auto_wrap_policy=..., 2025-09-07T08:19:17.0635744Z ... sync_module_states=True, 2025-09-07T08:19:17.0636034Z ... ) 2025-09-07T08:19:17.0636379Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-09-07T08:19:17.0636741Z 2025-09-07T08:19:17.0636828Z Attributes: 2025-09-07T08:19:17.0637163Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-09-07T08:19:17.0637692Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-09-07T08:19:17.0638172Z ranks save the full state dict. (Default: ``False``) 2025-09-07T08:19:17.0638441Z 2025-09-07T08:19:17.0638708Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.0639078Z 2025-09-07T08:19:17.0713259Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=634. 2025-09-07T08:19:17.0714444Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.0715258Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T08:19:17.0715648Z 2025-09-07T08:19:17.0715910Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-09-07T08:19:17.0716504Z The target module does not have to be a FSDP module. If the target 2025-09-07T08:19:17.0717200Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-09-07T08:19:17.0717597Z 2025-09-07T08:19:17.0717857Z .. note:: This API should be called for only the top-level (root) 2025-09-07T08:19:17.0718271Z module. 2025-09-07T08:19:17.0718426Z 2025-09-07T08:19:17.0718691Z .. note:: This API enables users to transparently use the conventional 2025-09-07T08:19:17.0719214Z ``state_dict`` API to take model checkpoints in cases where the 2025-09-07T08:19:17.0719732Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-09-07T08:19:17.0720261Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-09-07T08:19:17.0720831Z instances, while dispatching into `sharded_state_dict` implementation 2025-09-07T08:19:17.0721282Z for FSDP: 2025-09-07T08:19:17.0721432Z 2025-09-07T08:19:17.0721531Z Example:: 2025-09-07T08:19:17.0721725Z 2025-09-07T08:19:17.0721897Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:17.0722254Z >>> model = DDP(FSDP(...)) 2025-09-07T08:19:17.0722581Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:17.0722900Z >>> model, 2025-09-07T08:19:17.0723188Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T08:19:17.0723650Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-09-07T08:19:17.0724322Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-09-07T08:19:17.0724857Z >>> ) 2025-09-07T08:19:17.0725129Z >>> param_state_dict = model.state_dict() 2025-09-07T08:19:17.0725554Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T08:19:17.0725844Z 2025-09-07T08:19:17.0725927Z Args: 2025-09-07T08:19:17.0726184Z module (torch.nn.Module): Root module. 2025-09-07T08:19:17.0726661Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T08:19:17.0727258Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-09-07T08:19:17.0727720Z target ``state_dict_type``. 2025-09-07T08:19:17.0728194Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-09-07T08:19:17.0728690Z for the optimizer state dict. 2025-09-07T08:19:17.0728913Z 2025-09-07T08:19:17.0729007Z Returns: 2025-09-07T08:19:17.0729360Z A StateDictSettings that include the previous state_dict type and 2025-09-07T08:19:17.0729823Z configuration for the module. 2025-09-07T08:19:17.0730132Z 2025-09-07T08:19:17.0730510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.0730952Z 2025-09-07T08:19:17.0731774Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=792. 2025-09-07T08:19:17.0733154Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.0733789Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T08:19:17.0734173Z 2025-09-07T08:19:17.0734493Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-09-07T08:19:17.0735069Z :meth:`set_state_dict_type` for the detail. 2025-09-07T08:19:17.0735308Z 2025-09-07T08:19:17.0735414Z Example:: 2025-09-07T08:19:17.0735551Z 2025-09-07T08:19:17.0735681Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:17.0736111Z >>> model = DDP(FSDP(...)) 2025-09-07T08:19:17.0736437Z >>> with FSDP.state_dict_type( 2025-09-07T08:19:17.0736749Z >>> model, 2025-09-07T08:19:17.0737034Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T08:19:17.0737367Z >>> ): 2025-09-07T08:19:17.0737631Z >>> checkpoint = model.state_dict() 2025-09-07T08:19:17.0737864Z 2025-09-07T08:19:17.0737962Z Args: 2025-09-07T08:19:17.0738218Z module (torch.nn.Module): Root module. 2025-09-07T08:19:17.0738682Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T08:19:17.0739277Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-09-07T08:19:17.0739793Z configuration for the target ``state_dict_type``. 2025-09-07T08:19:17.0740311Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-09-07T08:19:17.0740867Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-09-07T08:19:17.0741274Z 2025-09-07T08:19:17.0741646Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.0742017Z 2025-09-07T08:19:17.0771964Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1805. 2025-09-07T08:19:17.0773157Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.0773674Z 2025-09-07T08:19:17.0773929Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-09-07T08:19:17.0774285Z 2025-09-07T08:19:17.0774473Z The given state-dict can be transformed to one of three types: 2025-09-07T08:19:17.0775346Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-09-07T08:19:17.0775895Z 2025-09-07T08:19:17.0776287Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-09-07T08:19:17.0776929Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-09-07T08:19:17.0777361Z avoid OOM. 2025-09-07T08:19:17.0777486Z 2025-09-07T08:19:17.0777721Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-09-07T08:19:17.0778286Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-09-07T08:19:17.0778698Z memory. 2025-09-07T08:19:17.0778810Z 2025-09-07T08:19:17.0779038Z For local state_dict, no transformation will be performed. But a state 2025-09-07T08:19:17.0779600Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-09-07T08:19:17.0780071Z nature (this is not supported yet). 2025-09-07T08:19:17.0780288Z 2025-09-07T08:19:17.0780379Z Example:: 2025-09-07T08:19:17.0780542Z 2025-09-07T08:19:17.0780666Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:17.0781149Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T08:19:17.0781752Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T08:19:17.0782205Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T08:19:17.0782705Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T08:19:17.0783130Z >>> # Save a checkpoint 2025-09-07T08:19:17.0783408Z >>> model, optim = ... 2025-09-07T08:19:17.0783682Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:17.0783975Z >>> model, 2025-09-07T08:19:17.0784238Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T08:19:17.0784602Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T08:19:17.0784983Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T08:19:17.0785332Z >>> ) 2025-09-07T08:19:17.0785570Z >>> state_dict = model.state_dict() 2025-09-07T08:19:17.0785964Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T08:19:17.0786468Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T08:19:17.0786834Z >>> # Load a checkpoint 2025-09-07T08:19:17.0787117Z >>> model, optim = ... 2025-09-07T08:19:17.0787445Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T08:19:17.0787810Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:17.0788092Z >>> model, 2025-09-07T08:19:17.0788358Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T08:19:17.0788719Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T08:19:17.0789102Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T08:19:17.0789438Z >>> ) 2025-09-07T08:19:17.0789679Z >>> model.load_state_dict(state_dict) 2025-09-07T08:19:17.0790054Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T08:19:17.0790418Z >>> model, optim, optim_state_dict 2025-09-07T08:19:17.0790726Z >>> ) 2025-09-07T08:19:17.0790970Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T08:19:17.0791206Z 2025-09-07T08:19:17.0791299Z Args: 2025-09-07T08:19:17.0791614Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T08:19:17.0792123Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T08:19:17.0792574Z were passed into the optimizer ``optim``. 2025-09-07T08:19:17.0793006Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T08:19:17.0793398Z parameters. 2025-09-07T08:19:17.0793748Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-09-07T08:19:17.0794297Z transform. If the value is None, optim.state_dict() will be used. ( 2025-09-07T08:19:17.0794729Z Default: ``None``) 2025-09-07T08:19:17.0795148Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T08:19:17.0795685Z are sharded or ``None`` if using the default process group. ( 2025-09-07T08:19:17.0796117Z Default: ``None``) 2025-09-07T08:19:17.0796297Z 2025-09-07T08:19:17.0796379Z Returns: 2025-09-07T08:19:17.0796699Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-09-07T08:19:17.0797167Z ``model``. The sharding of the optimizer state is based on 2025-09-07T08:19:17.0797551Z ``state_dict_type``. 2025-09-07T08:19:17.0797728Z 2025-09-07T08:19:17.0797980Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.0798346Z 2025-09-07T08:19:17.0799202Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1903. 2025-09-07T08:19:17.0800419Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.0800797Z 2025-09-07T08:19:17.0801161Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-09-07T08:19:17.0801633Z 2025-09-07T08:19:17.0801797Z Given a ``optim_state_dict`` that is transformed through 2025-09-07T08:19:17.0802299Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-09-07T08:19:17.0802882Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-09-07T08:19:17.0803403Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-09-07T08:19:17.0803704Z 2025-09-07T08:19:17.0803842Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:17.0804392Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T08:19:17.0804912Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T08:19:17.0805366Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T08:19:17.0805864Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T08:19:17.0806274Z >>> # Save a checkpoint 2025-09-07T08:19:17.0806557Z >>> model, optim = ... 2025-09-07T08:19:17.0806834Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:17.0807118Z >>> model, 2025-09-07T08:19:17.0807424Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T08:19:17.0807782Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T08:19:17.0808169Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T08:19:17.0808512Z >>> ) 2025-09-07T08:19:17.0808731Z >>> state_dict = model.state_dict() 2025-09-07T08:19:17.0809071Z >>> original_osd = optim.state_dict() 2025-09-07T08:19:17.0809426Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-09-07T08:19:17.0809753Z >>> model, 2025-09-07T08:19:17.0809964Z >>> optim, 2025-09-07T08:19:17.0810213Z >>> optim_state_dict=original_osd 2025-09-07T08:19:17.0810514Z >>> ) 2025-09-07T08:19:17.0810782Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T08:19:17.0811126Z >>> # Load a checkpoint 2025-09-07T08:19:17.0811396Z >>> model, optim = ... 2025-09-07T08:19:17.0811718Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T08:19:17.0812094Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:17.0812366Z >>> model, 2025-09-07T08:19:17.0812627Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T08:19:17.0812982Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T08:19:17.0813435Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T08:19:17.0813814Z >>> ) 2025-09-07T08:19:17.0814052Z >>> model.load_state_dict(state_dict) 2025-09-07T08:19:17.0814431Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T08:19:17.0814811Z >>> model, optim, optim_state_dict 2025-09-07T08:19:17.0815106Z >>> ) 2025-09-07T08:19:17.0815353Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T08:19:17.0815598Z 2025-09-07T08:19:17.0815677Z Args: 2025-09-07T08:19:17.0826804Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T08:19:17.0827453Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T08:19:17.0827924Z were passed into the optimizer ``optim``. 2025-09-07T08:19:17.0828360Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T08:19:17.0828765Z parameters. 2025-09-07T08:19:17.0829134Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-09-07T08:19:17.0829678Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-09-07T08:19:17.0830182Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-09-07T08:19:17.0830678Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-09-07T08:19:17.0831169Z load_directly (bool): If this is set to True, this API will also 2025-09-07T08:19:17.0831683Z call optim.load_state_dict(result) before returning the result. 2025-09-07T08:19:17.0832226Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-09-07T08:19:17.0832659Z (Default: ``False``) 2025-09-07T08:19:17.0833083Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T08:19:17.0833635Z are sharded or ``None`` if using the default process group. ( 2025-09-07T08:19:17.0834081Z Default: ``None``) 2025-09-07T08:19:17.0834251Z 2025-09-07T08:19:17.0834505Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.0834885Z 2025-09-07T08:19:17.1331817Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=129. 2025-09-07T08:19:17.1332866Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.1333248Z 2025-09-07T08:19:17.1333489Z RemoteModule instance can only be created after RPC initialization. 2025-09-07T08:19:17.1333835Z 2025-09-07T08:19:17.1334026Z It creates a user-specified module on a specified remote node. 2025-09-07T08:19:17.1334583Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T08:19:17.1335037Z executed on the remote node. 2025-09-07T08:19:17.1335662Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T08:19:17.1336183Z gradients back to the corresponding remote module. 2025-09-07T08:19:17.1336797Z It can be shared across processors using `RPC framework `__, 2025-09-07T08:19:17.1337467Z without incurring any overheads of copying the actual module, 2025-09-07T08:19:17.1337980Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-09-07T08:19:17.1338408Z pointing to the remote module. 2025-09-07T08:19:17.1338598Z 2025-09-07T08:19:17.1338796Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-09-07T08:19:17.1339318Z the ``forward`` method of the module returned by the ``module_cls``. 2025-09-07T08:19:17.1339650Z 2025-09-07T08:19:17.1340050Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-09-07T08:19:17.1340693Z 2025-09-07T08:19:17.1341203Z Particularly, to create a hybrid model, typically the local modules should be 2025-09-07T08:19:17.1342152Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-09-07T08:19:17.1342746Z Hybrid Example: 2025-09-07T08:19:17.1343010Z >>> class HybridModel(nn.Module): 2025-09-07T08:19:17.1343340Z >>> def __init__(self) -> None: 2025-09-07T08:19:17.1343677Z >>> nn.Module.__init__(self) 2025-09-07T08:19:17.1344042Z >>> self.remote_embedding = RemoteModule(...) 2025-09-07T08:19:17.1344436Z >>> self.local_linear = nn.Linear(...) 2025-09-07T08:19:17.1344671Z 2025-09-07T08:19:17.1344881Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T08:19:17.1345435Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T08:19:17.1346107Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-09-07T08:19:17.1346571Z ``def forward(input: Tensor) -> Tensor:`` and 2025-09-07T08:19:17.1346989Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-09-07T08:19:17.1347263Z 2025-09-07T08:19:17.1347362Z .. note:: 2025-09-07T08:19:17.1347628Z If the remote module is placed on a cuda device, 2025-09-07T08:19:17.1348121Z any input CPU tensors will be automatically moved to the same cuda device, 2025-09-07T08:19:17.1349092Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-09-07T08:19:17.1349853Z 2025-09-07T08:19:17.1350009Z Args: 2025-09-07T08:19:17.1350716Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T08:19:17.1351633Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T08:19:17.1352136Z formats: 2025-09-07T08:19:17.1352276Z 2025-09-07T08:19:17.1352430Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T08:19:17.1352834Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T08:19:17.1353195Z 2025-09-07T08:19:17.1353438Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T08:19:17.1353924Z module_cls (nn.Module): For example, 2025-09-07T08:19:17.1354266Z >>> class MyModule(nn.Module): 2025-09-07T08:19:17.1354584Z >>> def forward(input): 2025-09-07T08:19:17.1354873Z >>> return input + 1 2025-09-07T08:19:17.1355164Z >>> 2025-09-07T08:19:17.1355391Z >>> module_cls = MyModule 2025-09-07T08:19:17.1355786Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T08:19:17.1356281Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T08:19:17.1356872Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T08:19:17.1357518Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T08:19:17.1358166Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T08:19:17.1358750Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T08:19:17.1359102Z 2025-09-07T08:19:17.1359185Z Returns: 2025-09-07T08:19:17.1359548Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T08:19:17.1360147Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T08:19:17.1360769Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T08:19:17.1361299Z on the user-provided module on the remote side. 2025-09-07T08:19:17.1361569Z 2025-09-07T08:19:17.1361660Z Example:: 2025-09-07T08:19:17.1361937Z Run the following code in two different processes: 2025-09-07T08:19:17.1362198Z 2025-09-07T08:19:17.1362324Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:17.1362638Z >>> # On worker 0: 2025-09-07T08:19:17.1362879Z >>> import torch 2025-09-07T08:19:17.1363165Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.1363510Z >>> from torch import nn, Tensor 2025-09-07T08:19:17.1363940Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T08:19:17.1364436Z >>> 2025-09-07T08:19:17.1364703Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T08:19:17.1365079Z >>> remote_linear_module = RemoteModule( 2025-09-07T08:19:17.1365440Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T08:19:17.1365755Z >>> ) 2025-09-07T08:19:17.1365985Z >>> input = torch.randn(128, 20) 2025-09-07T08:19:17.1366350Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T08:19:17.1366721Z >>> ret = ret_fut.wait() 2025-09-07T08:19:17.1366986Z >>> rpc.shutdown() 2025-09-07T08:19:17.1367197Z 2025-09-07T08:19:17.1367286Z >>> # On worker 1: 2025-09-07T08:19:17.1367535Z >>> import torch 2025-09-07T08:19:17.1367819Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.1368129Z >>> 2025-09-07T08:19:17.1368392Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T08:19:17.1368743Z >>> rpc.shutdown() 2025-09-07T08:19:17.1368899Z 2025-09-07T08:19:17.1369163Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.1369527Z 2025-09-07T08:19:17.1370226Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=506. 2025-09-07T08:19:17.1371302Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.1371693Z 2025-09-07T08:19:17.1372014Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-09-07T08:19:17.1372468Z 2025-09-07T08:19:17.1372794Z This alternate initialization method can be particularly useful if we want to create multiple 2025-09-07T08:19:17.1373812Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-09-07T08:19:17.1374323Z 2025-09-07T08:19:17.1374616Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-09-07T08:19:17.1375184Z which is not supported. The recommended way is as follows: 2025-09-07T08:19:17.1375489Z 2025-09-07T08:19:17.1375607Z 1. the sender creates a RemoteModule; 2025-09-07T08:19:17.1375983Z 2. the sender sends its ``module_rref`` over RPC; 2025-09-07T08:19:17.1376578Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-09-07T08:19:17.1377028Z 2025-09-07T08:19:17.1377134Z Example:: 2025-09-07T08:19:17.1377401Z Run the following code in two different processes: 2025-09-07T08:19:17.1377673Z 2025-09-07T08:19:17.1377787Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:17.1378104Z >>> # On worker 0: 2025-09-07T08:19:17.1378353Z >>> import torch 2025-09-07T08:19:17.1378694Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.1379039Z >>> from torch import nn, Tensor 2025-09-07T08:19:17.1379464Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T08:19:17.1379883Z >>> 2025-09-07T08:19:17.1380128Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T08:19:17.1380491Z >>> remote_module = RemoteModule( 2025-09-07T08:19:17.1380833Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T08:19:17.1381158Z >>> ) 2025-09-07T08:19:17.1381352Z >>> 2025-09-07T08:19:17.1381581Z >>> remote_module1 = rpc.rpc_sync( 2025-09-07T08:19:17.1381892Z >>> "worker1/cpu", 2025-09-07T08:19:17.1382190Z >>> RemoteModule.init_from_module_rref, 2025-09-07T08:19:17.1382572Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-09-07T08:19:17.1382931Z >>> ) 2025-09-07T08:19:17.1383155Z >>> rpc.shutdown() 2025-09-07T08:19:17.1383310Z 2025-09-07T08:19:17.1383412Z >>> # On worker 1: 2025-09-07T08:19:17.1383649Z >>> import torch 2025-09-07T08:19:17.1383930Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.1384251Z >>> 2025-09-07T08:19:17.1384510Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T08:19:17.1384853Z >>> rpc.shutdown() 2025-09-07T08:19:17.1385009Z 2025-09-07T08:19:17.1385101Z Args: 2025-09-07T08:19:17.1385512Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T08:19:17.1386208Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T08:19:17.1386712Z formats: 2025-09-07T08:19:17.1386847Z 2025-09-07T08:19:17.1386998Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T08:19:17.1387405Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T08:19:17.1387727Z 2025-09-07T08:19:17.1387973Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T08:19:17.1388599Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-09-07T08:19:17.1389086Z the created remote module. 2025-09-07T08:19:17.1389573Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T08:19:17.1390211Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T08:19:17.1390777Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T08:19:17.1391354Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T08:19:17.1391714Z 2025-09-07T08:19:17.1391796Z Returns: 2025-09-07T08:19:17.1392162Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T08:19:17.1392747Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-09-07T08:19:17.1393373Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T08:19:17.1393915Z on the user-provided module on the remote side. 2025-09-07T08:19:17.1394195Z 2025-09-07T08:19:17.1394457Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.1394819Z 2025-09-07T08:19:17.1395473Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=598. 2025-09-07T08:19:17.1396453Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.1396827Z 2025-09-07T08:19:17.1397068Z A RemoteModule instance can only be created after RPC initialization. 2025-09-07T08:19:17.1397413Z 2025-09-07T08:19:17.1397604Z It creates a user-specified module on a specified remote node. 2025-09-07T08:19:17.1398148Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T08:19:17.1398611Z executed on the remote node. 2025-09-07T08:19:17.1399109Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T08:19:17.1399627Z gradients back to the corresponding remote module. 2025-09-07T08:19:17.1399894Z 2025-09-07T08:19:17.1400110Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-09-07T08:19:17.1400663Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-09-07T08:19:17.1401248Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-09-07T08:19:17.1401813Z and ``forward`` are the same as the ``forward`` method of the module 2025-09-07T08:19:17.1402228Z returned by the ``module_cls``. 2025-09-07T08:19:17.1402443Z 2025-09-07T08:19:17.1402640Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T08:19:17.1403209Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T08:19:17.1403810Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-09-07T08:19:17.1404242Z 2025-09-07T08:19:17.1404387Z | ``def forward(input: Tensor) -> Tensor:`` 2025-09-07T08:19:17.1404786Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-09-07T08:19:17.1405073Z 2025-09-07T08:19:17.1405154Z Args: 2025-09-07T08:19:17.1405571Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T08:19:17.1406332Z The format should be "/", where the device field can be parsed as torch.device type. 2025-09-07T08:19:17.1406937Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-09-07T08:19:17.1407418Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T08:19:17.1408035Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-09-07T08:19:17.1408410Z 2025-09-07T08:19:17.1408553Z >>> class MyModule(nn.Module): 2025-09-07T08:19:17.1408869Z >>> def forward(input): 2025-09-07T08:19:17.1409162Z >>> return input + 1 2025-09-07T08:19:17.1409450Z >>> 2025-09-07T08:19:17.1409677Z >>> module_cls = MyModule 2025-09-07T08:19:17.1409872Z 2025-09-07T08:19:17.1410081Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T08:19:17.1410584Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T08:19:17.1410888Z 2025-09-07T08:19:17.1410971Z Returns: 2025-09-07T08:19:17.1411336Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T08:19:17.1411927Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T08:19:17.1412545Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T08:19:17.1413064Z on the user-provided module on the remote side. 2025-09-07T08:19:17.1413325Z 2025-09-07T08:19:17.1413417Z Example:: 2025-09-07T08:19:17.1413690Z Run the following code in two different processes: 2025-09-07T08:19:17.1413951Z 2025-09-07T08:19:17.1414077Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:17.1414417Z >>> # On worker 0: 2025-09-07T08:19:17.1414650Z >>> import torch 2025-09-07T08:19:17.1414925Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.1415260Z >>> from torch import nn, Tensor 2025-09-07T08:19:17.1415684Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T08:19:17.1416092Z >>> 2025-09-07T08:19:17.1416347Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T08:19:17.1416720Z >>> remote_linear_module = RemoteModule( 2025-09-07T08:19:17.1417076Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T08:19:17.1417391Z >>> ) 2025-09-07T08:19:17.1417619Z >>> input = torch.randn(128, 20) 2025-09-07T08:19:17.1417978Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T08:19:17.1418352Z >>> ret = ret_fut.wait() 2025-09-07T08:19:17.1418616Z >>> rpc.shutdown() 2025-09-07T08:19:17.1418787Z 2025-09-07T08:19:17.1418932Z >>> # On worker 1: 2025-09-07T08:19:17.1419184Z >>> import torch 2025-09-07T08:19:17.1419463Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.1419545Z >>> 2025-09-07T08:19:17.1419682Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T08:19:17.1419787Z >>> rpc.shutdown() 2025-09-07T08:19:17.1419791Z 2025-09-07T08:19:17.1419982Z Furthermore, a more practical example that is combined with 2025-09-07T08:19:17.1420480Z `DistributedDataParallel `__ (DDP) 2025-09-07T08:19:17.1420810Z can be found in this `tutorial `__. 2025-09-07T08:19:17.1420815Z 2025-09-07T08:19:17.1421065Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.1421082Z 2025-09-07T08:19:17.1582363Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-09-07T08:19:17.1582638Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.1582655Z 2025-09-07T08:19:17.1582897Z DistributedOptimizer takes remote references to parameters scattered 2025-09-07T08:19:17.1583132Z across workers and applies the given optimizer locally for each parameter. 2025-09-07T08:19:17.1583137Z 2025-09-07T08:19:17.1583381Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-09-07T08:19:17.1583534Z to retrieve the gradients for specific parameters. 2025-09-07T08:19:17.1583539Z 2025-09-07T08:19:17.1583642Z Concurrent calls to 2025-09-07T08:19:17.1583844Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-09-07T08:19:17.1583984Z either from the same or different clients, will 2025-09-07T08:19:17.1584332Z be serialized on each worker -- as each worker's optimizer can only work 2025-09-07T08:19:17.1584543Z on one set of gradients at a time. However, there is no guarantee that 2025-09-07T08:19:17.1584807Z the full forward-backward-optimizer sequence will execute for one client 2025-09-07T08:19:17.1585023Z at a time. This means that the gradients being applied may not correspond 2025-09-07T08:19:17.1585246Z to the latest forward pass executed on a given worker. Also, there is no 2025-09-07T08:19:17.1585375Z guaranteed ordering across workers. 2025-09-07T08:19:17.1585379Z 2025-09-07T08:19:17.1585640Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-09-07T08:19:17.1585881Z by default, so that optimizer updates are not blocked by the Python Global 2025-09-07T08:19:17.1586124Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-09-07T08:19:17.1586377Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-09-07T08:19:17.1586628Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-09-07T08:19:17.1586753Z for your own custom optimizers. 2025-09-07T08:19:17.1586809Z 2025-09-07T08:19:17.1586891Z Args: 2025-09-07T08:19:17.1587089Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-09-07T08:19:17.1587210Z instantiate on each worker. 2025-09-07T08:19:17.1587422Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-09-07T08:19:17.1587510Z to optimize. 2025-09-07T08:19:17.1587736Z args: arguments to pass to the optimizer constructor on each worker. 2025-09-07T08:19:17.1587955Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-09-07T08:19:17.1587959Z 2025-09-07T08:19:17.1588069Z Example:: 2025-09-07T08:19:17.1588182Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:17.1588357Z >>> import torch.distributed.autograd as dist_autograd 2025-09-07T08:19:17.1588481Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.1588581Z >>> from torch import optim 2025-09-07T08:19:17.1588856Z >>> from torch.distributed.optim import DistributedOptimizer 2025-09-07T08:19:17.1588939Z >>> 2025-09-07T08:19:17.1589071Z >>> with dist_autograd.context() as context_id: 2025-09-07T08:19:17.1589178Z >>> # Forward pass. 2025-09-07T08:19:17.1589381Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-09-07T08:19:17.1589589Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-09-07T08:19:17.1589709Z >>> loss = rref1.to_here() + rref2.to_here() 2025-09-07T08:19:17.1589791Z >>> 2025-09-07T08:19:17.1589901Z >>> # Backward pass. 2025-09-07T08:19:17.1590046Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-09-07T08:19:17.1590141Z >>> 2025-09-07T08:19:17.1590230Z >>> # Optimizer. 2025-09-07T08:19:17.1590351Z >>> dist_optim = DistributedOptimizer( 2025-09-07T08:19:17.1590455Z >>> optim.SGD, 2025-09-07T08:19:17.1590549Z >>> [rref1, rref2], 2025-09-07T08:19:17.1590648Z >>> lr=0.05, 2025-09-07T08:19:17.1590733Z >>> ) 2025-09-07T08:19:17.1590844Z >>> dist_optim.step(context_id) 2025-09-07T08:19:17.1590848Z 2025-09-07T08:19:17.1591016Z __ https://github.com/pytorch/tutorials/pull/1465 2025-09-07T08:19:17.1591021Z 2025-09-07T08:19:17.1591270Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.1591274Z 2025-09-07T08:19:17.1601011Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2025-09-07T08:19:17.1601276Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.1601281Z 2025-09-07T08:19:17.1601683Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-09-07T08:19:17.1601899Z This optimizer runs local optimizer at every step. 2025-09-07T08:19:17.1602244Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-09-07T08:19:17.1602269Z 2025-09-07T08:19:17.1602347Z Args: 2025-09-07T08:19:17.1602450Z optim: The local optimizer. 2025-09-07T08:19:17.1602697Z averager: A model averager instance to run post-localSGD algorithm. 2025-09-07T08:19:17.1602701Z 2025-09-07T08:19:17.1602791Z Example:: 2025-09-07T08:19:17.1602795Z 2025-09-07T08:19:17.1602933Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:17.1603021Z >>> import torch 2025-09-07T08:19:17.1603144Z >>> import torch.distributed as dist 2025-09-07T08:19:17.1603414Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T08:19:17.1603513Z >>> import torch.nn as nn 2025-09-07T08:19:17.1603716Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-09-07T08:19:17.1603985Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T08:19:17.1604175Z >>> PostLocalSGDState, 2025-09-07T08:19:17.1604277Z >>> post_localSGD_hook, 2025-09-07T08:19:17.1604393Z >>> ) 2025-09-07T08:19:17.1604485Z >>> 2025-09-07T08:19:17.1604638Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T08:19:17.1604781Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T08:19:17.1604877Z >>> ) 2025-09-07T08:19:17.1604956Z >>> 2025-09-07T08:19:17.1605113Z >>> # Register a post-localSGD communication hook. 2025-09-07T08:19:17.1605410Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T08:19:17.1605570Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T08:19:17.1605664Z >>> 2025-09-07T08:19:17.1605864Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-09-07T08:19:17.1606122Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-09-07T08:19:17.1606287Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T08:19:17.1606569Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-09-07T08:19:17.1606698Z >>> opt = PostLocalSGDOptimizer( 2025-09-07T08:19:17.1606795Z >>> optim=local_optim, 2025-09-07T08:19:17.1607053Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T08:19:17.1607133Z >>> ) 2025-09-07T08:19:17.1607212Z >>> 2025-09-07T08:19:17.1607446Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-09-07T08:19:17.1607745Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-09-07T08:19:17.1608132Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-09-07T08:19:17.1608233Z >>> for step in range(0, 200): 2025-09-07T08:19:17.1608331Z >>> opt.zero_grad() 2025-09-07T08:19:17.1608453Z >>> loss = loss_fn(output, labels) 2025-09-07T08:19:17.1608547Z >>> loss.backward() 2025-09-07T08:19:17.1608653Z >>> opt.step() 2025-09-07T08:19:17.1608660Z 2025-09-07T08:19:17.1608913Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.1608917Z 2025-09-07T08:19:17.1717947Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=284. 2025-09-07T08:19:17.1718234Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.1718240Z 2025-09-07T08:19:17.1718645Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-09-07T08:19:17.1718666Z 2025-09-07T08:19:17.1718918Z The sharing is done as described by `ZeRO `_. 2025-09-07T08:19:17.1719023Z 2025-09-07T08:19:17.1719171Z The local optimizer instance in each rank is only 2025-09-07T08:19:17.1719439Z responsible for updating approximately ``1 / world_size`` parameters and 2025-09-07T08:19:17.1719681Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-09-07T08:19:17.1719926Z parameters are updated locally, each rank will broadcast its parameters to 2025-09-07T08:19:17.1720109Z all other peers to keep all model replicas in the same state. 2025-09-07T08:19:17.1720314Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-09-07T08:19:17.1720570Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-09-07T08:19:17.1720677Z memory consumption. 2025-09-07T08:19:17.1720682Z 2025-09-07T08:19:17.1720938Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-09-07T08:19:17.1721166Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-09-07T08:19:17.1721417Z not divided among ranks. The partition is arbitrary and might not match the 2025-09-07T08:19:17.1721544Z the parameter registration or usage order. 2025-09-07T08:19:17.1721548Z 2025-09-07T08:19:17.1721651Z Arguments: 2025-09-07T08:19:17.1721882Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-09-07T08:19:17.1722065Z or :class:`dict` s giving all parameters, which will be sharded 2025-09-07T08:19:17.1722166Z across ranks. 2025-09-07T08:19:17.1722170Z 2025-09-07T08:19:17.1722255Z Keyword Args: 2025-09-07T08:19:17.1722485Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-09-07T08:19:17.1722571Z optimizer. 2025-09-07T08:19:17.1722780Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-09-07T08:19:17.1722986Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-09-07T08:19:17.1723133Z :meth:`torch.distributed.init_process_group`). 2025-09-07T08:19:17.1723370Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-09-07T08:19:17.1723582Z packed into buckets to speed up communication, and ``param.data`` 2025-09-07T08:19:17.1723849Z fields point to bucket views at different offsets; if ``False``, 2025-09-07T08:19:17.1724066Z each individual parameter is communicated separately, and each 2025-09-07T08:19:17.1724344Z ``params.data`` stays intact (default: ``False``). 2025-09-07T08:19:17.1724547Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-09-07T08:19:17.1724743Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-09-07T08:19:17.1724955Z synchronization; this requires (1) either a functional optimizer 2025-09-07T08:19:17.1725147Z for the ``optimizer_class`` argument or one with a functional 2025-09-07T08:19:17.1725320Z equivalent and (2) registering a DDP communication hook 2025-09-07T08:19:17.1725531Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-09-07T08:19:17.1725697Z parameters are packed into buckets matching those in 2025-09-07T08:19:17.1725863Z :class:`DistributedDataParallel`, meaning that the 2025-09-07T08:19:17.1726011Z ``parameters_as_bucket_view`` argument is ignored. 2025-09-07T08:19:17.1726196Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-09-07T08:19:17.1726297Z (per normal). 2025-09-07T08:19:17.1726392Z (default: ``False``) 2025-09-07T08:19:17.1726599Z **defaults: any trailing arguments, which are forwarded to the local 2025-09-07T08:19:17.1726697Z optimizer. 2025-09-07T08:19:17.1726701Z 2025-09-07T08:19:17.1726791Z Example:: 2025-09-07T08:19:17.1726795Z 2025-09-07T08:19:17.1726900Z >>> # xdoctest: +SKIP 2025-09-07T08:19:17.1726998Z >>> import torch.nn as nn 2025-09-07T08:19:17.1727206Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-09-07T08:19:17.1727400Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T08:19:17.1727660Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-09-07T08:19:17.1727790Z >>> ddp = DDP(model, device_ids=[rank]) 2025-09-07T08:19:17.1727907Z >>> opt = ZeroRedundancyOptimizer( 2025-09-07T08:19:17.1728017Z >>> ddp.parameters(), 2025-09-07T08:19:17.1728136Z >>> optimizer_class=torch.optim.Adam, 2025-09-07T08:19:17.1728218Z >>> lr=0.01 2025-09-07T08:19:17.1728311Z >>> ) 2025-09-07T08:19:17.1728415Z >>> ddp(inputs).sum().backward() 2025-09-07T08:19:17.1728502Z >>> opt.step() 2025-09-07T08:19:17.1728518Z 2025-09-07T08:19:17.1728603Z .. warning:: 2025-09-07T08:19:17.1728807Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-09-07T08:19:17.1728959Z passed-in parameters are the same dense type. 2025-09-07T08:19:17.1728964Z 2025-09-07T08:19:17.1729048Z .. warning:: 2025-09-07T08:19:17.1729254Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-09-07T08:19:17.1729466Z the way that overlapping :class:`DistributedDataParallel` with 2025-09-07T08:19:17.1729697Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-09-07T08:19:17.1729951Z two or three training iterations do not perform parameter updates in 2025-09-07T08:19:17.1730142Z the optimizer step, depending on if ``static_graph=False`` or 2025-09-07T08:19:17.1730340Z ``static_graph=True``, respectively. This is because it needs 2025-09-07T08:19:17.1730521Z information about the gradient bucketing strategy used by 2025-09-07T08:19:17.1730736Z :class:`DistributedDataParallel`, which is not finalized until the 2025-09-07T08:19:17.1730945Z second forward pass if ``static_graph=False`` or until the third 2025-09-07T08:19:17.1731149Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-09-07T08:19:17.1731264Z is to prepend dummy inputs. 2025-09-07T08:19:17.1731269Z 2025-09-07T08:19:17.1731520Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-09-07T08:19:17.1731526Z 2025-09-07T08:19:17.1732142Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.1732162Z 2025-09-07T08:19:17.1957573Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-09-07T08:19:17.1957839Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.1957845Z 2025-09-07T08:19:17.1958084Z Custom reducer class that can be used to specify a custom operation that 2025-09-07T08:19:17.1958254Z reduces losses of multiple microbatches into one value. 2025-09-07T08:19:17.1958258Z 2025-09-07T08:19:17.1958355Z Example: 2025-09-07T08:19:17.1958447Z >>> # xdoctest: +SKIP 2025-09-07T08:19:17.1958554Z >>> sum_reducer = _CustomReducer( 2025-09-07T08:19:17.1958663Z >>> torch.tensor(0.0), 2025-09-07T08:19:17.1958757Z >>> lambda a, b: a + b 2025-09-07T08:19:17.1958855Z >>> ) 2025-09-07T08:19:17.1958859Z 2025-09-07T08:19:17.1959109Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.1959121Z 2025-09-07T08:19:17.2491861Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6. 2025-09-07T08:19:17.2492256Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.2492265Z 2025-09-07T08:19:17.2492679Z A decorator for a function indicating that the return value of the function 2025-09-07T08:19:17.2493024Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-09-07T08:19:17.2493374Z function can run asynchronously on the RPC callee. More specifically, the 2025-09-07T08:19:17.2493720Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-09-07T08:19:17.2494107Z function and installs subsequent processing steps as a callback to that 2025-09-07T08:19:17.2494732Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-09-07T08:19:17.2495118Z from the :class:`~torch.futures.Future` when completed and send the 2025-09-07T08:19:17.2495476Z value back as the RPC response. That also means the returned 2025-09-07T08:19:17.2495749Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-09-07T08:19:17.2495972Z sent through RPC. This decorator is useful when the wrapped function's 2025-09-07T08:19:17.2496182Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-09-07T08:19:17.2496406Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-09-07T08:19:17.2496411Z 2025-09-07T08:19:17.2496652Z .. note:: To enable asynchronous execution, applications must pass the 2025-09-07T08:19:17.2496881Z function object returned by this decorator to RPC APIs. If RPC detected 2025-09-07T08:19:17.2497101Z attributes installed by this decorator, it knows that this function 2025-09-07T08:19:17.2497298Z returns a ``Future`` object and will handle that accordingly. 2025-09-07T08:19:17.2497514Z However, this does not mean this decorator has to be outmost one when 2025-09-07T08:19:17.2497816Z defining a function. For example, when combined with ``@staticmethod`` 2025-09-07T08:19:17.2498024Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-09-07T08:19:17.2498240Z inner decorator to allow the target function be recognized as a static 2025-09-07T08:19:17.2498522Z or class function. This target function can still execute asynchronously 2025-09-07T08:19:17.2498758Z because, when accessed, the static or class method preserves attributes 2025-09-07T08:19:17.2498908Z installed by ``@rpc.functions.async_execution``. 2025-09-07T08:19:17.2498913Z 2025-09-07T08:19:17.2498918Z 2025-09-07T08:19:17.2499018Z Example:: 2025-09-07T08:19:17.2499216Z The returned :class:`~torch.futures.Future` object can come from 2025-09-07T08:19:17.2499348Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-09-07T08:19:17.2499581Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-09-07T08:19:17.2499845Z constructor. The example below shows directly using the 2025-09-07T08:19:17.2499990Z :class:`~torch.futures.Future` returned by 2025-09-07T08:19:17.2500107Z :meth:`~torch.futures.Future.then`. 2025-09-07T08:19:17.2500112Z 2025-09-07T08:19:17.2500228Z >>> from torch.distributed import rpc 2025-09-07T08:19:17.2500323Z >>> 2025-09-07T08:19:17.2500436Z >>> # omitting setup and shutdown RPC 2025-09-07T08:19:17.2500528Z >>> 2025-09-07T08:19:17.2500620Z >>> # On all workers 2025-09-07T08:19:17.2500734Z >>> @rpc.functions.async_execution 2025-09-07T08:19:17.2500858Z >>> def async_add_chained(to, x, y, z): 2025-09-07T08:19:17.2501047Z >>> # This function runs on "worker1" and returns immediately when 2025-09-07T08:19:17.2501246Z >>> # the callback is installed through the `then(cb)` API. In the 2025-09-07T08:19:17.2501428Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-09-07T08:19:17.2501590Z >>> # When the return value of that `rpc_async` arrives at 2025-09-07T08:19:17.2501788Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-09-07T08:19:17.2501971Z >>> # and set the value for the previously returned `Future`, which 2025-09-07T08:19:17.2502163Z >>> # will then trigger RPC to send the result back to "worker0". 2025-09-07T08:19:17.2502329Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T08:19:17.2502437Z >>> lambda fut: fut.wait() + z 2025-09-07T08:19:17.2502530Z >>> ) 2025-09-07T08:19:17.2502611Z >>> 2025-09-07T08:19:17.2502710Z >>> # On worker0 2025-09-07T08:19:17.2502804Z >>> # xdoctest: +SKIP 2025-09-07T08:19:17.2502900Z >>> ret = rpc.rpc_sync( 2025-09-07T08:19:17.2502996Z >>> "worker1", 2025-09-07T08:19:17.2503120Z >>> async_add_chained, 2025-09-07T08:19:17.2503252Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-09-07T08:19:17.2503333Z >>> ) 2025-09-07T08:19:17.2503453Z >>> print(ret) # prints tensor([3., 3.]) 2025-09-07T08:19:17.2503460Z 2025-09-07T08:19:17.2503690Z When combined with TorchScript decorators, this decorator must be the 2025-09-07T08:19:17.2503777Z outmost one. 2025-09-07T08:19:17.2503781Z 2025-09-07T08:19:17.2503896Z >>> from torch import Tensor 2025-09-07T08:19:17.2504007Z >>> from torch.futures import Future 2025-09-07T08:19:17.2504120Z >>> from torch.distributed import rpc 2025-09-07T08:19:17.2504213Z >>> 2025-09-07T08:19:17.2504321Z >>> # omitting setup and shutdown RPC 2025-09-07T08:19:17.2504414Z >>> 2025-09-07T08:19:17.2504503Z >>> # On all workers 2025-09-07T08:19:17.2504598Z >>> @torch.jit.script 2025-09-07T08:19:17.2504754Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T08:19:17.2504842Z >>> return x + y 2025-09-07T08:19:17.2504924Z >>> 2025-09-07T08:19:17.2505047Z >>> @rpc.functions.async_execution 2025-09-07T08:19:17.2505140Z >>> @torch.jit.script 2025-09-07T08:19:17.2505342Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-09-07T08:19:17.2505517Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-09-07T08:19:17.2505597Z >>> 2025-09-07T08:19:17.2505693Z >>> # On worker0 2025-09-07T08:19:17.2505787Z >>> ret = rpc.rpc_sync( 2025-09-07T08:19:17.2505885Z >>> "worker1", 2025-09-07T08:19:17.2505971Z >>> async_add, 2025-09-07T08:19:17.2506084Z >>> args=("worker2", torch.ones(2), 1) 2025-09-07T08:19:17.2506174Z >>> ) 2025-09-07T08:19:17.2506288Z >>> print(ret) # prints tensor([2., 2.]) 2025-09-07T08:19:17.2506292Z 2025-09-07T08:19:17.2506518Z When combined with static or class method, this decorator must be the 2025-09-07T08:19:17.2506601Z inner one. 2025-09-07T08:19:17.2506605Z 2025-09-07T08:19:17.2506721Z >>> from torch.distributed import rpc 2025-09-07T08:19:17.2506812Z >>> 2025-09-07T08:19:17.2506921Z >>> # omitting setup and shutdown RPC 2025-09-07T08:19:17.2507087Z >>> 2025-09-07T08:19:17.2507177Z >>> # On all workers 2025-09-07T08:19:17.2507285Z >>> class AsyncExecutionClass: 2025-09-07T08:19:17.2507379Z >>> 2025-09-07T08:19:17.2507469Z >>> @staticmethod 2025-09-07T08:19:17.2507581Z >>> @rpc.functions.async_execution 2025-09-07T08:19:17.2507706Z >>> def static_async_add(to, x, y, z): 2025-09-07T08:19:17.2507873Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T08:19:17.2507996Z >>> lambda fut: fut.wait() + z 2025-09-07T08:19:17.2508079Z >>> ) 2025-09-07T08:19:17.2508157Z >>> 2025-09-07T08:19:17.2508258Z >>> @classmethod 2025-09-07T08:19:17.2508369Z >>> @rpc.functions.async_execution 2025-09-07T08:19:17.2508499Z >>> def class_async_add(cls, to, x, y, z): 2025-09-07T08:19:17.2508618Z >>> ret_fut = torch.futures.Future() 2025-09-07T08:19:17.2508762Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T08:19:17.2508924Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-09-07T08:19:17.2509008Z >>> ) 2025-09-07T08:19:17.2509115Z >>> return ret_fut 2025-09-07T08:19:17.2509195Z >>> 2025-09-07T08:19:17.2509307Z >>> @rpc.functions.async_execution 2025-09-07T08:19:17.2509453Z >>> def bound_async_add(self, to, x, y, z): 2025-09-07T08:19:17.2509619Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T08:19:17.2509730Z >>> lambda fut: fut.wait() + z 2025-09-07T08:19:17.2509824Z >>> ) 2025-09-07T08:19:17.2509905Z >>> 2025-09-07T08:19:17.2510005Z >>> # On worker0 2025-09-07T08:19:17.2510101Z >>> ret = rpc.rpc_sync( 2025-09-07T08:19:17.2510189Z >>> "worker1", 2025-09-07T08:19:17.2510333Z >>> AsyncExecutionClass.static_async_add, 2025-09-07T08:19:17.2510475Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T08:19:17.2510570Z >>> ) 2025-09-07T08:19:17.2510689Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:17.2510771Z >>> 2025-09-07T08:19:17.2510881Z >>> ret = rpc.rpc_sync( 2025-09-07T08:19:17.2510969Z >>> "worker1", 2025-09-07T08:19:17.2511098Z >>> AsyncExecutionClass.class_async_add, 2025-09-07T08:19:17.2511227Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T08:19:17.2511311Z >>> ) 2025-09-07T08:19:17.2511438Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:17.2511443Z 2025-09-07T08:19:17.2511605Z This decorator also works with RRef helpers, i.e., . 2025-09-07T08:19:17.2511745Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-09-07T08:19:17.2511914Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-09-07T08:19:17.2512052Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-09-07T08:19:17.2512056Z 2025-09-07T08:19:17.2512190Z >>> from torch.distributed import rpc 2025-09-07T08:19:17.2512273Z >>> 2025-09-07T08:19:17.2512409Z >>> # reuse the AsyncExecutionClass class above 2025-09-07T08:19:17.2512604Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T08:19:17.2512813Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-09-07T08:19:17.2512943Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:17.2513024Z >>> 2025-09-07T08:19:17.2513170Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T08:19:17.2513420Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-09-07T08:19:17.2513535Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:17.2513632Z >>> 2025-09-07T08:19:17.2513779Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T08:19:17.2514009Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-09-07T08:19:17.2514140Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:17.2514144Z 2025-09-07T08:19:17.2514451Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.2514458Z 2025-09-07T08:19:17.2544900Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=113. 2025-09-07T08:19:17.2545167Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.2545173Z 2025-09-07T08:19:17.2545387Z Set device mapping between each RPC caller and callee pair. This 2025-09-07T08:19:17.2545566Z function can be called multiple times to incrementally add 2025-09-07T08:19:17.2545690Z device placement configurations. 2025-09-07T08:19:17.2545695Z 2025-09-07T08:19:17.2545774Z Args: 2025-09-07T08:19:17.2545871Z to (str): Callee name. 2025-09-07T08:19:17.2546078Z device_map (Dict of int, str, or torch.device): Device placement 2025-09-07T08:19:17.2546279Z mappings from this worker to the callee. This map must be 2025-09-07T08:19:17.2546377Z invertible. 2025-09-07T08:19:17.2546387Z 2025-09-07T08:19:17.2546472Z Example: 2025-09-07T08:19:17.2546582Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:17.2546678Z >>> # both workers 2025-09-07T08:19:17.2546767Z >>> def add(x, y): 2025-09-07T08:19:17.2546909Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-09-07T08:19:17.2547010Z >>> return x + y, (x + y).to(2) 2025-09-07T08:19:17.2547093Z >>> 2025-09-07T08:19:17.2547190Z >>> # on worker 0 2025-09-07T08:19:17.2547324Z >>> options = TensorPipeRpcBackendOptions( 2025-09-07T08:19:17.2547433Z >>> num_worker_threads=8, 2025-09-07T08:19:17.2547542Z >>> device_maps={"worker1": {0: 1}} 2025-09-07T08:19:17.2547667Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-09-07T08:19:17.2547758Z >>> ) 2025-09-07T08:19:17.2547883Z >>> options.set_device_map("worker1", {1: 2}) 2025-09-07T08:19:17.2548086Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-09-07T08:19:17.2548168Z >>> 2025-09-07T08:19:17.2548262Z >>> rpc.init_rpc( 2025-09-07T08:19:17.2548364Z >>> "worker0", 2025-09-07T08:19:17.2548446Z >>> rank=0, 2025-09-07T08:19:17.2548536Z >>> world_size=2, 2025-09-07T08:19:17.2548677Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-09-07T08:19:17.2548783Z >>> rpc_backend_options=options 2025-09-07T08:19:17.2548880Z >>> ) 2025-09-07T08:19:17.2548960Z >>> 2025-09-07T08:19:17.2549052Z >>> x = torch.ones(2) 2025-09-07T08:19:17.2549221Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-09-07T08:19:17.2549404Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-09-07T08:19:17.2549597Z >>> # sending the return value back, it will follow the invert of 2025-09-07T08:19:17.2549766Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-09-07T08:19:17.2549861Z >>> # cuda:1 on worker0 2025-09-07T08:19:17.2550017Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-09-07T08:19:17.2550162Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-09-07T08:19:17.2550199Z 2025-09-07T08:19:17.2550460Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.2550465Z 2025-09-07T08:19:17.2578491Z msg = Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19. 2025-09-07T08:19:17.2578786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.2578817Z 2025-09-07T08:19:17.2579015Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-09-07T08:19:17.2579306Z except that it enables profiling on all threads running RPC server request callbacks. 2025-09-07T08:19:17.2579311Z 2025-09-07T08:19:17.2579587Z Context manager that manages autograd profiler state and holds a summary of results. 2025-09-07T08:19:17.2579981Z Under the hood it just records events of functions being executed in C++ and 2025-09-07T08:19:17.2580218Z exposes those events to Python. You can wrap any code into it and it will 2025-09-07T08:19:17.2580342Z only report runtime of PyTorch functions. 2025-09-07T08:19:17.2580624Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-09-07T08:19:17.2580629Z 2025-09-07T08:19:17.2580712Z Args: 2025-09-07T08:19:17.2580994Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-09-07T08:19:17.2581091Z Default: ``True``. 2025-09-07T08:19:17.2581096Z 2025-09-07T08:19:17.2581393Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-09-07T08:19:17.2581590Z Adds approximately 4us of overhead to each tensor operation. 2025-09-07T08:19:17.2581686Z Default: ``False`` 2025-09-07T08:19:17.2581692Z 2025-09-07T08:19:17.2581932Z record_shapes (bool, optional): If shapes recording is set, information 2025-09-07T08:19:17.2582165Z about input dimensions will be collected. This allows one to see which 2025-09-07T08:19:17.2582390Z dimensions have been used under the hood and further group by them 2025-09-07T08:19:17.2582605Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-09-07T08:19:17.2582829Z shape recording might skew your profiling data. It is recommended to 2025-09-07T08:19:17.2583074Z use separate runs with and without shape recording to validate the timing. 2025-09-07T08:19:17.2583297Z Most likely the skew will be negligible for bottom most events (in a case 2025-09-07T08:19:17.2583517Z of nested function calls). But for higher level functions the total 2025-09-07T08:19:17.2583717Z self cpu time might be artificially increased because of the shape 2025-09-07T08:19:17.2583804Z collection. 2025-09-07T08:19:17.2583858Z 2025-09-07T08:19:17.2584120Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-09-07T08:19:17.2584129Z 2025-09-07T08:19:17.2584225Z .. warning:: 2025-09-07T08:19:17.2584434Z Enabling memory profiling incurs additional profiler overhead 2025-09-07T08:19:17.2584439Z 2025-09-07T08:19:17.2584525Z .. warning:: 2025-09-07T08:19:17.2584825Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-09-07T08:19:17.2585020Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-09-07T08:19:17.2585253Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-09-07T08:19:17.2585427Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-09-07T08:19:17.2585431Z 2025-09-07T08:19:17.2585516Z Example: 2025-09-07T08:19:17.2585622Z >>> # xdoctest: +SKIP 2025-09-07T08:19:17.2585708Z >>> # On worker 0: 2025-09-07T08:19:17.2585794Z >>> import torch 2025-09-07T08:19:17.2585928Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.2586067Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T08:19:17.2586235Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-09-07T08:19:17.2586344Z >>> outer_profile_rref = rpc.remote( 2025-09-07T08:19:17.2586503Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T08:19:17.2586595Z ... ) 2025-09-07T08:19:17.2586720Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-09-07T08:19:17.2586876Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-09-07T08:19:17.2586986Z >>> inner_profile_rref = rpc.remote( 2025-09-07T08:19:17.2587142Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T08:19:17.2587235Z ... ) 2025-09-07T08:19:17.2587359Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-09-07T08:19:17.2587516Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-09-07T08:19:17.2587687Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T08:19:17.2587857Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T08:19:17.2588097Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-09-07T08:19:17.2588335Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:17.2588654Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T08:19:17.2588880Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:17.2589067Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-09-07T08:19:17.2589265Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-09-07T08:19:17.2589487Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:17.2589609Z Self CPU time total: 89.667us 2025-09-07T08:19:17.2589769Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-09-07T08:19:17.2590005Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:17.2590305Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T08:19:17.2590524Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:17.2590713Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-09-07T08:19:17.2590896Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-09-07T08:19:17.2591086Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-09-07T08:19:17.2591335Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:17.2591442Z Self CPU time total: 213.926us 2025-09-07T08:19:17.2591547Z >>> rpc.shutdown() 2025-09-07T08:19:17.2591552Z 2025-09-07T08:19:17.2591639Z >>> # On worker 1: 2025-09-07T08:19:17.2591775Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:17.2591911Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T08:19:17.2592067Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-09-07T08:19:17.2592171Z >>> rpc.shutdown() 2025-09-07T08:19:17.2592175Z 2025-09-07T08:19:17.2592429Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.2592434Z 2025-09-07T08:19:17.3857697Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=35. 2025-09-07T08:19:17.3858028Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.3858034Z 2025-09-07T08:19:17.3858309Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-09-07T08:19:17.3858756Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-09-07T08:19:17.3859034Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-09-07T08:19:17.3859195Z :class:`DTensor` according to the ``out_placements``. 2025-09-07T08:19:17.3859200Z 2025-09-07T08:19:17.3859293Z Args: 2025-09-07T08:19:17.3859497Z func (Callable): the function to be applied on each local shard of 2025-09-07T08:19:17.3859595Z :class:`DTensor` s. 2025-09-07T08:19:17.3859832Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-09-07T08:19:17.3860084Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-09-07T08:19:17.3860334Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-09-07T08:19:17.3860667Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-09-07T08:19:17.3860926Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-09-07T08:19:17.3861046Z mapping to the flattened ``output``. 2025-09-07T08:19:17.3861246Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-09-07T08:19:17.3861531Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-09-07T08:19:17.3861627Z should be `None`. 2025-09-07T08:19:17.3861876Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-09-07T08:19:17.3862095Z in. In this case, even if `out_placements` is not `None`, the result function 2025-09-07T08:19:17.3862348Z should ignore the desired placements because the function is not running with 2025-09-07T08:19:17.3862461Z :class:`DTensor` s. 2025-09-07T08:19:17.3862624Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T08:19:17.3862923Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-09-07T08:19:17.3863157Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-09-07T08:19:17.3863382Z placements of each :class:`DTensor` argument is the same as the required 2025-09-07T08:19:17.3863575Z placements or not. If the placements are not the same and 2025-09-07T08:19:17.3863817Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-09-07T08:19:17.3864069Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-09-07T08:19:17.3864327Z the required sharding placements before passing its local tensor to ``func``. 2025-09-07T08:19:17.3864549Z The only exception is when required placements are not ``None`` and the 2025-09-07T08:19:17.3864842Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-09-07T08:19:17.3865062Z will be skipped and the argument will be directly passed to ``func``. 2025-09-07T08:19:17.3865298Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-09-07T08:19:17.3865391Z Default: None 2025-09-07T08:19:17.3865587Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T08:19:17.3865794Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-09-07T08:19:17.3866005Z to the flattened input DTensor. This argument is the hint that user 2025-09-07T08:19:17.3866200Z can give to :meth:`to_local` in case the gradient layout of the 2025-09-07T08:19:17.3866415Z local tensor input does not match its :class:`DTensor` input layout. 2025-09-07T08:19:17.3866626Z If not specified, we will assume the gradient layout of the local 2025-09-07T08:19:17.3866844Z tensor input remains the same as the original :class:`DTensor` input 2025-09-07T08:19:17.3867012Z and use that for gradient computation. Default: None. 2025-09-07T08:19:17.3867189Z device_mesh (:class:`DeviceMesh`, optional): 2025-09-07T08:19:17.3867409Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-09-07T08:19:17.3867672Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-09-07T08:19:17.3867773Z mesh. Default: None. 2025-09-07T08:19:17.3867778Z 2025-09-07T08:19:17.3867866Z Keyword Args: 2025-09-07T08:19:17.3867999Z redistribute_inputs (bool, optional): 2025-09-07T08:19:17.3868251Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-09-07T08:19:17.3868513Z their placements are different from the required input placements. If this 2025-09-07T08:19:17.3868737Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-09-07T08:19:17.3868875Z an exception will be raised. Default: False. 2025-09-07T08:19:17.3868880Z 2025-09-07T08:19:17.3869034Z Returns: 2025-09-07T08:19:17.3869291Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-09-07T08:19:17.3869540Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-09-07T08:19:17.3869545Z 2025-09-07T08:19:17.3869630Z Raises: 2025-09-07T08:19:17.3869882Z AssertionError: For any non-DTensor output, we require its corresponding 2025-09-07T08:19:17.3870137Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-09-07T08:19:17.3870238Z if this is not the case. 2025-09-07T08:19:17.3870242Z 2025-09-07T08:19:17.3870514Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-09-07T08:19:17.3870664Z a redistribution according to ``in_placements``. 2025-09-07T08:19:17.3870668Z 2025-09-07T08:19:17.3870763Z Example: 2025-09-07T08:19:17.3870874Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:17.3871010Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-09-07T08:19:17.3871143Z >>> partial_sum_tensor = torch.mm(W, X) 2025-09-07T08:19:17.3871378Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-09-07T08:19:17.3871489Z >>> return reduced_tensor 2025-09-07T08:19:17.3871568Z >>> 2025-09-07T08:19:17.3871692Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-09-07T08:19:17.3871824Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-09-07T08:19:17.3871916Z >>> Y = torch.mm(W, X) 2025-09-07T08:19:17.3872112Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-09-07T08:19:17.3872293Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-09-07T08:19:17.3872372Z >>> 2025-09-07T08:19:17.3872650Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-09-07T08:19:17.3872801Z >>> local_mm_allreduce_forward = local_map( 2025-09-07T08:19:17.3872915Z >>> mm_allreduce_forward, 2025-09-07T08:19:17.3873029Z >>> out_placements=[Replicate()], 2025-09-07T08:19:17.3873146Z >>> in_placements=[col_wise, row_wise], 2025-09-07T08:19:17.3873443Z >>> device_mesh=device_mesh, 2025-09-07T08:19:17.3873525Z >>> ) 2025-09-07T08:19:17.3873604Z >>> 2025-09-07T08:19:17.3873716Z >>> W_dt = distribute_tensor( 2025-09-07T08:19:17.3873818Z ... W, device_mesh, (col_wise) 2025-09-07T08:19:17.3873943Z ... ) # col-wisely sharded W tensor 2025-09-07T08:19:17.3874041Z >>> X_dt = distribute_tensor( 2025-09-07T08:19:17.3874141Z ... X, device_mesh, (row_wise) 2025-09-07T08:19:17.3874258Z ... ) # row-wisely sharded X tensor 2025-09-07T08:19:17.3874372Z >>> Y_dt = local_mm_allreduce_forward( 2025-09-07T08:19:17.3874485Z ... device_mesh, W_dt, X_dt 2025-09-07T08:19:17.3874631Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-09-07T08:19:17.3874636Z 2025-09-07T08:19:17.3874853Z .. note:: This API is currently experimental and subject to change 2025-09-07T08:19:17.3874916Z 2025-09-07T08:19:17.3875167Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.3875172Z 2025-09-07T08:19:17.3878991Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2025-09-07T08:19:17.3879546Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.3879557Z 2025-09-07T08:19:17.3880080Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-09-07T08:19:17.3880560Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-09-07T08:19:17.3881038Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-09-07T08:19:17.3881505Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-09-07T08:19:17.3882292Z when users would like to overwrite default sharding strategies of existing operators. 2025-09-07T08:19:17.3882309Z 2025-09-07T08:19:17.3882474Z Args: 2025-09-07T08:19:17.3882722Z op (Union[OpOverload, List[OpOverload]]): 2025-09-07T08:19:17.3883075Z An op or a list of ops to register the customized sharding function. 2025-09-07T08:19:17.3883084Z 2025-09-07T08:19:17.3883259Z Returns: 2025-09-07T08:19:17.3883755Z A function decorator which can be used to wrap a function that defines the sharding 2025-09-07T08:19:17.3884351Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-09-07T08:19:17.3884886Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-09-07T08:19:17.3885469Z already implemented the operator. The customized sharding function takes the same inputs 2025-09-07T08:19:17.3885944Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-09-07T08:19:17.3886471Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-09-07T08:19:17.3886997Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-09-07T08:19:17.3887213Z corresponding input placements. 2025-09-07T08:19:17.3887221Z 2025-09-07T08:19:17.3887362Z Example: 2025-09-07T08:19:17.3887585Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:17.3887822Z >>> @register_sharding(aten._softmax.default) 2025-09-07T08:19:17.3888101Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-09-07T08:19:17.3888350Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-09-07T08:19:17.3888545Z >>> acceptable_shardings = [] 2025-09-07T08:19:17.3888706Z >>> 2025-09-07T08:19:17.3889035Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-09-07T08:19:17.3889444Z >>> acceptable_shardings.append(all_replicate) 2025-09-07T08:19:17.3889599Z >>> 2025-09-07T08:19:17.3889808Z >>> for sharding_dim in range(x.ndim): 2025-09-07T08:19:17.3890024Z >>> if sharding_dim != softmax_dim: 2025-09-07T08:19:17.3890210Z >>> all_sharded = ( 2025-09-07T08:19:17.3890421Z >>> [Shard(sharding_dim)], 2025-09-07T08:19:17.3890643Z >>> [Shard(sharding_dim), None, None], 2025-09-07T08:19:17.3890789Z >>> ) 2025-09-07T08:19:17.3891040Z >>> acceptable_shardings.append(all_sharded) 2025-09-07T08:19:17.3891193Z >>> 2025-09-07T08:19:17.3891414Z >>> return acceptable_shardings 2025-09-07T08:19:17.3891422Z 2025-09-07T08:19:17.3891788Z .. note:: This API is currently experimental and subject to change 2025-09-07T08:19:17.3891800Z 2025-09-07T08:19:17.3892273Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.3892306Z 2025-09-07T08:19:17.4132393Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=428. 2025-09-07T08:19:17.4133594Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.4133988Z 2025-09-07T08:19:17.4134367Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-09-07T08:19:17.4135195Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-09-07T08:19:17.4135627Z 2025-09-07T08:19:17.4135727Z Keyword Args: 2025-09-07T08:19:17.4136065Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T08:19:17.4136928Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T08:19:17.4137869Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T08:19:17.4138472Z as a placeholder. default: None. 2025-09-07T08:19:17.4139051Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T08:19:17.4139787Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T08:19:17.4140678Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T08:19:17.4141312Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T08:19:17.4141937Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T08:19:17.4142532Z default: None 2025-09-07T08:19:17.4142855Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T08:19:17.4143501Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T08:19:17.4144132Z have the desired DTensor layouts. default: None. 2025-09-07T08:19:17.4144653Z use_local_output (bool, optional): 2025-09-07T08:19:17.4145355Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T08:19:17.4146127Z Returns: 2025-09-07T08:19:17.4146614Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-09-07T08:19:17.4147128Z 2025-09-07T08:19:17.4147230Z Example:: 2025-09-07T08:19:17.4147462Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:17.4148049Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-09-07T08:19:17.4148730Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T08:19:17.4149129Z >>> ... 2025-09-07T08:19:17.4149628Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T08:19:17.4150329Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T08:19:17.4150657Z >>> 2025-09-07T08:19:17.4151177Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-09-07T08:19:17.4151851Z >>> # and then redistributed to Replicated DTensor. 2025-09-07T08:19:17.4152222Z >>> parallelize_module( 2025-09-07T08:19:17.4152606Z >>> block, # this can be a submodule or module 2025-09-07T08:19:17.4152936Z >>> tp_mesh, 2025-09-07T08:19:17.4153217Z >>> parallelize_plan={ 2025-09-07T08:19:17.4153572Z >>> "attn": PrepareModuleInput( 2025-09-07T08:19:17.4153936Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T08:19:17.4154411Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-09-07T08:19:17.4154829Z >>> ), 2025-09-07T08:19:17.4155076Z >>> } 2025-09-07T08:19:17.4155295Z >>> ) 2025-09-07T08:19:17.4155409Z 2025-09-07T08:19:17.4155728Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.4156108Z 2025-09-07T08:19:17.4156834Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=597. 2025-09-07T08:19:17.4158053Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.4158433Z 2025-09-07T08:19:17.4158899Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-09-07T08:19:17.4159810Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-09-07T08:19:17.4160311Z 2025-09-07T08:19:17.4160414Z Keyword Args: 2025-09-07T08:19:17.4160703Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T08:19:17.4161376Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T08:19:17.4162276Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T08:19:17.4163071Z ``None`` need to be specified as a placeholder. 2025-09-07T08:19:17.4163586Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T08:19:17.4164434Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T08:19:17.4165127Z have the desired DTensor layouts. 2025-09-07T08:19:17.4165471Z use_local_output (bool, optional): 2025-09-07T08:19:17.4166126Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T08:19:17.4166744Z Returns: 2025-09-07T08:19:17.4167162Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-09-07T08:19:17.4167638Z 2025-09-07T08:19:17.4167738Z Example:: 2025-09-07T08:19:17.4167966Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:17.4168537Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-09-07T08:19:17.4169240Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T08:19:17.4169652Z >>> ... 2025-09-07T08:19:17.4170121Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T08:19:17.4170729Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T08:19:17.4171055Z >>> 2025-09-07T08:19:17.4171639Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-09-07T08:19:17.4172365Z >>> # and then redistributed to Sharded DTensor. 2025-09-07T08:19:17.4172719Z >>> parallelize_module( 2025-09-07T08:19:17.4173088Z >>> block, # this can be a submodule or module 2025-09-07T08:19:17.4173636Z >>> tp_mesh, 2025-09-07T08:19:17.4173938Z >>> parallelize_plan = PrepareModuleOutput( 2025-09-07T08:19:17.4174451Z >>> output_layouts=Replicate(), 2025-09-07T08:19:17.4174795Z >>> desired_output_layouts=Shard(0) 2025-09-07T08:19:17.4175142Z >>> ) 2025-09-07T08:19:17.4175397Z >>> ) 2025-09-07T08:19:17.4175512Z 2025-09-07T08:19:17.4175774Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.4176194Z 2025-09-07T08:19:17.4176927Z msg = Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-09-07T08:19:17.4178046Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.4178442Z 2025-09-07T08:19:17.4178874Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-09-07T08:19:17.4179917Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-09-07T08:19:17.4180838Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-09-07T08:19:17.4181596Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-09-07T08:19:17.4181910Z 2025-09-07T08:19:17.4181997Z Keyword Args: 2025-09-07T08:19:17.4182332Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T08:19:17.4182975Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T08:19:17.4183845Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T08:19:17.4184436Z as a placeholder. default: None. 2025-09-07T08:19:17.4184879Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T08:19:17.4185604Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T08:19:17.4186575Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T08:19:17.4187225Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T08:19:17.4187845Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T08:19:17.4188427Z default: None 2025-09-07T08:19:17.4188746Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T08:19:17.4189386Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T08:19:17.4190025Z have the desired DTensor layouts. default: None. 2025-09-07T08:19:17.4190388Z use_local_input (bool, optional): 2025-09-07T08:19:17.4191026Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T08:19:17.4191677Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T08:19:17.4192299Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T08:19:17.4193141Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T08:19:17.4193766Z ``None`` need to be specified as a placeholder. 2025-09-07T08:19:17.4194220Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T08:19:17.4194919Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T08:19:17.4195542Z have the desired DTensor layouts. 2025-09-07T08:19:17.4195881Z use_local_output (bool, optional): 2025-09-07T08:19:17.4196452Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T08:19:17.4197018Z Returns: 2025-09-07T08:19:17.4197549Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-09-07T08:19:17.4198035Z 2025-09-07T08:19:17.4198137Z Example:: 2025-09-07T08:19:17.4198354Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:17.4198903Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-09-07T08:19:17.4199561Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T08:19:17.4199954Z >>> ... 2025-09-07T08:19:17.4200374Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T08:19:17.4200927Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T08:19:17.4201253Z >>> 2025-09-07T08:19:17.4201714Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-09-07T08:19:17.4202529Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-09-07T08:19:17.4203208Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-09-07T08:19:17.4203648Z >>> parallelize_module( 2025-09-07T08:19:17.4204071Z >>> block, # this can be a submodule or module 2025-09-07T08:19:17.4204500Z >>> tp_mesh, 2025-09-07T08:19:17.4204752Z >>> parallelize_plan={ 2025-09-07T08:19:17.4205084Z >>> "attn": PrepareModuleInputOutput( 2025-09-07T08:19:17.4205467Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T08:19:17.4205893Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-09-07T08:19:17.4206295Z >>> output_layouts=Replicate(), 2025-09-07T08:19:17.4206652Z >>> desired_output_layouts=Shard(0), 2025-09-07T08:19:17.4206972Z >>> ), 2025-09-07T08:19:17.4207195Z >>> } 2025-09-07T08:19:17.4207395Z >>> ) 2025-09-07T08:19:17.4207519Z 2025-09-07T08:19:17.4207771Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.4208147Z 2025-09-07T08:19:17.4755789Z msg = Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-09-07T08:19:17.4757067Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.4757466Z 2025-09-07T08:19:17.4757819Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-09-07T08:19:17.4758505Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-09-07T08:19:17.4758795Z 2025-09-07T08:19:17.4758982Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-09-07T08:19:17.4759330Z 2025-09-07T08:19:17.4759417Z Example: 2025-09-07T08:19:17.4759691Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T08:19:17.4760151Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:17.4760527Z >>> m = LowRankMultivariateNormal( 2025-09-07T08:19:17.4760986Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-09-07T08:19:17.4761422Z ... ) 2025-09-07T08:19:17.4761856Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-09-07T08:19:17.4762433Z tensor([-0.2102, -0.5429]) 2025-09-07T08:19:17.4762617Z 2025-09-07T08:19:17.4762698Z Args: 2025-09-07T08:19:17.4763119Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-09-07T08:19:17.4763785Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-09-07T08:19:17.4764357Z `batch_shape + event_shape + (rank,)` 2025-09-07T08:19:17.4764915Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-09-07T08:19:17.4765454Z `batch_shape + event_shape` 2025-09-07T08:19:17.4765670Z 2025-09-07T08:19:17.4765750Z Note: 2025-09-07T08:19:17.4766282Z The computation for determinant and inverse of covariance matrix is avoided when 2025-09-07T08:19:17.4766987Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-09-07T08:19:17.4767560Z `_ and 2025-09-07T08:19:17.4768250Z `matrix determinant lemma `_. 2025-09-07T08:19:17.4768995Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-09-07T08:19:17.4769568Z the small size "capacitance" matrix:: 2025-09-07T08:19:17.4769792Z 2025-09-07T08:19:17.4770007Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-09-07T08:19:17.4770344Z 2025-09-07T08:19:17.4770594Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.4771039Z 2025-09-07T08:19:17.4774823Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=15. 2025-09-07T08:19:17.4775959Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.4776451Z 2025-09-07T08:19:17.4776677Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-09-07T08:19:17.4777269Z distribution where all component are from different parameterizations of 2025-09-07T08:19:17.4777850Z the same distribution type. It is parameterized by a `Categorical` 2025-09-07T08:19:17.4778366Z "selecting distribution" (over `k` component) and a component 2025-09-07T08:19:17.4778884Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-09-07T08:19:17.4779359Z (equal to `[k]`) which indexes each (batch of) component. 2025-09-07T08:19:17.4779625Z 2025-09-07T08:19:17.4779734Z Examples:: 2025-09-07T08:19:17.4779853Z 2025-09-07T08:19:17.4779965Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:17.4780394Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-09-07T08:19:17.4780829Z >>> # weighted normal distributions 2025-09-07T08:19:17.4781167Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T08:19:17.4781614Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-09-07T08:19:17.4781993Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T08:19:17.4782227Z 2025-09-07T08:19:17.4782439Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-09-07T08:19:17.4782890Z >>> # weighted bivariate normal distributions 2025-09-07T08:19:17.4783250Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T08:19:17.4783572Z >>> comp = D.Independent(D.Normal( 2025-09-07T08:19:17.4783917Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-09-07T08:19:17.4784274Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T08:19:17.4784495Z 2025-09-07T08:19:17.4784684Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-09-07T08:19:17.4785184Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-09-07T08:19:17.4785614Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-09-07T08:19:17.4785961Z >>> comp = D.Independent(D.Normal( 2025-09-07T08:19:17.4786310Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-09-07T08:19:17.4786675Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T08:19:17.4786895Z 2025-09-07T08:19:17.4786974Z Args: 2025-09-07T08:19:17.4787302Z mixture_distribution: `torch.distributions.Categorical`-like 2025-09-07T08:19:17.4787813Z instance. Manages the probability of selecting component. 2025-09-07T08:19:17.4788291Z The number of categories must match the rightmost batch 2025-09-07T08:19:17.4788752Z dimension of the `component_distribution`. Must have either 2025-09-07T08:19:17.4789204Z scalar `batch_shape` or `batch_shape` matching 2025-09-07T08:19:17.4789594Z `component_distribution.batch_shape[:-1]` 2025-09-07T08:19:17.4790063Z component_distribution: `torch.distributions.Distribution`-like 2025-09-07T08:19:17.4790621Z instance. Right-most batch dimension indexes component. 2025-09-07T08:19:17.4790908Z 2025-09-07T08:19:17.4791161Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.4791566Z 2025-09-07T08:19:17.4904751Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-09-07T08:19:17.4905764Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.4906158Z 2025-09-07T08:19:17.4906342Z Creates a RelaxedBernoulli distribution, parametrized by 2025-09-07T08:19:17.4906845Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-09-07T08:19:17.4907356Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-09-07T08:19:17.4907878Z so the values are in (0, 1), and has reparametrizable samples. 2025-09-07T08:19:17.4908185Z 2025-09-07T08:19:17.4908291Z Example:: 2025-09-07T08:19:17.4908411Z 2025-09-07T08:19:17.4908565Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:17.4908959Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-09-07T08:19:17.4909415Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-09-07T08:19:17.4909761Z >>> m.sample() 2025-09-07T08:19:17.4910026Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-09-07T08:19:17.4910249Z 2025-09-07T08:19:17.4910345Z Args: 2025-09-07T08:19:17.4910601Z temperature (Tensor): relaxation temperature 2025-09-07T08:19:17.4911038Z probs (Number, Tensor): the probability of sampling `1` 2025-09-07T08:19:17.4911493Z logits (Number, Tensor): the log-odds of sampling `1` 2025-09-07T08:19:17.4911762Z 2025-09-07T08:19:17.4912030Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.4912397Z 2025-09-07T08:19:17.4925488Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-09-07T08:19:17.4926716Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.4927102Z 2025-09-07T08:19:17.4927317Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-09-07T08:19:17.4927847Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-09-07T08:19:17.4928401Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-09-07T08:19:17.4928929Z its samples are on simplex, and are reparametrizable. 2025-09-07T08:19:17.4929197Z 2025-09-07T08:19:17.4929300Z Example:: 2025-09-07T08:19:17.4929418Z 2025-09-07T08:19:17.4929552Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:17.4929960Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-09-07T08:19:17.4930355Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-09-07T08:19:17.4930690Z >>> m.sample() 2025-09-07T08:19:17.4930940Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-09-07T08:19:17.4931177Z 2025-09-07T08:19:17.4931257Z Args: 2025-09-07T08:19:17.4931515Z temperature (Tensor): relaxation temperature 2025-09-07T08:19:17.4931884Z probs (Tensor): event probabilities 2025-09-07T08:19:17.4932280Z logits (Tensor): unnormalized log probability for each event 2025-09-07T08:19:17.4932591Z 2025-09-07T08:19:17.4932847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.4933227Z 2025-09-07T08:19:17.8776742Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2025-09-07T08:19:17.8778141Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.8779053Z Return a new dict with new, potentially nested, key value pair 2025-09-07T08:19:17.8779358Z 2025-09-07T08:19:17.8779466Z >>> purchase = { 2025-09-07T08:19:17.8779920Z ... "name": "Alice", 2025-09-07T08:19:17.8780421Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T08:19:17.8781090Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T08:19:17.8781622Z ... } 2025-09-07T08:19:17.8781938Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-09-07T08:19:17.8782376Z {'credit card': '5555-1234-1234-1234', 2025-09-07T08:19:17.8782747Z 'name': 'Alice', 2025-09-07T08:19:17.8783068Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-09-07T08:19:17.8783426Z 2025-09-07T08:19:17.8783913Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.8784452Z 2025-09-07T08:19:17.8785487Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2025-09-07T08:19:17.8786533Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.8787069Z Update value in a (potentially) nested dictionary 2025-09-07T08:19:17.8787332Z 2025-09-07T08:19:17.8787530Z inputs: 2025-09-07T08:19:17.8787766Z d - dictionary on which to operate 2025-09-07T08:19:17.8788216Z keys - list or tuple giving the location of the value to be changed in d 2025-09-07T08:19:17.8788690Z func - function to operate on that value 2025-09-07T08:19:17.8788917Z 2025-09-07T08:19:17.8789118Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-09-07T08:19:17.8789665Z original dictionary with v replaced by func(v), but does not mutate the 2025-09-07T08:19:17.8790105Z original dictionary. 2025-09-07T08:19:17.8790282Z 2025-09-07T08:19:17.8790488Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-09-07T08:19:17.8791032Z specified by the keys, with the innermost value set to func(default). 2025-09-07T08:19:17.8791365Z 2025-09-07T08:19:17.8791474Z >>> inc = lambda x: x + 1 2025-09-07T08:19:17.8791753Z >>> update_in({"a": 0}, ["a"], inc) 2025-09-07T08:19:17.8792153Z {'a': 1} 2025-09-07T08:19:17.8792295Z 2025-09-07T08:19:17.8792387Z >>> transaction = { 2025-09-07T08:19:17.8792646Z ... "name": "Alice", 2025-09-07T08:19:17.8793014Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T08:19:17.8793431Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T08:19:17.8793744Z ... } 2025-09-07T08:19:17.8794082Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-09-07T08:19:17.8794524Z {'credit card': '5555-1234-1234-1234', 2025-09-07T08:19:17.8794816Z 'name': 'Alice', 2025-09-07T08:19:17.8795137Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-09-07T08:19:17.8795427Z 2025-09-07T08:19:17.8795543Z >>> # updating a value when k0 is not in d 2025-09-07T08:19:17.8795903Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-09-07T08:19:17.8796224Z {1: {2: {3: 'bar'}}} 2025-09-07T08:19:17.8796502Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-09-07T08:19:17.8796825Z {1: 'foo', 2: {3: {4: 1}}} 2025-09-07T08:19:17.8797089Z 2025-09-07T08:19:17.8797440Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.8797820Z 2025-09-07T08:19:17.8798466Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2025-09-07T08:19:17.8799488Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.8800039Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-09-07T08:19:17.8800314Z 2025-09-07T08:19:17.8800508Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-09-07T08:19:17.8801002Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-09-07T08:19:17.8801394Z 2025-09-07T08:19:17.8801676Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-09-07T08:19:17.8802143Z structures such as dictionaries and lists. 2025-09-07T08:19:17.8802381Z 2025-09-07T08:19:17.8802489Z >>> transaction = { 2025-09-07T08:19:17.8802744Z ... "name": "Alice", 2025-09-07T08:19:17.8813835Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T08:19:17.8814286Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T08:19:17.8814616Z ... } 2025-09-07T08:19:17.8814890Z >>> get_in(["purchase", "items", 0], transaction) 2025-09-07T08:19:17.8815233Z 'Apple' 2025-09-07T08:19:17.8815461Z >>> get_in(["name"], transaction) 2025-09-07T08:19:17.8815761Z 'Alice' 2025-09-07T08:19:17.8816017Z >>> get_in(["purchase", "total"], transaction) 2025-09-07T08:19:17.8816418Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-09-07T08:19:17.8816805Z >>> get_in(["purchase", "items", 10], transaction) 2025-09-07T08:19:17.8817204Z >>> get_in(["purchase", "total"], transaction, 0) 2025-09-07T08:19:17.8817538Z 0 2025-09-07T08:19:17.8817778Z >>> get_in(["y"], {}, no_default=True) 2025-09-07T08:19:17.8818189Z Traceback (most recent call last): 2025-09-07T08:19:17.8818492Z ... 2025-09-07T08:19:17.8818711Z KeyError: 'y' 2025-09-07T08:19:17.8818854Z 2025-09-07T08:19:17.8818955Z See Also: 2025-09-07T08:19:17.8819164Z itertoolz.get 2025-09-07T08:19:17.8819404Z operator.getitem 2025-09-07T08:19:17.8819662Z 2025-09-07T08:19:17.8820039Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.8820407Z 2025-09-07T08:19:17.8821081Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2025-09-07T08:19:17.8822106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:17.8822605Z Group a collection by a key function 2025-09-07T08:19:17.8822829Z 2025-09-07T08:19:17.8823062Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-09-07T08:19:17.8823473Z >>> groupby(len, names) # doctest: +SKIP 2025-09-07T08:19:17.8823857Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-09-07T08:19:17.8824261Z 2025-09-07T08:19:17.8824393Z >>> iseven = lambda x: x % 2 == 0 2025-09-07T08:19:17.8824773Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-09-07T08:19:17.8825172Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-09-07T08:19:17.8825387Z 2025-09-07T08:19:17.8825539Z Non-callable keys imply grouping on a member. 2025-09-07T08:19:17.8825788Z 2025-09-07T08:19:17.8825873Z >>> groupby( 2025-09-07T08:19:17.8826105Z ... "gender", 2025-09-07T08:19:17.8826339Z ... [ 2025-09-07T08:19:17.8826591Z ... {"name": "Alice", "gender": "F"}, 2025-09-07T08:19:17.8826931Z ... {"name": "Bob", "gender": "M"}, 2025-09-07T08:19:17.8827283Z ... {"name": "Charlie", "gender": "M"}, 2025-09-07T08:19:17.8827616Z ... ], 2025-09-07T08:19:17.8827851Z ... ) # doctest:+SKIP 2025-09-07T08:19:17.8828123Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-09-07T08:19:17.8828456Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-09-07T08:19:17.8828789Z {'gender': 'M', 'name': 'Charlie'}]} 2025-09-07T08:19:17.8829010Z 2025-09-07T08:19:17.8829161Z Not to be confused with ``itertools.groupby`` 2025-09-07T08:19:17.8829407Z 2025-09-07T08:19:17.8829494Z See Also: 2025-09-07T08:19:17.8829709Z countby 2025-09-07T08:19:17.8829926Z 2025-09-07T08:19:17.8830295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:17.8830661Z 2025-09-07T08:19:18.1940942Z msg = Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py line=142. 2025-09-07T08:19:18.1942050Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.1942692Z Return the recommended gain value for the given nonlinearity function. 2025-09-07T08:19:18.1943046Z 2025-09-07T08:19:18.1943150Z The values are as follows: 2025-09-07T08:19:18.1943354Z 2025-09-07T08:19:18.1943467Z ================= ==================================================== 2025-09-07T08:19:18.1943817Z nonlinearity gain 2025-09-07T08:19:18.1944114Z ================= ==================================================== 2025-09-07T08:19:18.1944458Z Linear / Identity :math:`1` 2025-09-07T08:19:18.1944734Z Conv{1,2,3}D :math:`1` 2025-09-07T08:19:18.1945014Z Sigmoid :math:`1` 2025-09-07T08:19:18.1945306Z Tanh :math:`\frac{5}{3}` 2025-09-07T08:19:18.1945629Z ReLU :math:`\sqrt{2}` 2025-09-07T08:19:18.1946014Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-09-07T08:19:18.1946437Z SELU :math:`\frac{3}{4}` 2025-09-07T08:19:18.1946775Z ================= ==================================================== 2025-09-07T08:19:18.1947073Z 2025-09-07T08:19:18.1947195Z .. warning:: 2025-09-07T08:19:18.1947518Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-09-07T08:19:18.1948065Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-09-07T08:19:18.1948605Z This gives the initial weights a variance of ``1 / N``, 2025-09-07T08:19:18.1949120Z which is necessary to induce a stable fixed point in the forward pass. 2025-09-07T08:19:18.1949694Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-09-07T08:19:18.1950213Z effect for more stable gradient flow in rectangular layers. 2025-09-07T08:19:18.1950526Z 2025-09-07T08:19:18.1950612Z Args: 2025-09-07T08:19:18.1950940Z nonlinearity: the non-linear function (`nn.functional` name) 2025-09-07T08:19:18.1951436Z param: optional parameter for the non-linear function 2025-09-07T08:19:18.1951717Z 2025-09-07T08:19:18.1951905Z Examples: 2025-09-07T08:19:18.1952166Z >>> gain = nn.init.calculate_gain( 2025-09-07T08:19:18.1952501Z ... "leaky_relu", 0.2 2025-09-07T08:19:18.1952828Z ... ) # leaky_relu with negative_slope=0.2 2025-09-07T08:19:18.1953066Z 2025-09-07T08:19:18.1953567Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-09-07T08:19:18.1954232Z 2025-09-07T08:19:18.1954606Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.1954999Z 2025-09-07T08:19:18.2736530Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=603. 2025-09-07T08:19:18.2737489Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.2738070Z Applies Batch Normalization over a N-Dimensional input. 2025-09-07T08:19:18.2738358Z 2025-09-07T08:19:18.2738711Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-09-07T08:19:18.2739413Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-09-07T08:19:18.2740122Z Internal Covariate Shift `__ . 2025-09-07T08:19:18.2740572Z 2025-09-07T08:19:18.2740725Z .. math:: 2025-09-07T08:19:18.2740873Z 2025-09-07T08:19:18.2741165Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-09-07T08:19:18.2741822Z 2025-09-07T08:19:18.2742130Z The mean and standard-deviation are calculated per-dimension over all 2025-09-07T08:19:18.2742721Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-09-07T08:19:18.2743314Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-09-07T08:19:18.2744001Z By default, the elements of :math:`\gamma` are sampled from 2025-09-07T08:19:18.2744508Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-09-07T08:19:18.2745108Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-09-07T08:19:18.2745598Z `torch.var(input, unbiased=False)`. 2025-09-07T08:19:18.2745835Z 2025-09-07T08:19:18.2746068Z Also by default, during training this layer keeps running estimates of its 2025-09-07T08:19:18.2746671Z computed mean and variance, which are then used for normalization during 2025-09-07T08:19:18.2747269Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-09-07T08:19:18.2747722Z of 0.1. 2025-09-07T08:19:18.2747842Z 2025-09-07T08:19:18.2748068Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-09-07T08:19:18.2748638Z keep running estimates, and batch statistics are instead used during 2025-09-07T08:19:18.2749086Z evaluation time as well. 2025-09-07T08:19:18.2749267Z 2025-09-07T08:19:18.2749367Z .. note:: 2025-09-07T08:19:18.2749712Z This :attr:`momentum` argument is different from one used in optimizer 2025-09-07T08:19:18.2750347Z classes and the conventional notion of momentum. Mathematically, the 2025-09-07T08:19:18.2750828Z update rule for running statistics here is 2025-09-07T08:19:18.2751342Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-09-07T08:19:18.2751937Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-09-07T08:19:18.2752354Z new observed value. 2025-09-07T08:19:18.2752545Z 2025-09-07T08:19:18.2752849Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-09-07T08:19:18.2753536Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-09-07T08:19:18.2754096Z Normalization or Spatio-temporal Batch Normalization. 2025-09-07T08:19:18.2754381Z 2025-09-07T08:19:18.2754636Z Currently :class:`SyncBatchNorm` only supports 2025-09-07T08:19:18.2755175Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-09-07T08:19:18.2755805Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-09-07T08:19:18.2756353Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-09-07T08:19:18.2756785Z Network with DDP. 2025-09-07T08:19:18.2756941Z 2025-09-07T08:19:18.2757028Z Args: 2025-09-07T08:19:18.2757329Z num_features: :math:`C` from an expected input of size 2025-09-07T08:19:18.2757705Z :math:`(N, C, +)` 2025-09-07T08:19:18.2758077Z eps: a value added to the denominator for numerical stability. 2025-09-07T08:19:18.2758474Z Default: ``1e-5`` 2025-09-07T08:19:18.2758858Z momentum: the value used for the running_mean and running_var 2025-09-07T08:19:18.2759389Z computation. Can be set to ``None`` for cumulative moving average 2025-09-07T08:19:18.2759850Z (i.e. simple average). Default: 0.1 2025-09-07T08:19:18.2760295Z affine: a boolean value that when set to ``True``, this module has 2025-09-07T08:19:18.2760762Z learnable affine parameters. Default: ``True`` 2025-09-07T08:19:18.2761244Z track_running_stats: a boolean value that when set to ``True``, this 2025-09-07T08:19:18.2761812Z module tracks the running mean and variance, and when set to ``False``, 2025-09-07T08:19:18.2762396Z this module does not track such statistics, and initializes statistics 2025-09-07T08:19:18.2762954Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-09-07T08:19:18.2763497Z When these buffers are ``None``, this module always uses batch statistics. 2025-09-07T08:19:18.2764004Z in both training and eval modes. Default: ``True`` 2025-09-07T08:19:18.2764645Z process_group: synchronization of stats happen within each process group 2025-09-07T08:19:18.2765255Z individually. Default behavior is synchronization across the whole 2025-09-07T08:19:18.2765695Z world 2025-09-07T08:19:18.2765834Z 2025-09-07T08:19:18.2765919Z Shape: 2025-09-07T08:19:18.2766151Z - Input: :math:`(N, C, +)` 2025-09-07T08:19:18.2766499Z - Output: :math:`(N, C, +)` (same shape as input) 2025-09-07T08:19:18.2766750Z 2025-09-07T08:19:18.2766849Z .. note:: 2025-09-07T08:19:18.2767211Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-09-07T08:19:18.2767788Z synchronization is disabled when ``model.eval()`` is set or if 2025-09-07T08:19:18.2768238Z ``self.training`` is otherwise ``False``. 2025-09-07T08:19:18.2768469Z 2025-09-07T08:19:18.2768569Z Examples:: 2025-09-07T08:19:18.2768699Z 2025-09-07T08:19:18.2768795Z >>> # xdoctest: +SKIP 2025-09-07T08:19:18.2769092Z >>> # With Learnable Parameters 2025-09-07T08:19:18.2769418Z >>> m = nn.SyncBatchNorm(100) 2025-09-07T08:19:18.2769757Z >>> # creating process group (optional) 2025-09-07T08:19:18.2770177Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T08:19:18.2770523Z >>> ranks = list(range(8)) 2025-09-07T08:19:18.2770829Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T08:19:18.2771195Z >>> # Note: every rank calls into new_group for every 2025-09-07T08:19:18.2771613Z >>> # process group created, even if that rank is not 2025-09-07T08:19:18.2771970Z >>> # part of the group. 2025-09-07T08:19:18.2772408Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T08:19:18.2772986Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T08:19:18.2773626Z >>> # Without Learnable Parameters 2025-09-07T08:19:18.2774046Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-09-07T08:19:18.2774504Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-09-07T08:19:18.2774963Z >>> output = m(input) 2025-09-07T08:19:18.2775147Z 2025-09-07T08:19:18.2775275Z >>> # network is nn.BatchNorm layer 2025-09-07T08:19:18.2775783Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-09-07T08:19:18.2776343Z >>> # only single gpu per process is currently supported 2025-09-07T08:19:18.2776844Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-09-07T08:19:18.2777301Z >>> sync_bn_network, 2025-09-07T08:19:18.2777667Z >>> device_ids=[args.local_rank], 2025-09-07T08:19:18.2778042Z >>> output_device=args.local_rank) 2025-09-07T08:19:18.2778377Z 2025-09-07T08:19:18.2778742Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.2779113Z 2025-09-07T08:19:18.2779813Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=830. 2025-09-07T08:19:18.2780850Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.2781546Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-09-07T08:19:18.2781975Z 2025-09-07T08:19:18.2782060Z Args: 2025-09-07T08:19:18.2782442Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-09-07T08:19:18.2783032Z process_group (optional): process group to scope synchronization, 2025-09-07T08:19:18.2783487Z default is the whole world 2025-09-07T08:19:18.2783700Z 2025-09-07T08:19:18.2783785Z Returns: 2025-09-07T08:19:18.2784178Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-09-07T08:19:18.2784815Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-09-07T08:19:18.2785371Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-09-07T08:19:18.2785796Z instead. 2025-09-07T08:19:18.2785943Z 2025-09-07T08:19:18.2786034Z Example:: 2025-09-07T08:19:18.2786187Z 2025-09-07T08:19:18.2786304Z >>> # Network with nn.BatchNorm layer 2025-09-07T08:19:18.2786680Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:18.2787050Z >>> module = torch.nn.Sequential( 2025-09-07T08:19:18.2787384Z >>> torch.nn.Linear(20, 100), 2025-09-07T08:19:18.2787738Z >>> torch.nn.BatchNorm1d(100), 2025-09-07T08:19:18.2788065Z >>> ).cuda() 2025-09-07T08:19:18.2788378Z >>> # creating process group (optional) 2025-09-07T08:19:18.2788748Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T08:19:18.2789112Z >>> ranks = list(range(8)) 2025-09-07T08:19:18.2789431Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T08:19:18.2789802Z >>> # Note: every rank calls into new_group for every 2025-09-07T08:19:18.2790244Z >>> # process group created, even if that rank is not 2025-09-07T08:19:18.2790616Z >>> # part of the group. 2025-09-07T08:19:18.2790940Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:18.2791427Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T08:19:18.2792004Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T08:19:18.2792608Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-09-07T08:19:18.2793032Z 2025-09-07T08:19:18.2793114Z 2025-09-07T08:19:18.2793485Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.2793855Z 2025-09-07T08:19:18.3015926Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=66. 2025-09-07T08:19:18.3016841Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.3017222Z 2025-09-07T08:19:18.3017545Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-09-07T08:19:18.3017970Z 2025-09-07T08:19:18.3018238Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-09-07T08:19:18.3018860Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-09-07T08:19:18.3019210Z 2025-09-07T08:19:18.3019524Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-09-07T08:19:18.3020213Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-09-07T08:19:18.3020756Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-09-07T08:19:18.3021038Z 2025-09-07T08:19:18.3021120Z Shape: 2025-09-07T08:19:18.3021463Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-09-07T08:19:18.3022048Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-09-07T08:19:18.3022637Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-09-07T08:19:18.3023092Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-09-07T08:19:18.3023341Z 2025-09-07T08:19:18.3023424Z Args: 2025-09-07T08:19:18.3023693Z dim (Union[int, str]): Dimension to be unflattened 2025-09-07T08:19:18.3024303Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-09-07T08:19:18.3024762Z 2025-09-07T08:19:18.3024862Z Examples: 2025-09-07T08:19:18.3025083Z >>> input = torch.randn(2, 50) 2025-09-07T08:19:18.3025389Z >>> # With tuple of ints 2025-09-07T08:19:18.3025718Z >>> m = nn.Sequential( 2025-09-07T08:19:18.3025994Z >>> nn.Linear(50, 50), 2025-09-07T08:19:18.3026275Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-09-07T08:19:18.3026575Z >>> ) 2025-09-07T08:19:18.3026796Z >>> output = m(input) 2025-09-07T08:19:18.3027065Z >>> output.size() 2025-09-07T08:19:18.3027312Z torch.Size([2, 2, 5, 5]) 2025-09-07T08:19:18.3027605Z >>> # With torch.Size 2025-09-07T08:19:18.3027872Z >>> m = nn.Sequential( 2025-09-07T08:19:18.3028144Z >>> nn.Linear(50, 50), 2025-09-07T08:19:18.3028433Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-09-07T08:19:18.3028752Z >>> ) 2025-09-07T08:19:18.3028967Z >>> output = m(input) 2025-09-07T08:19:18.3029228Z >>> output.size() 2025-09-07T08:19:18.3029467Z torch.Size([2, 2, 5, 5]) 2025-09-07T08:19:18.3029764Z >>> # With namedshape (tuple of tuples) 2025-09-07T08:19:18.3030138Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-09-07T08:19:18.3030609Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-09-07T08:19:18.3031030Z >>> output = unflatten(input) 2025-09-07T08:19:18.3031327Z >>> output.size() 2025-09-07T08:19:18.3031632Z torch.Size([2, 2, 5, 5]) 2025-09-07T08:19:18.3031806Z 2025-09-07T08:19:18.3032069Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.3032438Z 2025-09-07T08:19:18.3388854Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1798. 2025-09-07T08:19:18.3389948Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.3390532Z Creates a criterion that measures the triplet loss given input 2025-09-07T08:19:18.3391044Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-09-07T08:19:18.3391563Z positive, and negative examples, respectively), and a nonnegative, 2025-09-07T08:19:18.3392152Z real-valued function ("distance function") used to compute the relationship 2025-09-07T08:19:18.3392882Z between the anchor and positive example ("positive distance") and the 2025-09-07T08:19:18.3393385Z anchor and negative example ("negative distance"). 2025-09-07T08:19:18.3393660Z 2025-09-07T08:19:18.3393868Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-09-07T08:19:18.3394296Z can be described as: 2025-09-07T08:19:18.3394467Z 2025-09-07T08:19:18.3394569Z .. math:: 2025-09-07T08:19:18.3394849Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-09-07T08:19:18.3395262Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-09-07T08:19:18.3395524Z 2025-09-07T08:19:18.3395805Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-09-07T08:19:18.3396463Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-09-07T08:19:18.3397118Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-09-07T08:19:18.3397720Z between the positive and negative distances that is required for the loss to 2025-09-07T08:19:18.3398321Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-09-07T08:19:18.3398794Z that the distance function can handle. 2025-09-07T08:19:18.3399018Z 2025-09-07T08:19:18.3399143Z If :attr:`reduction` is not ``'none'`` 2025-09-07T08:19:18.3399468Z (default ``'mean'``), then: 2025-09-07T08:19:18.3399652Z 2025-09-07T08:19:18.3399735Z .. math:: 2025-09-07T08:19:18.3399954Z \ell(x, y) = 2025-09-07T08:19:18.3400197Z \begin{cases} 2025-09-07T08:19:18.3400552Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-09-07T08:19:18.3401048Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-09-07T08:19:18.3401444Z \end{cases} 2025-09-07T08:19:18.3401596Z 2025-09-07T08:19:18.3401892Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-09-07T08:19:18.3402913Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-09-07T08:19:18.3403472Z 2025-09-07T08:19:18.3403571Z Args: 2025-09-07T08:19:18.3404158Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-09-07T08:19:18.3405138Z quantifies the closeness of two tensors. If not specified, 2025-09-07T08:19:18.3405926Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-09-07T08:19:18.3406478Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-09-07T08:19:18.3407136Z between the positive and negative distances required for the loss to be 0. Larger 2025-09-07T08:19:18.3407810Z margins penalize cases where the negative examples are not distant enough from the 2025-09-07T08:19:18.3408387Z anchors, relative to the positives. Default: :math:`1`. 2025-09-07T08:19:18.3408932Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-09-07T08:19:18.3409568Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-09-07T08:19:18.3410263Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-09-07T08:19:18.3410882Z negative example than the anchor is, swaps the positive example and the anchor in 2025-09-07T08:19:18.3411409Z the loss computation. Default: ``False``. 2025-09-07T08:19:18.3411942Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-09-07T08:19:18.3412529Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T08:19:18.3413008Z ``'mean'``: the sum of the output will be divided by the number of 2025-09-07T08:19:18.3413551Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-09-07T08:19:18.3413924Z 2025-09-07T08:19:18.3413928Z 2025-09-07T08:19:18.3414014Z Shape: 2025-09-07T08:19:18.3414441Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-09-07T08:19:18.3414926Z as supported by the distance function. 2025-09-07T08:19:18.3415420Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-09-07T08:19:18.3415884Z otherwise. 2025-09-07T08:19:18.3416033Z 2025-09-07T08:19:18.3416129Z Examples: 2025-09-07T08:19:18.3416258Z 2025-09-07T08:19:18.3416371Z >>> # Initialize embeddings 2025-09-07T08:19:18.3416665Z >>> embedding = nn.Embedding(1000, 128) 2025-09-07T08:19:18.3417017Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-09-07T08:19:18.3417382Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-09-07T08:19:18.3417751Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-09-07T08:19:18.3418091Z >>> anchor = embedding(anchor_ids) 2025-09-07T08:19:18.3418431Z >>> positive = embedding(positive_ids) 2025-09-07T08:19:18.3418776Z >>> negative = embedding(negative_ids) 2025-09-07T08:19:18.3419084Z >>> 2025-09-07T08:19:18.3419304Z >>> # Built-in Distance Function 2025-09-07T08:19:18.3419612Z >>> triplet_loss = \ 2025-09-07T08:19:18.3420062Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-09-07T08:19:18.3420622Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T08:19:18.3420987Z >>> output.backward() 2025-09-07T08:19:18.3421234Z >>> 2025-09-07T08:19:18.3421458Z >>> # Custom Distance Function 2025-09-07T08:19:18.3421764Z >>> def l_infinity(x1, x2): 2025-09-07T08:19:18.3422110Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-09-07T08:19:18.3422449Z >>> 2025-09-07T08:19:18.3422751Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-09-07T08:19:18.3423152Z >>> triplet_loss = ( 2025-09-07T08:19:18.3423635Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-09-07T08:19:18.3424180Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T08:19:18.3424548Z >>> output.backward() 2025-09-07T08:19:18.3424803Z >>> 2025-09-07T08:19:18.3425041Z >>> # Custom Distance Function (Lambda) 2025-09-07T08:19:18.3425354Z >>> triplet_loss = ( 2025-09-07T08:19:18.3425656Z >>> nn.TripletMarginWithDistanceLoss( 2025-09-07T08:19:18.3426118Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-09-07T08:19:18.3426603Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T08:19:18.3426955Z >>> output.backward() 2025-09-07T08:19:18.3427136Z 2025-09-07T08:19:18.3427221Z Reference: 2025-09-07T08:19:18.3427662Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-09-07T08:19:18.3428315Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-09-07T08:19:18.3428719Z 2025-09-07T08:19:18.3429093Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-09-07T08:19:18.3429528Z 2025-09-07T08:19:18.3430073Z msg = Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1933. 2025-09-07T08:19:18.3430933Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.3431462Z The Connectionist Temporal Classification loss. 2025-09-07T08:19:18.3431730Z 2025-09-07T08:19:18.3432110Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-09-07T08:19:18.3432972Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-09-07T08:19:18.3433791Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-09-07T08:19:18.3434537Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-09-07T08:19:18.3434961Z 2025-09-07T08:19:18.3435104Z Args: 2025-09-07T08:19:18.3435396Z blank (int, optional): blank label. Default :math:`0`. 2025-09-07T08:19:18.3435916Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-09-07T08:19:18.3436461Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T08:19:18.3436962Z ``'mean'``: the output losses will be divided by the target lengths and 2025-09-07T08:19:18.3437541Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-09-07T08:19:18.3438008Z Default: ``'mean'`` 2025-09-07T08:19:18.3438307Z zero_infinity (bool, optional): 2025-09-07T08:19:18.3438711Z Whether to zero infinite losses and the associated gradients. 2025-09-07T08:19:18.3439122Z Default: ``False`` 2025-09-07T08:19:18.3439495Z Infinite losses mainly occur when the inputs are too short 2025-09-07T08:19:18.3439918Z to be aligned to the targets. 2025-09-07T08:19:18.3440135Z 2025-09-07T08:19:18.3440218Z Shape: 2025-09-07T08:19:18.3440530Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-09-07T08:19:18.3440959Z where :math:`T = \text{input length}`, 2025-09-07T08:19:18.3441324Z :math:`N = \text{batch size}`, and 2025-09-07T08:19:18.3441692Z :math:`C = \text{number of classes (including blank)}`. 2025-09-07T08:19:18.3442192Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-09-07T08:19:18.3442673Z :func:`torch.nn.functional.log_softmax`). 2025-09-07T08:19:18.3443052Z - Targets: Tensor of size :math:`(N, S)` or 2025-09-07T08:19:18.3443448Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-09-07T08:19:18.3443847Z where :math:`N = \text{batch size}` and 2025-09-07T08:19:18.3444376Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-09-07T08:19:18.3444867Z It represents the target sequences. Each element in the target 2025-09-07T08:19:18.3445429Z sequence is a class index. And the target index cannot be blank (default=0). 2025-09-07T08:19:18.3445939Z In the :math:`(N, S)` form, targets are padded to the 2025-09-07T08:19:18.3446361Z length of the longest sequence, and stacked. 2025-09-07T08:19:18.3446806Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-09-07T08:19:18.3447260Z the targets are assumed to be un-padded and 2025-09-07T08:19:18.3447612Z concatenated within 1 dimension. 2025-09-07T08:19:18.3448053Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T08:19:18.3448588Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-09-07T08:19:18.3449121Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-09-07T08:19:18.3449688Z for each sequence to achieve masking under the assumption that sequences 2025-09-07T08:19:18.3450176Z are padded to equal lengths. 2025-09-07T08:19:18.3450605Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T08:19:18.3451151Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-09-07T08:19:18.3451711Z Lengths are specified for each sequence to achieve masking under the 2025-09-07T08:19:18.3452285Z assumption that sequences are padded to equal lengths. If target shape is 2025-09-07T08:19:18.3452824Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-09-07T08:19:18.3453370Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-09-07T08:19:18.3453924Z each target in a batch. Lengths must each be :math:`\leq S` 2025-09-07T08:19:18.3454463Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-09-07T08:19:18.3455111Z targets, the target_lengths must add up to the total length of the tensor. 2025-09-07T08:19:18.3455672Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-09-07T08:19:18.3456213Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-09-07T08:19:18.3456765Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-09-07T08:19:18.3457080Z 2025-09-07T08:19:18.3457181Z Examples: 2025-09-07T08:19:18.3457312Z 2025-09-07T08:19:18.3457416Z >>> # Target are to be padded 2025-09-07T08:19:18.3457742Z >>> T = 50 # Input sequence length 2025-09-07T08:19:18.3458103Z >>> C = 20 # Number of classes (including blank) 2025-09-07T08:19:18.3458452Z >>> N = 16 # Batch size 2025-09-07T08:19:18.3458852Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-09-07T08:19:18.3459386Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-09-07T08:19:18.3459773Z >>> 2025-09-07T08:19:18.3460096Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T08:19:18.3460610Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T08:19:18.3461026Z >>> 2025-09-07T08:19:18.3461346Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T08:19:18.3461875Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-09-07T08:19:18.3462283Z >>> 2025-09-07T08:19:18.3462631Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T08:19:18.3463085Z >>> target_lengths = torch.randint( 2025-09-07T08:19:18.3463401Z ... low=S_min, 2025-09-07T08:19:18.3463660Z ... high=S, 2025-09-07T08:19:18.3463897Z ... size=(N,), 2025-09-07T08:19:18.3464549Z ... dtype=torch.long, 2025-09-07T08:19:18.3464832Z ... ) 2025-09-07T08:19:18.3465061Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T08:19:18.3465461Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T08:19:18.3465863Z >>> loss.backward() 2025-09-07T08:19:18.3466125Z >>> 2025-09-07T08:19:18.3466329Z >>> 2025-09-07T08:19:18.3466551Z >>> # Target are to be un-padded 2025-09-07T08:19:18.3466878Z >>> T = 50 # Input sequence length 2025-09-07T08:19:18.3467230Z >>> C = 20 # Number of classes (including blank) 2025-09-07T08:19:18.3467576Z >>> N = 16 # Batch size 2025-09-07T08:19:18.3467830Z >>> 2025-09-07T08:19:18.3468149Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T08:19:18.3468673Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T08:19:18.3469220Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T08:19:18.3469633Z >>> 2025-09-07T08:19:18.3469956Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T08:19:18.3470537Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-09-07T08:19:18.3471000Z >>> target = torch.randint( 2025-09-07T08:19:18.3471278Z ... low=1, 2025-09-07T08:19:18.3471518Z ... high=C, 2025-09-07T08:19:18.3471787Z ... size=(sum(target_lengths),), 2025-09-07T08:19:18.3472111Z ... dtype=torch.long, 2025-09-07T08:19:18.3472379Z ... ) 2025-09-07T08:19:18.3472611Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T08:19:18.3472997Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T08:19:18.3473600Z >>> loss.backward() 2025-09-07T08:19:18.3473854Z >>> 2025-09-07T08:19:18.3474058Z >>> 2025-09-07T08:19:18.3474373Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-09-07T08:19:18.3474794Z >>> T = 50 # Input sequence length 2025-09-07T08:19:18.3475232Z >>> C = 20 # Number of classes (including blank) 2025-09-07T08:19:18.3475570Z >>> 2025-09-07T08:19:18.3475889Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-09-07T08:19:18.3476338Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-09-07T08:19:18.3476777Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-09-07T08:19:18.3477254Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-09-07T08:19:18.3477610Z >>> 2025-09-07T08:19:18.3477930Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T08:19:18.3478479Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-09-07T08:19:18.3478925Z >>> target = torch.randint( 2025-09-07T08:19:18.3479220Z ... low=1, 2025-09-07T08:19:18.3479465Z ... high=C, 2025-09-07T08:19:18.3479734Z ... size=(target_lengths,), 2025-09-07T08:19:18.3480046Z ... dtype=torch.long, 2025-09-07T08:19:18.3480330Z ... ) 2025-09-07T08:19:18.3480564Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T08:19:18.3480954Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T08:19:18.3481348Z >>> loss.backward() 2025-09-07T08:19:18.3481536Z 2025-09-07T08:19:18.3481626Z Reference: 2025-09-07T08:19:18.3481942Z A. Graves et al.: Connectionist Temporal Classification: 2025-09-07T08:19:18.3482469Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-09-07T08:19:18.3482978Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-09-07T08:19:18.3483258Z 2025-09-07T08:19:18.3483341Z Note: 2025-09-07T08:19:18.3483712Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-09-07T08:19:18.3484450Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-09-07T08:19:18.3485050Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-09-07T08:19:18.3485497Z dtype :attr:`torch.int32`. 2025-09-07T08:19:18.3485715Z 2025-09-07T08:19:18.3485984Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-09-07T08:19:18.3486385Z 2025-09-07T08:19:18.3486389Z 2025-09-07T08:19:18.3486472Z Note: 2025-09-07T08:19:18.3486840Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-09-07T08:19:18.3487470Z may select a nondeterministic algorithm to increase performance. If this is 2025-09-07T08:19:18.3488079Z undesirable, you can try to make the operation deterministic (potentially at 2025-09-07T08:19:18.3488680Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-09-07T08:19:18.3489117Z True``. 2025-09-07T08:19:18.3489457Z Please see the notes on :doc:`/notes/randomness` for background. 2025-09-07T08:19:18.3489843Z 2025-09-07T08:19:18.3490214Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.3490631Z 2025-09-07T08:19:18.3958804Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=410. 2025-09-07T08:19:18.3959761Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.3960305Z Computes a partial inverse of :class:`MaxPool2d`. 2025-09-07T08:19:18.3960583Z 2025-09-07T08:19:18.3960845Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-09-07T08:19:18.3961238Z 2025-09-07T08:19:18.3961462Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-09-07T08:19:18.3962053Z including the indices of the maximal values and computes a partial inverse 2025-09-07T08:19:18.3962559Z in which all non-maximal values are set to zero. 2025-09-07T08:19:18.3962822Z 2025-09-07T08:19:18.3962906Z Note: 2025-09-07T08:19:18.3963492Z This operation may behave nondeterministically when the input indices has repeat values. 2025-09-07T08:19:18.3964403Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-09-07T08:19:18.3964899Z 2025-09-07T08:19:18.3965155Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-09-07T08:19:18.3965668Z sizes. Hence, the inversion process can get ambiguous. 2025-09-07T08:19:18.3966155Z To accommodate this, you can provide the needed output size 2025-09-07T08:19:18.3966679Z as an additional argument :attr:`output_size` in the forward call. 2025-09-07T08:19:18.3967130Z See the Inputs and Example below. 2025-09-07T08:19:18.3967358Z 2025-09-07T08:19:18.3967454Z Args: 2025-09-07T08:19:18.3967750Z kernel_size (int or tuple): Size of the max pooling window. 2025-09-07T08:19:18.3968220Z stride (int or tuple): Stride of the max pooling window. 2025-09-07T08:19:18.3968647Z It is set to :attr:`kernel_size` by default. 2025-09-07T08:19:18.3969076Z padding (int or tuple): Padding that was added to the input 2025-09-07T08:19:18.3969368Z 2025-09-07T08:19:18.3969484Z Inputs: 2025-09-07T08:19:18.3969719Z - `input`: the input Tensor to invert 2025-09-07T08:19:18.3970157Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-09-07T08:19:18.3970635Z - `output_size` (optional): the targeted output size 2025-09-07T08:19:18.3970904Z 2025-09-07T08:19:18.3970999Z Shape: 2025-09-07T08:19:18.3971300Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-09-07T08:19:18.3971818Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-09-07T08:19:18.3972158Z 2025-09-07T08:19:18.3972298Z .. math:: 2025-09-07T08:19:18.3972716Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-09-07T08:19:18.3973098Z 2025-09-07T08:19:18.3973199Z .. math:: 2025-09-07T08:19:18.3973766Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-09-07T08:19:18.3974151Z 2025-09-07T08:19:18.3974313Z or as given by :attr:`output_size` in the call operator 2025-09-07T08:19:18.3974602Z 2025-09-07T08:19:18.3974694Z Example:: 2025-09-07T08:19:18.3974823Z 2025-09-07T08:19:18.3974997Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-09-07T08:19:18.3975398Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-09-07T08:19:18.3975758Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-09-07T08:19:18.3976114Z [ 5., 6., 7., 8.], 2025-09-07T08:19:18.3976446Z [ 9., 10., 11., 12.], 2025-09-07T08:19:18.3976782Z [13., 14., 15., 16.]]]]) 2025-09-07T08:19:18.3977115Z >>> output, indices = pool(input) 2025-09-07T08:19:18.3977489Z >>> unpool(output, indices) 2025-09-07T08:19:18.3977798Z tensor([[[[ 0., 0., 0., 0.], 2025-09-07T08:19:18.3978107Z [ 0., 6., 0., 8.], 2025-09-07T08:19:18.3978397Z [ 0., 0., 0., 0.], 2025-09-07T08:19:18.3978706Z [ 0., 14., 0., 16.]]]]) 2025-09-07T08:19:18.3979128Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-09-07T08:19:18.3979611Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-09-07T08:19:18.3979985Z [ 6., 7., 8., 9., 10.], 2025-09-07T08:19:18.3980314Z [11., 12., 13., 14., 15.], 2025-09-07T08:19:18.3980658Z [16., 17., 18., 19., 20.]]]]) 2025-09-07T08:19:18.3981003Z >>> output, indices = pool(input) 2025-09-07T08:19:18.3981473Z >>> # This call will not work without specifying output_size 2025-09-07T08:19:18.3981935Z >>> unpool(output, indices, output_size=input.size()) 2025-09-07T08:19:18.3982318Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-09-07T08:19:18.3982620Z [ 0., 7., 0., 9., 0.], 2025-09-07T08:19:18.3982935Z [ 0., 0., 0., 0., 0.], 2025-09-07T08:19:18.3983246Z [ 0., 17., 0., 19., 0.]]]]) 2025-09-07T08:19:18.3983457Z 2025-09-07T08:19:18.3983462Z 2025-09-07T08:19:18.3983556Z 2025-09-07T08:19:18.3983914Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.3984298Z 2025-09-07T08:19:18.4239056Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=272. 2025-09-07T08:19:18.4240113Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.4240836Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-09-07T08:19:18.4241270Z 2025-09-07T08:19:18.4241606Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-09-07T08:19:18.4242161Z and with 2D inputs, this class 2025-09-07T08:19:18.4242364Z 2025-09-07T08:19:18.4242674Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-09-07T08:19:18.4243414Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-09-07T08:19:18.4244228Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-09-07T08:19:18.4244664Z 2025-09-07T08:19:18.4245014Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-09-07T08:19:18.4245683Z operations. 2025-09-07T08:19:18.4245819Z 2025-09-07T08:19:18.4246087Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-09-07T08:19:18.4246708Z pass. This scales the output of the Embedding before performing a weighted 2025-09-07T08:19:18.4247319Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-09-07T08:19:18.4247929Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-09-07T08:19:18.4248393Z :attr:`per_sample_weights`. 2025-09-07T08:19:18.4248584Z 2025-09-07T08:19:18.4248665Z Args: 2025-09-07T08:19:18.4248972Z num_embeddings (int): size of the dictionary of embeddings 2025-09-07T08:19:18.4249437Z embedding_dim (int): the size of each embedding vector 2025-09-07T08:19:18.4250034Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-09-07T08:19:18.4250613Z is renormalized to have norm :attr:`max_norm`. 2025-09-07T08:19:18.4251230Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-09-07T08:19:18.4252057Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-09-07T08:19:18.4252672Z the words in the mini-batch. Default ``False``. 2025-09-07T08:19:18.4253140Z Note: this option is not supported when ``mode="max"``. 2025-09-07T08:19:18.4253680Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-09-07T08:19:18.4254274Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-09-07T08:19:18.4254836Z into consideration. ``"mean"`` computes the average of the values 2025-09-07T08:19:18.4255376Z in the bag, ``"max"`` computes the max value over each bag. 2025-09-07T08:19:18.4255871Z Default: ``"mean"`` 2025-09-07T08:19:18.4256423Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-09-07T08:19:18.4257108Z Notes for more details regarding sparse gradients. Note: this option is not 2025-09-07T08:19:18.4257616Z supported when ``mode="max"``. 2025-09-07T08:19:18.4258237Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-09-07T08:19:18.4258954Z is equivalent to the size of `indices`. This matches the CSR format. 2025-09-07T08:19:18.4259634Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-09-07T08:19:18.4260350Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-09-07T08:19:18.4260999Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-09-07T08:19:18.4261635Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-09-07T08:19:18.4262275Z zeros, but can be updated to another value to be used as the padding vector. 2025-09-07T08:19:18.4262892Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-09-07T08:19:18.4263357Z reduction. 2025-09-07T08:19:18.4263587Z 2025-09-07T08:19:18.4263676Z Attributes: 2025-09-07T08:19:18.4264135Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-09-07T08:19:18.4264713Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-09-07T08:19:18.4264995Z 2025-09-07T08:19:18.4265109Z Examples:: 2025-09-07T08:19:18.4265245Z 2025-09-07T08:19:18.4265416Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-09-07T08:19:18.4265865Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-09-07T08:19:18.4266266Z >>> # a batch of 2 samples of 4 indices each 2025-09-07T08:19:18.4266698Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T08:19:18.4267141Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T08:19:18.4267549Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:18.4267918Z >>> embedding_sum(input, offsets) 2025-09-07T08:19:18.4268256Z tensor([[-0.8861, -5.4350, -0.0523], 2025-09-07T08:19:18.4268576Z [ 1.1306, -2.5798, -1.0044]]) 2025-09-07T08:19:18.4268782Z 2025-09-07T08:19:18.4268891Z >>> # Example with padding_idx 2025-09-07T08:19:18.4269312Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-09-07T08:19:18.4269826Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T08:19:18.4270309Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T08:19:18.4270671Z >>> embedding_sum(input, offsets) 2025-09-07T08:19:18.4270995Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-09-07T08:19:18.4271305Z [-0.7082, 3.2145, -2.6251]]) 2025-09-07T08:19:18.4271511Z 2025-09-07T08:19:18.4271693Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-09-07T08:19:18.4272134Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-09-07T08:19:18.4272542Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-09-07T08:19:18.4272917Z embedding.weight, 2025-09-07T08:19:18.4273243Z padding_idx=embedding.padding_idx, 2025-09-07T08:19:18.4273767Z mode='sum') 2025-09-07T08:19:18.4274014Z 2025-09-07T08:19:18.4274385Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.4274850Z 2025-09-07T08:19:18.4310544Z msg = Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py line=186. 2025-09-07T08:19:18.4311866Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.4312406Z Take in and process masked source/target sequences. 2025-09-07T08:19:18.4312673Z 2025-09-07T08:19:18.4312769Z .. note:: 2025-09-07T08:19:18.4312920Z 2025-09-07T08:19:18.4313314Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-09-07T08:19:18.4313972Z not allowed to participate in the attention, 2025-09-07T08:19:18.4314425Z which is the opposite of the definition for :attr:`attn_mask` 2025-09-07T08:19:18.4315045Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-09-07T08:19:18.4315355Z 2025-09-07T08:19:18.4315437Z Args: 2025-09-07T08:19:18.4315718Z src: the sequence to the encoder (required). 2025-09-07T08:19:18.4316111Z tgt: the sequence to the decoder (required). 2025-09-07T08:19:18.4316574Z src_mask: the additive mask for the src sequence (optional). 2025-09-07T08:19:18.4317058Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-09-07T08:19:18.4317556Z memory_mask: the additive mask for the encoder output (optional). 2025-09-07T08:19:18.4318121Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-09-07T08:19:18.4318704Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-09-07T08:19:18.4319311Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-09-07T08:19:18.4319906Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-09-07T08:19:18.4320471Z Default: ``None``; try to detect a causal mask. 2025-09-07T08:19:18.4320838Z Warning: 2025-09-07T08:19:18.4321168Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-09-07T08:19:18.4321627Z the causal mask. Providing incorrect hints can result in 2025-09-07T08:19:18.4322081Z incorrect execution, including forward and backward 2025-09-07T08:19:18.4322472Z compatibility. 2025-09-07T08:19:18.4322873Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-09-07T08:19:18.4323355Z Default: ``None``; try to detect a causal mask. 2025-09-07T08:19:18.4323714Z Warning: 2025-09-07T08:19:18.4324028Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-09-07T08:19:18.4324567Z the causal mask. Providing incorrect hints can result in 2025-09-07T08:19:18.4325043Z incorrect execution, including forward and backward 2025-09-07T08:19:18.4325433Z compatibility. 2025-09-07T08:19:18.4325837Z memory_is_causal: If specified, applies a causal mask as 2025-09-07T08:19:18.4326232Z ``memory_mask``. 2025-09-07T08:19:18.4326526Z Default: ``False``. 2025-09-07T08:19:18.4326822Z Warning: 2025-09-07T08:19:18.4327106Z ``memory_is_causal`` provides a hint that 2025-09-07T08:19:18.4327526Z ``memory_mask`` is the causal mask. Providing incorrect 2025-09-07T08:19:18.4327973Z hints can result in incorrect execution, including 2025-09-07T08:19:18.4328378Z forward and backward compatibility. 2025-09-07T08:19:18.4328613Z 2025-09-07T08:19:18.4328696Z Shape: 2025-09-07T08:19:18.4329083Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-09-07T08:19:18.4329575Z `(N, S, E)` if `batch_first=True`. 2025-09-07T08:19:18.4330099Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T08:19:18.4330582Z `(N, T, E)` if `batch_first=True`. 2025-09-07T08:19:18.4330987Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-09-07T08:19:18.4331489Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-09-07T08:19:18.4331914Z - memory_mask: :math:`(T, S)`. 2025-09-07T08:19:18.4332382Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T08:19:18.4332986Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-09-07T08:19:18.4333614Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T08:19:18.4334008Z 2025-09-07T08:19:18.4334322Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-09-07T08:19:18.4334976Z positions. If a BoolTensor is provided, positions with ``True`` 2025-09-07T08:19:18.4335572Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-09-07T08:19:18.4336118Z is provided, it will be added to the attention weight. 2025-09-07T08:19:18.4336711Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-09-07T08:19:18.4337354Z the attention. If a BoolTensor is provided, the positions with the 2025-09-07T08:19:18.4338003Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-09-07T08:19:18.4338438Z 2025-09-07T08:19:18.4338709Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T08:19:18.4339200Z `(N, T, E)` if `batch_first=True`. 2025-09-07T08:19:18.4339462Z 2025-09-07T08:19:18.4339706Z Note: Due to the multi-head attention architecture in the transformer model, 2025-09-07T08:19:18.4340319Z the output sequence length of a transformer is same as the input sequence 2025-09-07T08:19:18.4340810Z (i.e. target) length of the decoder. 2025-09-07T08:19:18.4341037Z 2025-09-07T08:19:18.4341378Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-09-07T08:19:18.4341955Z batch size, :math:`E` is the feature number 2025-09-07T08:19:18.4342209Z 2025-09-07T08:19:18.4342297Z Examples: 2025-09-07T08:19:18.4342544Z >>> # xdoctest: +SKIP 2025-09-07T08:19:18.4342855Z >>> output = transformer_model( 2025-09-07T08:19:18.4343225Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-09-07T08:19:18.4343565Z ... ) 2025-09-07T08:19:18.4343789Z 2025-09-07T08:19:18.4344160Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.4344530Z 2025-09-07T08:19:18.4623971Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1766. 2025-09-07T08:19:18.4625123Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.4625503Z 2025-09-07T08:19:18.4625738Z Context manager for training with uneven inputs across processes in DDP. 2025-09-07T08:19:18.4626105Z 2025-09-07T08:19:18.4626325Z This context manager will keep track of already-joined DDP processes, 2025-09-07T08:19:18.4626871Z and "shadow" the forward and backward passes by inserting collective 2025-09-07T08:19:18.4627422Z communication operations to match with the ones created by non-joined 2025-09-07T08:19:18.4628005Z DDP processes. This will ensure each collective call has a corresponding 2025-09-07T08:19:18.4628563Z call by already-joined DDP processes, preventing hangs or errors that 2025-09-07T08:19:18.4629103Z would otherwise happen when training with uneven inputs across 2025-09-07T08:19:18.4629738Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-09-07T08:19:18.4630307Z specified to be ``True``, all trainers will throw an error once one rank 2025-09-07T08:19:18.4630820Z runs out of inputs, allowing these errors to be caught and handled 2025-09-07T08:19:18.4631245Z according to application logic. 2025-09-07T08:19:18.4631450Z 2025-09-07T08:19:18.4631664Z Once all DDP processes have joined, the context manager will broadcast 2025-09-07T08:19:18.4632224Z the model corresponding to the last joined process to all processes to 2025-09-07T08:19:18.4632713Z ensure the model is the same across all processes 2025-09-07T08:19:18.4633065Z (which is guaranteed by DDP). 2025-09-07T08:19:18.4633263Z 2025-09-07T08:19:18.4633460Z To use this to enable training with uneven inputs across processes, 2025-09-07T08:19:18.4633998Z simply wrap this context manager around your training loop. No further 2025-09-07T08:19:18.4634510Z modifications to the model or data loading is required. 2025-09-07T08:19:18.4634795Z 2025-09-07T08:19:18.4634902Z .. warning:: 2025-09-07T08:19:18.4635239Z If the model or training loop this context manager is wrapped around 2025-09-07T08:19:18.4635750Z has additional distributed collective operations, such as 2025-09-07T08:19:18.4636265Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-09-07T08:19:18.4636778Z ``throw_on_early_termination`` must be enabled. This is because this 2025-09-07T08:19:18.4637319Z context manager is not aware of non-DDP collective communication. 2025-09-07T08:19:18.4637809Z This flag will cause all ranks to throw when any one rank 2025-09-07T08:19:18.4638306Z exhausts inputs, allowing these errors to be caught and recovered 2025-09-07T08:19:18.4638736Z from across all ranks. 2025-09-07T08:19:18.4638906Z 2025-09-07T08:19:18.4639037Z Args: 2025-09-07T08:19:18.4639324Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-09-07T08:19:18.4639832Z gradients by the initial ``world_size`` DDP training was launched 2025-09-07T08:19:18.4640328Z with. If ``False``, will compute the effective world size 2025-09-07T08:19:18.4640800Z (number of ranks that have not depleted their inputs yet) and 2025-09-07T08:19:18.4641258Z divide gradients by that during allreduce. Set 2025-09-07T08:19:18.4641695Z ``divide_by_initial_world_size=True`` to ensure every input 2025-09-07T08:19:18.4642201Z sample including the uneven inputs have equal weight in terms of 2025-09-07T08:19:18.4642708Z how much they contribute to the global gradient. This is 2025-09-07T08:19:18.4643174Z achieved by always dividing the gradient by the initial 2025-09-07T08:19:18.4643638Z ``world_size`` even when we encounter uneven inputs. If you set 2025-09-07T08:19:18.4644194Z this to ``False``, we divide the gradient by the remaining 2025-09-07T08:19:18.4644689Z number of nodes. This ensures parity with training on a smaller 2025-09-07T08:19:18.4645198Z ``world_size`` although it also means the uneven inputs would 2025-09-07T08:19:18.4645716Z contribute more towards the global gradient. Typically, you 2025-09-07T08:19:18.4646219Z would want to set this to ``True`` for cases where the last few 2025-09-07T08:19:18.4646726Z inputs of your training job are uneven. In extreme cases, where 2025-09-07T08:19:18.4647238Z there is a large discrepancy in the number of inputs, setting 2025-09-07T08:19:18.4647688Z this to ``False`` might provide better results. 2025-09-07T08:19:18.4648142Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-09-07T08:19:18.4648648Z in ``enable=False`` to disable in cases where you know that 2025-09-07T08:19:18.4649126Z inputs are even across participating processes. Default is 2025-09-07T08:19:18.4649526Z ``True``. 2025-09-07T08:19:18.4649838Z throw_on_early_termination (bool): Whether to throw an error 2025-09-07T08:19:18.4650371Z or continue training when at least one rank has exhausted 2025-09-07T08:19:18.4650936Z inputs. If ``True``, will throw upon the first rank reaching end 2025-09-07T08:19:18.4651413Z of data. If ``False``, will continue training with a smaller 2025-09-07T08:19:18.4652092Z effective world size until all ranks are joined. Note that if 2025-09-07T08:19:18.4652886Z this flag is specified, then the flag 2025-09-07T08:19:18.4653327Z ``divide_by_initial_world_size`` would be ignored. Default 2025-09-07T08:19:18.4653708Z is ``False``. 2025-09-07T08:19:18.4653858Z 2025-09-07T08:19:18.4653862Z 2025-09-07T08:19:18.4653968Z Example:: 2025-09-07T08:19:18.4654086Z 2025-09-07T08:19:18.4654197Z >>> # xdoctest: +SKIP("Distributed") 2025-09-07T08:19:18.4654510Z >>> import torch 2025-09-07T08:19:18.4654789Z >>> import torch.distributed as dist 2025-09-07T08:19:18.4655107Z >>> import os 2025-09-07T08:19:18.4655361Z >>> import torch.multiprocessing as mp 2025-09-07T08:19:18.4655705Z >>> import torch.nn as nn 2025-09-07T08:19:18.4656001Z >>> # On each spawned worker 2025-09-07T08:19:18.4656295Z >>> def worker(rank): 2025-09-07T08:19:18.4656629Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-09-07T08:19:18.4657033Z >>> torch.cuda.set_device(rank) 2025-09-07T08:19:18.4657388Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T08:19:18.4657812Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-09-07T08:19:18.4658229Z >>> model, device_ids=[rank], output_device=rank 2025-09-07T08:19:18.4658571Z >>> ) 2025-09-07T08:19:18.4658831Z >>> # Rank 1 gets one more input than rank 0. 2025-09-07T08:19:18.4659257Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-09-07T08:19:18.4659715Z >>> with model.join(): 2025-09-07T08:19:18.4659989Z >>> for _ in range(5): 2025-09-07T08:19:18.4660300Z >>> for inp in inputs: 2025-09-07T08:19:18.4660629Z >>> loss = model(inp).sum() 2025-09-07T08:19:18.4660963Z >>> loss.backward() 2025-09-07T08:19:18.4661352Z >>> # Without the join() API, the below synchronization will hang 2025-09-07T08:19:18.4661802Z >>> # blocking for rank 1's allreduce to complete. 2025-09-07T08:19:18.4662185Z >>> torch.cuda.synchronize(device=rank) 2025-09-07T08:19:18.4662418Z 2025-09-07T08:19:18.4662680Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.4663044Z 2025-09-07T08:19:18.4663797Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2057. 2025-09-07T08:19:18.4664875Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.4665269Z 2025-09-07T08:19:18.4665577Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-09-07T08:19:18.4666045Z 2025-09-07T08:19:18.4666250Z Registers an optimizer with DDP such that the optimization for a 2025-09-07T08:19:18.4666782Z parameter will run immediately when that parameter's gradient is 2025-09-07T08:19:18.4667310Z finished with reduction, instead of waiting for all parameters' 2025-09-07T08:19:18.4667833Z gradients to finish reduction. This can result in a training speedup 2025-09-07T08:19:18.4668385Z depending on your workload since the optimizer can run while gradient 2025-09-07T08:19:18.4668949Z reduction for other parameters are still ongoing. In addition, this has 2025-09-07T08:19:18.4669527Z the potential to reduce peak memory consumption during training, as it 2025-09-07T08:19:18.4670071Z only needs to load the per-parameter optimizer states of a single 2025-09-07T08:19:18.4670589Z parameter at a time, instead of loading all per-parameter optimizer 2025-09-07T08:19:18.4671007Z states at once. 2025-09-07T08:19:18.4671141Z 2025-09-07T08:19:18.4671306Z Args: 2025-09-07T08:19:18.4671622Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-09-07T08:19:18.4672023Z as a fused optimizer. 2025-09-07T08:19:18.4672361Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-09-07T08:19:18.4672854Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-09-07T08:19:18.4673570Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-09-07T08:19:18.4674107Z Optimizers. If this is omitted, all DDP model parameters will be 2025-09-07T08:19:18.4674517Z optimized. 2025-09-07T08:19:18.4674855Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-09-07T08:19:18.4675164Z 2025-09-07T08:19:18.4675268Z .. warning :: 2025-09-07T08:19:18.4675614Z _register_fused_optim should only be called once on a DDP instance, 2025-09-07T08:19:18.4676144Z and registering multiple fused optimizers for the same DDP model 2025-09-07T08:19:18.4676603Z is not currently supported. Please ping 2025-09-07T08:19:18.4677082Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T08:19:18.4677525Z for your use case. 2025-09-07T08:19:18.4677685Z 2025-09-07T08:19:18.4677772Z .. warning :: 2025-09-07T08:19:18.4678095Z _register_fused_optim and register_comm_hook currently do not 2025-09-07T08:19:18.4678619Z compose together, meaning that custom DDP communication hooks are 2025-09-07T08:19:18.4679126Z not supported with overlapped optimizers. Please ping 2025-09-07T08:19:18.4679623Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T08:19:18.4680059Z for your use case. 2025-09-07T08:19:18.4690676Z 2025-09-07T08:19:18.4690823Z .. warning :: 2025-09-07T08:19:18.4691217Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-09-07T08:19:18.4691815Z with overlapped optimizer. Please ping 2025-09-07T08:19:18.4692306Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T08:19:18.4692749Z for your use case. 2025-09-07T08:19:18.4692921Z 2025-09-07T08:19:18.4693013Z Example:: 2025-09-07T08:19:18.4693146Z 2025-09-07T08:19:18.4693276Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-09-07T08:19:18.4693827Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-09-07T08:19:18.4694456Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-09-07T08:19:18.4694846Z >>> lr = 1e-2 2025-09-07T08:19:18.4695088Z >>> betas = (0.9, 0.99) 2025-09-07T08:19:18.4695355Z >>> eps = 1e-6 2025-09-07T08:19:18.4695728Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-09-07T08:19:18.4696183Z >>> # Example with subset of parameters 2025-09-07T08:19:18.4696562Z >>> params_to_opt = [list(net.parameters())[0]] 2025-09-07T08:19:18.4697018Z >>> net._register_fused_optim( 2025-09-07T08:19:18.4697460Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-09-07T08:19:18.4697953Z ... ) 2025-09-07T08:19:18.4698087Z 2025-09-07T08:19:18.4698340Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.4698722Z 2025-09-07T08:19:18.5157334Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=14. 2025-09-07T08:19:18.5158377Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.5158985Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-09-07T08:19:18.5159320Z 2025-09-07T08:19:18.5159608Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T08:19:18.5160273Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T08:19:18.5161089Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T08:19:18.5161793Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T08:19:18.5162295Z 2025-09-07T08:19:18.5162459Z .. note:: 2025-09-07T08:19:18.5162909Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-09-07T08:19:18.5163760Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-09-07T08:19:18.5164643Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T08:19:18.5165209Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T08:19:18.5165782Z One place we are confident in is that NHWC(channels_last) conversion for 2025-09-07T08:19:18.5166337Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-09-07T08:19:18.5166895Z even in cases where we have to apply permutation to input tensors. 2025-09-07T08:19:18.5167230Z 2025-09-07T08:19:18.5167460Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T08:19:18.5167931Z channels_last. This ensures that; 2025-09-07T08:19:18.5168379Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T08:19:18.5168941Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T08:19:18.5169537Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T08:19:18.5170012Z from memory_format conversion. 2025-09-07T08:19:18.5170225Z 2025-09-07T08:19:18.5170460Z The optimal case is that, layers between convolution layers are channels 2025-09-07T08:19:18.5171050Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T08:19:18.5171628Z encounters the first convolution layer and stay in that memory format. 2025-09-07T08:19:18.5172306Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T08:19:18.5172687Z 2025-09-07T08:19:18.5172911Z In case where a channels last incompatible layer is between convolution 2025-09-07T08:19:18.5173615Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T08:19:18.5174166Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T08:19:18.5174747Z contiguous format and be permuted to channels last when it encounters 2025-09-07T08:19:18.5175314Z another convolution layer. There's no point in propagating that 2025-09-07T08:19:18.5175867Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T08:19:18.5176344Z ``memory_format``. 2025-09-07T08:19:18.5176513Z 2025-09-07T08:19:18.5176757Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T08:19:18.5177322Z there might have been a better spot to fuse the permutation other than 2025-09-07T08:19:18.5177788Z immediately before a convolution. 2025-09-07T08:19:18.5178035Z 2025-09-07T08:19:18.5178167Z Args: 2025-09-07T08:19:18.5178509Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-09-07T08:19:18.5178951Z ``nn.Module`` 2025-09-07T08:19:18.5179303Z memory_format: user specified ``memory_format``, 2025-09-07T08:19:18.5179754Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T08:19:18.5180064Z 2025-09-07T08:19:18.5180149Z Returns: 2025-09-07T08:19:18.5180432Z The original module with updated ``nn.Conv2d`` 2025-09-07T08:19:18.5180682Z 2025-09-07T08:19:18.5180772Z Example: 2025-09-07T08:19:18.5181044Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:18.5181455Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T08:19:18.5181841Z >>> input = torch.randint( 2025-09-07T08:19:18.5182187Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T08:19:18.5182625Z ... ) 2025-09-07T08:19:18.5182867Z >>> model = nn.Sequential( 2025-09-07T08:19:18.5183183Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-09-07T08:19:18.5183505Z >>> # This is identical to: 2025-09-07T08:19:18.5183958Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-09-07T08:19:18.5184499Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-09-07T08:19:18.5184900Z ... model, torch.channels_last 2025-09-07T08:19:18.5185211Z ... ) 2025-09-07T08:19:18.5185431Z >>> out = model(input) 2025-09-07T08:19:18.5185698Z 2025-09-07T08:19:18.5186070Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.5186442Z 2025-09-07T08:19:18.5187101Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=93. 2025-09-07T08:19:18.5188128Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.5188738Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-09-07T08:19:18.5189351Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T08:19:18.5190030Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T08:19:18.5190692Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T08:19:18.5191373Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T08:19:18.5191815Z 2025-09-07T08:19:18.5191911Z .. note:: 2025-09-07T08:19:18.5192295Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-09-07T08:19:18.5192936Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-09-07T08:19:18.5193506Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T08:19:18.5194061Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T08:19:18.5194652Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-09-07T08:19:18.5195245Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-09-07T08:19:18.5195801Z even in cases where we have to apply permutation to input tensors. 2025-09-07T08:19:18.5196123Z 2025-09-07T08:19:18.5196348Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T08:19:18.5196823Z channels_last_3d. This ensures that; 2025-09-07T08:19:18.5197272Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T08:19:18.5197848Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T08:19:18.5198446Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T08:19:18.5198912Z from memory_format conversion. 2025-09-07T08:19:18.5199164Z 2025-09-07T08:19:18.5199383Z The optimal case is that, layers between convolution layers are channels 2025-09-07T08:19:18.5199973Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T08:19:18.5200567Z encounters the first convolution layer and stay in that memory format. 2025-09-07T08:19:18.5201163Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T08:19:18.5201519Z 2025-09-07T08:19:18.5201738Z In case where a channels last incompatible layer is between convolution 2025-09-07T08:19:18.5202301Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T08:19:18.5202868Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T08:19:18.5203448Z contiguous format and be permuted to channels last when it encounters 2025-09-07T08:19:18.5204062Z another convolution layer. There's no point in propagating that 2025-09-07T08:19:18.5204703Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T08:19:18.5205146Z ``memory_format``. 2025-09-07T08:19:18.5205319Z 2025-09-07T08:19:18.5205568Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T08:19:18.5206148Z there might have been a better spot to fuse the permutation other than 2025-09-07T08:19:18.5206598Z immediately before a convolution. 2025-09-07T08:19:18.5206837Z 2025-09-07T08:19:18.5206919Z Args: 2025-09-07T08:19:18.5207258Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-09-07T08:19:18.5207695Z ``nn.Module`` 2025-09-07T08:19:18.5208047Z memory_format: user specified ``memory_format``, 2025-09-07T08:19:18.5208494Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T08:19:18.5208799Z 2025-09-07T08:19:18.5208886Z Returns: 2025-09-07T08:19:18.5209169Z The original module with updated ``nn.Conv3d`` 2025-09-07T08:19:18.5209418Z 2025-09-07T08:19:18.5209516Z Example: 2025-09-07T08:19:18.5209772Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:18.5210179Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T08:19:18.5210556Z >>> input = torch.randint( 2025-09-07T08:19:18.5210913Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T08:19:18.5211262Z ... ) 2025-09-07T08:19:18.5211496Z >>> model = nn.Sequential( 2025-09-07T08:19:18.5211816Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-09-07T08:19:18.5212153Z >>> # This is identical to: 2025-09-07T08:19:18.5212599Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-09-07T08:19:18.5213184Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-09-07T08:19:18.5213592Z ... model, torch.channels_last_3d 2025-09-07T08:19:18.5213911Z ... ) 2025-09-07T08:19:18.5214133Z >>> out = model(input) 2025-09-07T08:19:18.5214415Z 2025-09-07T08:19:18.5214787Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.5215157Z 2025-09-07T08:19:18.5284042Z msg = Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py line=424. 2025-09-07T08:19:18.5285748Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.5286674Z Register a parametrization to a tensor in a module. 2025-09-07T08:19:18.5287140Z 2025-09-07T08:19:18.5287613Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-09-07T08:19:18.5288764Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-09-07T08:19:18.5289894Z If the original tensor requires a gradient, the backward pass will differentiate 2025-09-07T08:19:18.5291197Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-09-07T08:19:18.5291892Z 2025-09-07T08:19:18.5292367Z The first time that a module registers a parametrization, this function will add an attribute 2025-09-07T08:19:18.5293167Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-09-07T08:19:18.5293537Z 2025-09-07T08:19:18.5293805Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-09-07T08:19:18.5294323Z ``module.parametrizations.weight``. 2025-09-07T08:19:18.5294554Z 2025-09-07T08:19:18.5294691Z The original tensor will be accessible under 2025-09-07T08:19:18.5295091Z ``module.parametrizations.weight.original``. 2025-09-07T08:19:18.5295361Z 2025-09-07T08:19:18.5295630Z Parametrizations may be concatenated by registering several parametrizations 2025-09-07T08:19:18.5296121Z on the same attribute. 2025-09-07T08:19:18.5296425Z 2025-09-07T08:19:18.5296685Z The training mode of a registered parametrization is updated on registration 2025-09-07T08:19:18.5297182Z to match the training mode of the host module 2025-09-07T08:19:18.5297435Z 2025-09-07T08:19:18.5297742Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-09-07T08:19:18.5298308Z using the context manager :func:`cached`. 2025-09-07T08:19:18.5298539Z 2025-09-07T08:19:18.5298796Z A :attr:`parametrization` may optionally implement a method with signature 2025-09-07T08:19:18.5299159Z 2025-09-07T08:19:18.5299285Z .. code-block:: python 2025-09-07T08:19:18.5299456Z 2025-09-07T08:19:18.5299673Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-09-07T08:19:18.5300020Z 2025-09-07T08:19:18.5300282Z This method is called on the unparametrized tensor when the first parametrization 2025-09-07T08:19:18.5300890Z is registered to compute the initial value of the original tensor. 2025-09-07T08:19:18.5301630Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-09-07T08:19:18.5302094Z 2025-09-07T08:19:18.5302420Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-09-07T08:19:18.5303155Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-09-07T08:19:18.5303569Z 2025-09-07T08:19:18.5303797Z It is possible for the first parametrization to depend on several inputs. 2025-09-07T08:19:18.5304394Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-09-07T08:19:18.5305007Z (see the example implementation of a ``RankOne`` parametrization below). 2025-09-07T08:19:18.5305361Z 2025-09-07T08:19:18.5305712Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-09-07T08:19:18.5306362Z with names ``original0``, ``original1``,... 2025-09-07T08:19:18.5306603Z 2025-09-07T08:19:18.5306694Z .. note:: 2025-09-07T08:19:18.5306837Z 2025-09-07T08:19:18.5307109Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-09-07T08:19:18.5307661Z once to perform a number of consistency checks. 2025-09-07T08:19:18.5308196Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-09-07T08:19:18.5308715Z and nothing will be called otherwise. 2025-09-07T08:19:18.5308944Z 2025-09-07T08:19:18.5309027Z .. note:: 2025-09-07T08:19:18.5309163Z 2025-09-07T08:19:18.5309366Z In most situations, ``right_inverse`` will be a function such that 2025-09-07T08:19:18.5309817Z ``forward(right_inverse(X)) == X`` (see 2025-09-07T08:19:18.5310342Z `right inverse `_). 2025-09-07T08:19:18.5311007Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-09-07T08:19:18.5311473Z to relax this. 2025-09-07T08:19:18.5311670Z 2025-09-07T08:19:18.5311757Z .. warning:: 2025-09-07T08:19:18.5311892Z 2025-09-07T08:19:18.5312181Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-09-07T08:19:18.5312843Z will register a number of new parameters. If such parametrization is registered 2025-09-07T08:19:18.5313487Z after the optimizer is created, these new parameters will need to be added manually 2025-09-07T08:19:18.5314083Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-09-07T08:19:18.5314406Z 2025-09-07T08:19:18.5314488Z Args: 2025-09-07T08:19:18.5314827Z module (nn.Module): module on which to register the parametrization 2025-09-07T08:19:18.5315378Z tensor_name (str): name of the parameter or buffer on which to register 2025-09-07T08:19:18.5315812Z the parametrization 2025-09-07T08:19:18.5316267Z parametrization (nn.Module): the parametrization to register 2025-09-07T08:19:18.5316682Z Keyword args: 2025-09-07T08:19:18.5317044Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-09-07T08:19:18.5317564Z may change the dtype and shape of the tensor. Default: `False` 2025-09-07T08:19:18.5318152Z Warning: the parametrization is not checked for consistency upon registration. 2025-09-07T08:19:18.5318671Z Enable this flag at your own risk. 2025-09-07T08:19:18.5318895Z 2025-09-07T08:19:18.5318990Z Raises: 2025-09-07T08:19:18.5319405Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-09-07T08:19:18.5319816Z 2025-09-07T08:19:18.5319903Z Examples: 2025-09-07T08:19:18.5320189Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T08:19:18.5320558Z >>> import torch 2025-09-07T08:19:18.5320831Z >>> import torch.nn as nn 2025-09-07T08:19:18.5321160Z >>> import torch.nn.utils.parametrize as P 2025-09-07T08:19:18.5321499Z >>> 2025-09-07T08:19:18.5321734Z >>> class Symmetric(nn.Module): 2025-09-07T08:19:18.5322055Z >>> def forward(self, X): 2025-09-07T08:19:18.5322422Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-09-07T08:19:18.5322808Z >>> 2025-09-07T08:19:18.5323042Z >>> def right_inverse(self, A): 2025-09-07T08:19:18.5323361Z >>> return A.triu() 2025-09-07T08:19:18.5323628Z >>> 2025-09-07T08:19:18.5323846Z >>> m = nn.Linear(5, 5) 2025-09-07T08:19:18.5324287Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-09-07T08:19:18.5324822Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-09-07T08:19:18.5325268Z True 2025-09-07T08:19:18.5325511Z >>> A = torch.rand(5, 5) 2025-09-07T08:19:18.5325860Z >>> A = A + A.T # A is now symmetric 2025-09-07T08:19:18.5326292Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-09-07T08:19:18.5326734Z >>> print(torch.allclose(m.weight, A)) 2025-09-07T08:19:18.5327062Z True 2025-09-07T08:19:18.5327200Z 2025-09-07T08:19:18.5327309Z >>> class RankOne(nn.Module): 2025-09-07T08:19:18.5327628Z >>> def forward(self, x, y): 2025-09-07T08:19:18.5327977Z >>> # Form a rank 1 matrix multiplying two vectors 2025-09-07T08:19:18.5328377Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-09-07T08:19:18.5328712Z >>> 2025-09-07T08:19:18.5328952Z >>> def right_inverse(self, Z): 2025-09-07T08:19:18.5329292Z >>> # Project Z onto the rank 1 matrices 2025-09-07T08:19:18.5329669Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-09-07T08:19:18.5330063Z >>> # Return rescaled singular vectors 2025-09-07T08:19:18.5330419Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-09-07T08:19:18.5330811Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-09-07T08:19:18.5331182Z >>> 2025-09-07T08:19:18.5331457Z >>> linear_rank_one = P.register_parametrization( 2025-09-07T08:19:18.5331851Z ... nn.Linear(4, 4), "weight", RankOne() 2025-09-07T08:19:18.5332174Z ... ) 2025-09-07T08:19:18.5332499Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-09-07T08:19:18.5332905Z 1 2025-09-07T08:19:18.5333032Z 2025-09-07T08:19:18.5333113Z 2025-09-07T08:19:18.5333479Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.5333847Z 2025-09-07T08:19:18.5401691Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=979. 2025-09-07T08:19:18.5402578Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.5403424Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-09-07T08:19:18.5403872Z 2025-09-07T08:19:18.5404181Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-09-07T08:19:18.5404758Z by removing the specified ``amount`` of (currently unpruned) channels 2025-09-07T08:19:18.5405272Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-09-07T08:19:18.5405750Z Modifies module in place (and also return the modified module) 2025-09-07T08:19:18.5406321Z by: 2025-09-07T08:19:18.5406456Z 2025-09-07T08:19:18.5406665Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T08:19:18.5407424Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T08:19:18.5407969Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T08:19:18.5408506Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T08:19:18.5408929Z ``name+'_orig'``. 2025-09-07T08:19:18.5409089Z 2025-09-07T08:19:18.5409189Z Args: 2025-09-07T08:19:18.5409490Z module (nn.Module): module containing the tensor to prune 2025-09-07T08:19:18.5409952Z name (str): parameter name within ``module`` on which pruning 2025-09-07T08:19:18.5410344Z will act. 2025-09-07T08:19:18.5410777Z amount (int or float): quantity of parameters to prune. 2025-09-07T08:19:18.5411324Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T08:19:18.5411999Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T08:19:18.5412456Z absolute number of parameters to prune. 2025-09-07T08:19:18.5412901Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-09-07T08:19:18.5413373Z entries for argument ``p`` in :func:`torch.norm`. 2025-09-07T08:19:18.5413916Z dim (int): index of the dim along which we define channels to prune. 2025-09-07T08:19:18.5414484Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-09-07T08:19:18.5415041Z shape as module parameter) used to compute mask for pruning. 2025-09-07T08:19:18.5415580Z The values in this tensor indicate the importance of the corresponding 2025-09-07T08:19:18.5416060Z elements in the parameter being pruned. 2025-09-07T08:19:18.5416517Z If unspecified or None, the module parameter will be used in its place. 2025-09-07T08:19:18.5416902Z 2025-09-07T08:19:18.5416986Z Returns: 2025-09-07T08:19:18.5417342Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T08:19:18.5417689Z 2025-09-07T08:19:18.5417775Z Examples: 2025-09-07T08:19:18.5418029Z >>> from torch.nn.utils import prune 2025-09-07T08:19:18.5418367Z >>> m = prune.ln_structured( 2025-09-07T08:19:18.5418749Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-09-07T08:19:18.5419141Z ... ) 2025-09-07T08:19:18.5419352Z 2025-09-07T08:19:18.5419720Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.5420159Z 2025-09-07T08:19:18.5420706Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1026. 2025-09-07T08:19:18.5421626Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.5422012Z 2025-09-07T08:19:18.5422445Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-09-07T08:19:18.5422986Z 2025-09-07T08:19:18.5423112Z Modifies modules in place by: 2025-09-07T08:19:18.5423301Z 2025-09-07T08:19:18.5423520Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T08:19:18.5424049Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T08:19:18.5424598Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T08:19:18.5425193Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T08:19:18.5425615Z ``name+'_orig'``. 2025-09-07T08:19:18.5425765Z 2025-09-07T08:19:18.5425856Z Args: 2025-09-07T08:19:18.5426165Z parameters (Iterable of (module, name) tuples): parameters of 2025-09-07T08:19:18.5426673Z the model to prune in a global fashion, i.e. by aggregating all 2025-09-07T08:19:18.5427196Z weights prior to deciding which ones to prune. module must be of 2025-09-07T08:19:18.5427675Z type :class:`nn.Module`, and name must be a string. 2025-09-07T08:19:18.5428156Z pruning_method (function): a valid pruning function from this module, 2025-09-07T08:19:18.5428674Z or a custom one implemented by the user that satisfies the 2025-09-07T08:19:18.5429199Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-09-07T08:19:18.5429785Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-09-07T08:19:18.5430353Z the corresponding parameter's importance scores tensor. The tensor 2025-09-07T08:19:18.5430909Z should be the same shape as the parameter, and is used for computing 2025-09-07T08:19:18.5431336Z mask for pruning. 2025-09-07T08:19:18.5431719Z If unspecified or None, the parameter will be used in place of its 2025-09-07T08:19:18.5432143Z importance scores. 2025-09-07T08:19:18.5432433Z kwargs: other keyword arguments such as: 2025-09-07T08:19:18.5432864Z amount (int or float): quantity of parameters to prune across the 2025-09-07T08:19:18.5433284Z specified parameters. 2025-09-07T08:19:18.5433648Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T08:19:18.5434127Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T08:19:18.5434604Z absolute number of parameters to prune. 2025-09-07T08:19:18.5434855Z 2025-09-07T08:19:18.5434939Z Raises: 2025-09-07T08:19:18.5435214Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-09-07T08:19:18.5435472Z 2025-09-07T08:19:18.5435551Z Note: 2025-09-07T08:19:18.5435897Z Since global structured pruning doesn't make much sense unless the 2025-09-07T08:19:18.5436430Z norm is normalized by the size of the parameter, we now limit the 2025-09-07T08:19:18.5436888Z scope of global pruning to unstructured methods. 2025-09-07T08:19:18.5437163Z 2025-09-07T08:19:18.5437248Z Examples: 2025-09-07T08:19:18.5437491Z >>> from torch.nn.utils import prune 2025-09-07T08:19:18.5437839Z >>> from collections import OrderedDict 2025-09-07T08:19:18.5438155Z >>> net = nn.Sequential( 2025-09-07T08:19:18.5438432Z ... OrderedDict( 2025-09-07T08:19:18.5438677Z ... [ 2025-09-07T08:19:18.5438932Z ... ("first", nn.Linear(10, 4)), 2025-09-07T08:19:18.5439265Z ... ("second", nn.Linear(4, 1)), 2025-09-07T08:19:18.5439574Z ... ] 2025-09-07T08:19:18.5439794Z ... ) 2025-09-07T08:19:18.5440010Z ... ) 2025-09-07T08:19:18.5440252Z >>> parameters_to_prune = ( 2025-09-07T08:19:18.5440545Z ... (net.first, "weight"), 2025-09-07T08:19:18.5440851Z ... (net.second, "weight"), 2025-09-07T08:19:18.5441137Z ... ) 2025-09-07T08:19:18.5441358Z >>> prune.global_unstructured( 2025-09-07T08:19:18.5441670Z ... parameters_to_prune, 2025-09-07T08:19:18.5441997Z ... pruning_method=prune.L1Unstructured, 2025-09-07T08:19:18.5442332Z ... amount=10, 2025-09-07T08:19:18.5442556Z ... ) 2025-09-07T08:19:18.5442893Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-09-07T08:19:18.5443318Z tensor(10) 2025-09-07T08:19:18.5443447Z 2025-09-07T08:19:18.5443451Z 2025-09-07T08:19:18.5443714Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.5444166Z 2025-09-07T08:19:18.5444764Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1149. 2025-09-07T08:19:18.5445658Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.5446436Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-09-07T08:19:18.5446958Z 2025-09-07T08:19:18.5447171Z Modifies module in place (and also return the modified module) by: 2025-09-07T08:19:18.5447498Z 2025-09-07T08:19:18.5447719Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T08:19:18.5448269Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T08:19:18.5448807Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T08:19:18.5449348Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T08:19:18.5449770Z ``name+'_orig'``. 2025-09-07T08:19:18.5449933Z 2025-09-07T08:19:18.5450031Z Args: 2025-09-07T08:19:18.5450324Z module (nn.Module): module containing the tensor to prune 2025-09-07T08:19:18.5450804Z name (str): parameter name within ``module`` on which pruning 2025-09-07T08:19:18.5451193Z will act. 2025-09-07T08:19:18.5451522Z mask (Tensor): binary mask to be applied to the parameter. 2025-09-07T08:19:18.5451808Z 2025-09-07T08:19:18.5451902Z Returns: 2025-09-07T08:19:18.5452241Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T08:19:18.5452586Z 2025-09-07T08:19:18.5452672Z Examples: 2025-09-07T08:19:18.5452926Z >>> from torch.nn.utils import prune 2025-09-07T08:19:18.5453265Z >>> m = prune.custom_from_mask( 2025-09-07T08:19:18.5453639Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-09-07T08:19:18.5454012Z ... ) 2025-09-07T08:19:18.5454273Z >>> print(m.bias_mask) 2025-09-07T08:19:18.5454553Z tensor([0., 1., 0.]) 2025-09-07T08:19:18.5454724Z 2025-09-07T08:19:18.5454819Z 2025-09-07T08:19:18.5455196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.5455567Z 2025-09-07T08:19:18.5468528Z msg = Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py line=350. 2025-09-07T08:19:18.5469452Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.5469988Z Pad a packed batch of variable length sequences. 2025-09-07T08:19:18.5470240Z 2025-09-07T08:19:18.5470416Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-09-07T08:19:18.5470719Z 2025-09-07T08:19:18.5471002Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-09-07T08:19:18.5471652Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-09-07T08:19:18.5472145Z sequence and ``B`` is the batch size. 2025-09-07T08:19:18.5472365Z 2025-09-07T08:19:18.5472470Z Example: 2025-09-07T08:19:18.5472889Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-09-07T08:19:18.5473604Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-09-07T08:19:18.5473973Z >>> lens = [2, 1, 3] 2025-09-07T08:19:18.5474271Z >>> packed = pack_padded_sequence( 2025-09-07T08:19:18.5474641Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-09-07T08:19:18.5475001Z ... ) 2025-09-07T08:19:18.5475219Z >>> packed 2025-09-07T08:19:18.5475606Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-09-07T08:19:18.5476183Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-09-07T08:19:18.5476793Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-09-07T08:19:18.5477267Z >>> seq_unpacked 2025-09-07T08:19:18.5477530Z tensor([[1, 2, 0], 2025-09-07T08:19:18.5477898Z [3, 0, 0], 2025-09-07T08:19:18.5478142Z [4, 5, 6]]) 2025-09-07T08:19:18.5478403Z >>> lens_unpacked 2025-09-07T08:19:18.5478661Z tensor([2, 1, 3]) 2025-09-07T08:19:18.5478822Z 2025-09-07T08:19:18.5478926Z .. note:: 2025-09-07T08:19:18.5479187Z :attr:`total_length` is useful to implement the 2025-09-07T08:19:18.5479668Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-09-07T08:19:18.5480231Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-09-07T08:19:18.5480809Z See :ref:`this FAQ section ` for 2025-09-07T08:19:18.5481239Z details. 2025-09-07T08:19:18.5481389Z 2025-09-07T08:19:18.5481471Z Args: 2025-09-07T08:19:18.5481724Z sequence (PackedSequence): batch to pad 2025-09-07T08:19:18.5482206Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-09-07T08:19:18.5482692Z format, ``T x B x *`` otherwise. 2025-09-07T08:19:18.5483099Z padding_value (float, optional): values for padded elements. 2025-09-07T08:19:18.5483644Z total_length (int, optional): if not ``None``, the output will be padded to 2025-09-07T08:19:18.5484308Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-09-07T08:19:18.5484862Z if :attr:`total_length` is less than the max sequence length in 2025-09-07T08:19:18.5485259Z :attr:`sequence`. 2025-09-07T08:19:18.5485451Z 2025-09-07T08:19:18.5485536Z Returns: 2025-09-07T08:19:18.5485856Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-09-07T08:19:18.5486363Z containing the list of lengths of each sequence in the batch. 2025-09-07T08:19:18.5486905Z Batch elements will be re-ordered as they were ordered originally when 2025-09-07T08:19:18.5487511Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-09-07T08:19:18.5487941Z 2025-09-07T08:19:18.5488314Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.5488682Z 2025-09-07T08:19:18.6291122Z msg = Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=811. 2025-09-07T08:19:18.6292067Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.6292773Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-09-07T08:19:18.6293244Z 2025-09-07T08:19:18.6293614Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-09-07T08:19:18.6294335Z intervals by which each scheduler should be called at a given epoch. 2025-09-07T08:19:18.6294692Z 2025-09-07T08:19:18.6294775Z Args: 2025-09-07T08:19:18.6295037Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:18.6295428Z schedulers (list): List of chained schedulers. 2025-09-07T08:19:18.6296086Z milestones (list): List of integers that reflects milestone points. 2025-09-07T08:19:18.6296603Z last_epoch (int): The index of last epoch. Default: -1. 2025-09-07T08:19:18.6296882Z 2025-09-07T08:19:18.6296981Z Example: 2025-09-07T08:19:18.6297205Z >>> # xdoctest: +SKIP 2025-09-07T08:19:18.6297549Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-09-07T08:19:18.6297924Z >>> # lr = 0.005 if epoch == 0 2025-09-07T08:19:18.6298244Z >>> # lr = 0.005 if epoch == 1 2025-09-07T08:19:18.6298559Z >>> # lr = 0.005 if epoch == 2 2025-09-07T08:19:18.6298844Z >>> # ... 2025-09-07T08:19:18.6299091Z >>> # lr = 0.05 if epoch == 20 2025-09-07T08:19:18.6299414Z >>> # lr = 0.045 if epoch == 21 2025-09-07T08:19:18.6299737Z >>> # lr = 0.0405 if epoch == 22 2025-09-07T08:19:18.6300264Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-09-07T08:19:18.6300745Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-09-07T08:19:18.6301126Z >>> scheduler = SequentialLR( 2025-09-07T08:19:18.6301433Z ... optimizer, 2025-09-07T08:19:18.6301565Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T08:19:18.6301667Z ... milestones=[20], 2025-09-07T08:19:18.6301762Z ... ) 2025-09-07T08:19:18.6301865Z >>> for epoch in range(100): 2025-09-07T08:19:18.6301968Z >>> train(...) 2025-09-07T08:19:18.6302061Z >>> validate(...) 2025-09-07T08:19:18.6302159Z >>> scheduler.step() 2025-09-07T08:19:18.6302164Z 2025-09-07T08:19:18.6302365Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-09-07T08:19:18.6302443Z 2025-09-07T08:19:18.6302708Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.6302713Z 2025-09-07T08:19:18.6326523Z msg = Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1236. 2025-09-07T08:19:18.6326792Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:18.6326972Z Reduce learning rate when a metric has stopped improving. 2025-09-07T08:19:18.6326978Z 2025-09-07T08:19:18.6327183Z Models often benefit from reducing the learning rate by a factor 2025-09-07T08:19:18.6327384Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-09-07T08:19:18.6327577Z quantity and if no improvement is seen for a 'patience' number 2025-09-07T08:19:18.6327711Z of epochs, the learning rate is reduced. 2025-09-07T08:19:18.6327716Z 2025-09-07T08:19:18.6327798Z Args: 2025-09-07T08:19:18.6327931Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:18.6328197Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-09-07T08:19:18.6328357Z be reduced when the quantity monitored has stopped 2025-09-07T08:19:18.6328535Z decreasing; in `max` mode it will be reduced when the 2025-09-07T08:19:18.6328725Z quantity monitored has stopped increasing. Default: 'min'. 2025-09-07T08:19:18.6328892Z factor (float): Factor by which the learning rate will be 2025-09-07T08:19:18.6329039Z reduced. new_lr = lr * factor. Default: 0.1. 2025-09-07T08:19:18.6329255Z patience (int): The number of allowed epochs with no improvement after 2025-09-07T08:19:18.6329397Z which the learning rate will be reduced. 2025-09-07T08:19:18.6329623Z For example, consider the case of having no patience (`patience = 0`). 2025-09-07T08:19:18.6330018Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-09-07T08:19:18.6330225Z In the second epoch, if the performance is worse than the baseline, 2025-09-07T08:19:18.6330381Z we have what is considered an intolerable epoch. 2025-09-07T08:19:18.6330699Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-09-07T08:19:18.6330869Z the learning rate is reduced at the end of this epoch. 2025-09-07T08:19:18.6331201Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-09-07T08:19:18.6331515Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-09-07T08:19:18.6331644Z the learning rate is not adjusted. 2025-09-07T08:19:18.6331738Z Default: 10. 2025-09-07T08:19:18.6331929Z threshold (float): Threshold for measuring the new optimum, 2025-09-07T08:19:18.6332103Z to only focus on significant changes. Default: 1e-4. 2025-09-07T08:19:18.6332277Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-09-07T08:19:18.6332513Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-09-07T08:19:18.6332659Z mode or best * ( 1 - threshold ) in `min` mode. 2025-09-07T08:19:18.6332814Z In `abs` mode, dynamic_threshold = best + threshold in 2025-09-07T08:19:18.6333006Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-09-07T08:19:18.6333174Z cooldown (int): Number of epochs to wait before resuming 2025-09-07T08:19:18.6333365Z normal operation after lr has been reduced. Default: 0. 2025-09-07T08:19:18.6333524Z min_lr (float or list): A scalar or a list of scalars. A 2025-09-07T08:19:18.6333685Z lower bound on the learning rate of all param groups 2025-09-07T08:19:18.6333833Z or each group respectively. Default: 0. 2025-09-07T08:19:18.6334008Z eps (float): Minimal decay applied to lr. If the difference 2025-09-07T08:19:18.6334203Z between new and old lr is smaller than eps, the update is 2025-09-07T08:19:18.6334309Z ignored. Default: 1e-8. 2025-09-07T08:19:18.6334317Z 2025-09-07T08:19:18.6334406Z Example: 2025-09-07T08:19:18.6334520Z >>> # xdoctest: +SKIP 2025-09-07T08:19:18.6334747Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T08:19:18.6334951Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-09-07T08:19:18.6335119Z >>> for epoch in range(10): 2025-09-07T08:19:18.6335268Z >>> train(...) 2025-09-07T08:19:18.6335441Z >>> val_loss = validate(...) 2025-09-07T08:19:18.6335673Z >>> # Note that step should be called after validate() 2025-09-07T08:19:18.6335844Z >>> scheduler.step(val_loss) 2025-09-07T08:19:18.6335850Z 2025-09-07T08:19:18.6336053Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-09-07T08:19:18.6336143Z 2025-09-07T08:19:18.6336923Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-09-07T08:19:18.6336933Z 2025-09-07T08:19:18.6337140Z scheduler.step(val_loss) 2025-09-07T08:19:18.6337314Z ^ 2025-09-07T08:19:18.6337977Z msg = Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1433. 2025-09-07T08:19:18.6338255Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.6338582Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-09-07T08:19:18.6338587Z 2025-09-07T08:19:18.6338878Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-09-07T08:19:18.6339135Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-09-07T08:19:18.6339380Z The distance between the two boundaries can be scaled on a per-iteration 2025-09-07T08:19:18.6339481Z or per-cycle basis. 2025-09-07T08:19:18.6339485Z 2025-09-07T08:19:18.6339728Z Cyclical learning rate policy changes the learning rate after every batch. 2025-09-07T08:19:18.6339972Z `step` should be called after a batch has been used for training. 2025-09-07T08:19:18.6339976Z 2025-09-07T08:19:18.6340183Z This class has three built-in policies, as put forth in the paper: 2025-09-07T08:19:18.6340188Z 2025-09-07T08:19:18.6340402Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-09-07T08:19:18.6340698Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-09-07T08:19:18.6341016Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-09-07T08:19:18.6341129Z at each cycle iteration. 2025-09-07T08:19:18.6341133Z 2025-09-07T08:19:18.6341368Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-09-07T08:19:18.6341375Z 2025-09-07T08:19:18.6341471Z Args: 2025-09-07T08:19:18.6341602Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:18.6341849Z base_lr (float or list): Initial learning rate which is the 2025-09-07T08:19:18.6342019Z lower boundary in the cycle for each parameter group. 2025-09-07T08:19:18.6342213Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T08:19:18.6342352Z for each parameter group. Functionally, 2025-09-07T08:19:18.6342509Z it defines the cycle amplitude (max_lr - base_lr). 2025-09-07T08:19:18.6342647Z The lr at any cycle is the sum of base_lr 2025-09-07T08:19:18.6342783Z and some scaling of the amplitude; therefore 2025-09-07T08:19:18.6342925Z max_lr may not actually be reached depending on 2025-09-07T08:19:18.6343037Z scaling function. 2025-09-07T08:19:18.6343202Z step_size_up (int): Number of training iterations in the 2025-09-07T08:19:18.6343424Z increasing half of a cycle. Default: 2000 2025-09-07T08:19:18.6343661Z step_size_down (int): Number of training iterations in the 2025-09-07T08:19:18.6343833Z decreasing half of a cycle. If step_size_down is None, 2025-09-07T08:19:18.6344031Z it is set to step_size_up. Default: None 2025-09-07T08:19:18.6344273Z mode (str): One of {triangular, triangular2, exp_range}. 2025-09-07T08:19:18.6344465Z Values correspond to policies detailed above. 2025-09-07T08:19:18.6344614Z If scale_fn is not None, this argument is ignored. 2025-09-07T08:19:18.6344717Z Default: 'triangular' 2025-09-07T08:19:18.6344899Z gamma (float): Constant in 'exp_range' scaling function: 2025-09-07T08:19:18.6345049Z gamma**(cycle iterations) 2025-09-07T08:19:18.6345180Z Default: 1.0 2025-09-07T08:19:18.6345372Z scale_fn (function): Custom scaling policy defined by a single 2025-09-07T08:19:18.6345531Z argument lambda function, where 2025-09-07T08:19:18.6345656Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-09-07T08:19:18.6345779Z If specified, then 'mode' is ignored. 2025-09-07T08:19:18.6345885Z Default: None 2025-09-07T08:19:18.6346010Z scale_mode (str): {'cycle', 'iterations'}. 2025-09-07T08:19:18.6346140Z Defines whether scale_fn is evaluated on 2025-09-07T08:19:18.6346286Z cycle number or cycle iterations (training 2025-09-07T08:19:18.6346401Z iterations since start of cycle). 2025-09-07T08:19:18.6346506Z Default: 'cycle' 2025-09-07T08:19:18.6346702Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T08:19:18.6346886Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T08:19:18.6346990Z Default: True 2025-09-07T08:19:18.6347205Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T08:19:18.6347421Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T08:19:18.6347587Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T08:19:18.6347756Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T08:19:18.6347859Z Default: 0.8 2025-09-07T08:19:18.6348069Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T08:19:18.6348210Z for each parameter group. Functionally, 2025-09-07T08:19:18.6348401Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T08:19:18.6348581Z The momentum at any cycle is the difference of max_momentum 2025-09-07T08:19:18.6348731Z and some scaling of the amplitude; therefore 2025-09-07T08:19:18.6348899Z base_momentum may not actually be reached depending on 2025-09-07T08:19:18.6349088Z scaling function. Note that momentum is cycled inversely 2025-09-07T08:19:18.6349299Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T08:19:18.6349408Z and learning rate is 'base_lr' 2025-09-07T08:19:18.6349572Z Default: 0.9 2025-09-07T08:19:18.6349796Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T08:19:18.6350024Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T08:19:18.6350237Z batch instead of after each epoch, this number represents the total 2025-09-07T08:19:18.6350456Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T08:19:18.6350658Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T08:19:18.6350748Z Default: -1 2025-09-07T08:19:18.6350754Z 2025-09-07T08:19:18.6350860Z Example: 2025-09-07T08:19:18.6350957Z >>> # xdoctest: +SKIP 2025-09-07T08:19:18.6351183Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T08:19:18.6351355Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-09-07T08:19:18.6351451Z ... optimizer, 2025-09-07T08:19:18.6351558Z ... base_lr=0.01, 2025-09-07T08:19:18.6351647Z ... max_lr=0.1, 2025-09-07T08:19:18.6351743Z ... step_size_up=10, 2025-09-07T08:19:18.6351836Z ... ) 2025-09-07T08:19:18.6351983Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T08:19:18.6352098Z >>> for epoch in range(10): 2025-09-07T08:19:18.6352202Z >>> for batch in data_loader: 2025-09-07T08:19:18.6352297Z >>> train_batch(...) 2025-09-07T08:19:18.6352408Z >>> scheduler.step() 2025-09-07T08:19:18.6352412Z 2025-09-07T08:19:18.6352573Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-09-07T08:19:18.6352578Z 2025-09-07T08:19:18.6352904Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-09-07T08:19:18.6353119Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-09-07T08:19:18.6353198Z 2025-09-07T08:19:18.6353472Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.6353478Z 2025-09-07T08:19:18.6354127Z msg = Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1725. 2025-09-07T08:19:18.6354399Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.6354651Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-09-07T08:19:18.6354655Z 2025-09-07T08:19:18.6354845Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-09-07T08:19:18.6355080Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-09-07T08:19:18.6355226Z of epochs between two warm restarts in SGDR: 2025-09-07T08:19:18.6355231Z 2025-09-07T08:19:18.6355316Z .. math:: 2025-09-07T08:19:18.6355494Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-09-07T08:19:18.6355647Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-09-07T08:19:18.6355696Z 2025-09-07T08:19:18.6355860Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-09-07T08:19:18.6356057Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-09-07T08:19:18.6356062Z 2025-09-07T08:19:18.6356163Z It has been proposed in 2025-09-07T08:19:18.6356343Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-09-07T08:19:18.6356347Z 2025-09-07T08:19:18.6356428Z Args: 2025-09-07T08:19:18.6356561Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:18.6356728Z T_0 (int): Number of iterations until the first restart. 2025-09-07T08:19:18.6357010Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-09-07T08:19:18.6357210Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-09-07T08:19:18.6357474Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-09-07T08:19:18.6357480Z 2025-09-07T08:19:18.6357647Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-09-07T08:19:18.6357775Z https://arxiv.org/abs/1608.03983 2025-09-07T08:19:18.6357779Z 2025-09-07T08:19:18.6357861Z Example: 2025-09-07T08:19:18.6357969Z >>> # xdoctest: +SKIP 2025-09-07T08:19:18.6358151Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-09-07T08:19:18.6358378Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-09-07T08:19:18.6358490Z ... optimizer, T_0=20 2025-09-07T08:19:18.6358570Z ... ) 2025-09-07T08:19:18.6358684Z >>> for epoch in range(100): 2025-09-07T08:19:18.6358773Z >>> train(...) 2025-09-07T08:19:18.6358863Z >>> validate(...) 2025-09-07T08:19:18.6358978Z >>> scheduler.step() 2025-09-07T08:19:18.6358982Z 2025-09-07T08:19:18.6359218Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-09-07T08:19:18.6359312Z 2025-09-07T08:19:18.6359560Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.6359565Z 2025-09-07T08:19:18.6367265Z msg = Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1875. 2025-09-07T08:19:18.6367828Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.6368413Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-09-07T08:19:18.6368435Z 2025-09-07T08:19:18.6368735Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-09-07T08:19:18.6369022Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-09-07T08:19:18.6369230Z lower than the initial learning rate. 2025-09-07T08:19:18.6369459Z This policy was initially described in the paper `Super-Convergence: 2025-09-07T08:19:18.6369683Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-09-07T08:19:18.6369687Z 2025-09-07T08:19:18.6369931Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-09-07T08:19:18.6370126Z `step` should be called after a batch has been used for training. 2025-09-07T08:19:18.6370130Z 2025-09-07T08:19:18.6370249Z This scheduler is not chainable. 2025-09-07T08:19:18.6370253Z 2025-09-07T08:19:18.6370488Z Note also that the total number of steps in the cycle can be determined in one 2025-09-07T08:19:18.6370634Z of two ways (listed in order of precedence): 2025-09-07T08:19:18.6370639Z 2025-09-07T08:19:18.6370787Z #. A value for total_steps is explicitly provided. 2025-09-07T08:19:18.6370979Z #. A number of epochs (epochs) and a number of steps per epoch 2025-09-07T08:19:18.6371091Z (steps_per_epoch) are provided. 2025-09-07T08:19:18.6371253Z In this case, the number of total steps is inferred by 2025-09-07T08:19:18.6371420Z total_steps = epochs * steps_per_epoch 2025-09-07T08:19:18.6371425Z 2025-09-07T08:19:18.6371686Z You must either provide a value for total_steps or provide a value for both 2025-09-07T08:19:18.6371802Z epochs and steps_per_epoch. 2025-09-07T08:19:18.6371806Z 2025-09-07T08:19:18.6372107Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-09-07T08:19:18.6372409Z claims that "unpublished work has shown even better results by using only two phases". To 2025-09-07T08:19:18.6372666Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-09-07T08:19:18.6372670Z 2025-09-07T08:19:18.6372765Z Args: 2025-09-07T08:19:18.6372897Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:18.6373095Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T08:19:18.6373221Z for each parameter group. 2025-09-07T08:19:18.6373775Z total_steps (int): The total number of steps in the cycle. Note that 2025-09-07T08:19:18.6374006Z if a value is not provided here, then it must be inferred by providing 2025-09-07T08:19:18.6374134Z a value for epochs and steps_per_epoch. 2025-09-07T08:19:18.6374228Z Default: None 2025-09-07T08:19:18.6374443Z epochs (int): The number of epochs to train for. This is used along 2025-09-07T08:19:18.6374684Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-09-07T08:19:18.6374834Z if a value for total_steps is not provided. 2025-09-07T08:19:18.6374929Z Default: None 2025-09-07T08:19:18.6375153Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-09-07T08:19:18.6375395Z used along with epochs in order to infer the total number of steps in the 2025-09-07T08:19:18.6375545Z cycle if a value for total_steps is not provided. 2025-09-07T08:19:18.6375657Z Default: None 2025-09-07T08:19:18.6375885Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-09-07T08:19:18.6375998Z increasing the learning rate. 2025-09-07T08:19:18.6376103Z Default: 0.3 2025-09-07T08:19:18.6376229Z anneal_strategy (str): {'cos', 'linear'} 2025-09-07T08:19:18.6376489Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-09-07T08:19:18.6376589Z linear annealing. 2025-09-07T08:19:18.6376686Z Default: 'cos' 2025-09-07T08:19:18.6376899Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T08:19:18.6377083Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T08:19:18.6377188Z Default: True 2025-09-07T08:19:18.6377445Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T08:19:18.6377650Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T08:19:18.6377832Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T08:19:18.6377974Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T08:19:18.6378079Z Default: 0.85 2025-09-07T08:19:18.6378291Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T08:19:18.6378424Z for each parameter group. Functionally, 2025-09-07T08:19:18.6378630Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T08:19:18.6378755Z Note that momentum is cycled inversely 2025-09-07T08:19:18.6378976Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T08:19:18.6379085Z and learning rate is 'base_lr' 2025-09-07T08:19:18.6379176Z Default: 0.95 2025-09-07T08:19:18.6379375Z div_factor (float): Determines the initial learning rate via 2025-09-07T08:19:18.6379486Z initial_lr = max_lr/div_factor 2025-09-07T08:19:18.6379625Z Default: 25 2025-09-07T08:19:18.6379829Z final_div_factor (float): Determines the minimum learning rate via 2025-09-07T08:19:18.6379947Z min_lr = initial_lr/final_div_factor 2025-09-07T08:19:18.6380048Z Default: 1e4 2025-09-07T08:19:18.6380289Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-09-07T08:19:18.6380550Z learning rate according to 'final_div_factor' instead of modifying the second 2025-09-07T08:19:18.6380786Z phase (the first two phases will be symmetrical about the step indicated by 2025-09-07T08:19:18.6380877Z 'pct_start'). 2025-09-07T08:19:18.6381110Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T08:19:18.6381328Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T08:19:18.6381606Z batch instead of after each epoch, this number represents the total 2025-09-07T08:19:18.6381830Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T08:19:18.6382018Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T08:19:18.6382120Z Default: -1 2025-09-07T08:19:18.6382124Z 2025-09-07T08:19:18.6382207Z Example: 2025-09-07T08:19:18.6382315Z >>> # xdoctest: +SKIP 2025-09-07T08:19:18.6382464Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T08:19:18.6382695Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-09-07T08:19:18.6382862Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-09-07T08:19:18.6383072Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-09-07T08:19:18.6383170Z ... ) 2025-09-07T08:19:18.6383275Z >>> for epoch in range(10): 2025-09-07T08:19:18.6383381Z >>> for batch in data_loader: 2025-09-07T08:19:18.6383497Z >>> train_batch(...) 2025-09-07T08:19:18.6383602Z >>> optimizer.step() 2025-09-07T08:19:18.6383720Z >>> scheduler.step() 2025-09-07T08:19:18.6383724Z 2025-09-07T08:19:18.6383897Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-09-07T08:19:18.6383901Z 2025-09-07T08:19:18.6384204Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-09-07T08:19:18.6384329Z https://arxiv.org/abs/1708.07120 2025-09-07T08:19:18.6384409Z 2025-09-07T08:19:18.6384673Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.6384678Z 2025-09-07T08:19:18.6503105Z msg = Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/optimizer.py line=868. 2025-09-07T08:19:18.6503515Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.6503628Z Load the optimizer state. 2025-09-07T08:19:18.6503637Z 2025-09-07T08:19:18.6503731Z Args: 2025-09-07T08:19:18.6503927Z state_dict (dict): optimizer state. Should be an object returned 2025-09-07T08:19:18.6504046Z from a call to :meth:`state_dict`. 2025-09-07T08:19:18.6504050Z 2025-09-07T08:19:18.6504158Z .. warning:: 2025-09-07T08:19:18.6504495Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-09-07T08:19:18.6504715Z as calling it beforehand will overwrite the loaded learning rates. 2025-09-07T08:19:18.6504720Z 2025-09-07T08:19:18.6504806Z .. note:: 2025-09-07T08:19:18.6505093Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-09-07T08:19:18.6505280Z in :meth:`state_dict`) will not affect the loading process. 2025-09-07T08:19:18.6505613Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-09-07T08:19:18.6505827Z differ from those initialized in the optimizer), 2025-09-07T08:19:18.6506130Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-09-07T08:19:18.6506234Z accordingly. 2025-09-07T08:19:18.6506524Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-09-07T08:19:18.6506842Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-09-07T08:19:18.6507018Z the optimizer ``param_names`` will remain unchanged. 2025-09-07T08:19:18.6507023Z 2025-09-07T08:19:18.6507109Z Example: 2025-09-07T08:19:18.6507237Z >>> # xdoctest: +SKIP 2025-09-07T08:19:18.6507356Z >>> model = torch.nn.Linear(10, 10) 2025-09-07T08:19:18.6507537Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-09-07T08:19:18.6507791Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-09-07T08:19:18.6507885Z ... optim, 2025-09-07T08:19:18.6508002Z ... start_factor=0.1, 2025-09-07T08:19:18.6508096Z ... end_factor=1, 2025-09-07T08:19:18.6508192Z ... total_iters=20, 2025-09-07T08:19:18.6508287Z ... ) 2025-09-07T08:19:18.6508477Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-09-07T08:19:18.6508579Z ... optim, 2025-09-07T08:19:18.6508671Z ... T_max=80, 2025-09-07T08:19:18.6508764Z ... eta_min=3e-5, 2025-09-07T08:19:18.6508860Z ... ) 2025-09-07T08:19:18.6509004Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-09-07T08:19:18.6509106Z ... optim, 2025-09-07T08:19:18.6509241Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T08:19:18.6509342Z ... milestones=[20], 2025-09-07T08:19:18.6509441Z ... ) 2025-09-07T08:19:18.6509588Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-09-07T08:19:18.6509802Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-09-07T08:19:18.6509967Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-09-07T08:19:18.6509971Z 2025-09-07T08:19:18.6510053Z 2025-09-07T08:19:18.6510315Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.6510319Z 2025-09-07T08:19:18.6701593Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=120. 2025-09-07T08:19:18.6702107Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.6702665Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-09-07T08:19:18.6702774Z 2025-09-07T08:19:18.6703046Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-09-07T08:19:18.6703266Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-09-07T08:19:18.6703497Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-09-07T08:19:18.6703583Z (UAI 2018). 2025-09-07T08:19:18.6703588Z 2025-09-07T08:19:18.6703801Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-09-07T08:19:18.6704055Z but using exponential weights instead of equal weights across iterations. 2025-09-07T08:19:18.6704059Z 2025-09-07T08:19:18.6704293Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-09-07T08:19:18.6704526Z on the device :attr:`device` and allows to compute running averages of the 2025-09-07T08:19:18.6704633Z parameters of the :attr:`model`. 2025-09-07T08:19:18.6704637Z 2025-09-07T08:19:18.6704722Z Args: 2025-09-07T08:19:18.6704884Z model (torch.nn.Module): model to use with SWA/EMA 2025-09-07T08:19:18.6705123Z device (torch.device, optional): if provided, the averaged model will be 2025-09-07T08:19:18.6705285Z stored on the :attr:`device` 2025-09-07T08:19:18.6705488Z avg_fn (function, optional): the averaging function used to update 2025-09-07T08:19:18.6705687Z parameters; the function must take in the current value of the 2025-09-07T08:19:18.6705918Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-09-07T08:19:18.6706110Z parameter, and the number of models already averaged; if None, 2025-09-07T08:19:18.6706281Z an equally weighted average is used (default: None) 2025-09-07T08:19:18.6706504Z multi_avg_fn (function, optional): the averaging function used to update 2025-09-07T08:19:18.6706752Z parameters inplace; the function must take in the current values of the 2025-09-07T08:19:18.6707020Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-09-07T08:19:18.6707356Z parameters as a list, and the number of models already averaged; if None, 2025-09-07T08:19:18.6707516Z an equally weighted average is used (default: None) 2025-09-07T08:19:18.6707718Z use_buffers (bool): if ``True``, it will compute running averages for 2025-09-07T08:19:18.6707951Z both the parameters and the buffers of the model. (default: ``False``) 2025-09-07T08:19:18.6707955Z 2025-09-07T08:19:18.6708038Z Example: 2025-09-07T08:19:18.6708182Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:18.6708306Z >>> loader, optimizer, model, loss_fn = ... 2025-09-07T08:19:18.6708473Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-09-07T08:19:18.6708708Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-09-07T08:19:18.6708819Z >>> T_max=300) 2025-09-07T08:19:18.6708928Z >>> swa_start = 160 2025-09-07T08:19:18.6709070Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-09-07T08:19:18.6709169Z >>> for i in range(300): 2025-09-07T08:19:18.6709297Z >>> for input, target in loader: 2025-09-07T08:19:18.6709406Z >>> optimizer.zero_grad() 2025-09-07T08:19:18.6709550Z >>> loss_fn(model(input), target).backward() 2025-09-07T08:19:18.6709655Z >>> optimizer.step() 2025-09-07T08:19:18.6709751Z >>> if i > swa_start: 2025-09-07T08:19:18.6709892Z >>> swa_model.update_parameters(model) 2025-09-07T08:19:18.6709998Z >>> swa_scheduler.step() 2025-09-07T08:19:18.6710096Z >>> else: 2025-09-07T08:19:18.6710197Z >>> scheduler.step() 2025-09-07T08:19:18.6710280Z >>> 2025-09-07T08:19:18.6710446Z >>> # Update bn statistics for the swa_model at the end 2025-09-07T08:19:18.6710635Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-09-07T08:19:18.6710640Z 2025-09-07T08:19:18.6710954Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-09-07T08:19:18.6711150Z If no averaging function is provided, the default is to compute 2025-09-07T08:19:18.6711295Z equally-weighted average of the weights (SWA). 2025-09-07T08:19:18.6711312Z 2025-09-07T08:19:18.6711397Z Example: 2025-09-07T08:19:18.6711523Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:18.6711788Z >>> # Compute exponential moving averages of the weights and buffers 2025-09-07T08:19:18.6712037Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-09-07T08:19:18.6712377Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-09-07T08:19:18.6712386Z 2025-09-07T08:19:18.6712535Z .. note:: 2025-09-07T08:19:18.6712936Z When using SWA/EMA with models containing Batch Normalization you may 2025-09-07T08:19:18.6713353Z need to update the activation statistics for Batch Normalization. 2025-09-07T08:19:18.6713652Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-09-07T08:19:18.6713887Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-09-07T08:19:18.6714126Z statistics in a post-training step by passing data through the model. The 2025-09-07T08:19:18.6714363Z second does it during the parameter update phase by averaging all buffers. 2025-09-07T08:19:18.6714623Z Empirical evidence has shown that updating the statistics in normalization 2025-09-07T08:19:18.6714849Z layers increases accuracy, but you may wish to empirically test which 2025-09-07T08:19:18.6715017Z approach yields the best results in your problem. 2025-09-07T08:19:18.6715022Z 2025-09-07T08:19:18.6715108Z .. note:: 2025-09-07T08:19:18.6715379Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-09-07T08:19:18.6715384Z 2025-09-07T08:19:18.6715522Z .. note:: 2025-09-07T08:19:18.6715722Z When :meth:`update_parameters` is called for the first time (i.e. 2025-09-07T08:19:18.6715917Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-09-07T08:19:18.6716117Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-09-07T08:19:18.6716323Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-09-07T08:19:18.6716430Z to update the parameters. 2025-09-07T08:19:18.6716434Z 2025-09-07T08:19:18.6716655Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T08:19:18.6716795Z https://arxiv.org/abs/1803.05407 2025-09-07T08:19:18.6717031Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-09-07T08:19:18.6717130Z Average: 2025-09-07T08:19:18.6717247Z https://arxiv.org/abs/1806.05594 2025-09-07T08:19:18.6717447Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-09-07T08:19:18.6717579Z https://arxiv.org/abs/1904.11943 2025-09-07T08:19:18.6717807Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-09-07T08:19:18.6717920Z Generalizes Well: 2025-09-07T08:19:18.6718034Z https://arxiv.org/abs/2001.02312 2025-09-07T08:19:18.6718131Z .. _Polyak averaging: 2025-09-07T08:19:18.6718313Z https://paperswithcode.com/method/polyak-averaging 2025-09-07T08:19:18.6718394Z 2025-09-07T08:19:18.6718658Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.6718662Z 2025-09-07T08:19:18.6719165Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=375. 2025-09-07T08:19:18.6719440Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:18.6719690Z Anneals the learning rate in each parameter group to a fixed value. 2025-09-07T08:19:18.6719694Z 2025-09-07T08:19:18.6719926Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-09-07T08:19:18.6720151Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-09-07T08:19:18.6720155Z 2025-09-07T08:19:18.6720237Z Args: 2025-09-07T08:19:18.6720424Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-09-07T08:19:18.6720629Z swa_lrs (float or list): the learning rate value for all param groups 2025-09-07T08:19:18.6720771Z together or separately for each group. 2025-09-07T08:19:18.6720971Z annealing_epochs (int): number of epochs in the annealing phase 2025-09-07T08:19:18.6721064Z (default: 10) 2025-09-07T08:19:18.6721288Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-09-07T08:19:18.6721493Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-09-07T08:19:18.6721608Z (default: "cos") 2025-09-07T08:19:18.6721789Z last_epoch (int): the index of the last epoch (default: -1) 2025-09-07T08:19:18.6721820Z 2025-09-07T08:19:18.6721995Z The :class:`SWALR` scheduler can be used together with other 2025-09-07T08:19:18.6722221Z schedulers to switch to a constant learning rate late in the training 2025-09-07T08:19:18.6722320Z as in the example below. 2025-09-07T08:19:18.6722324Z 2025-09-07T08:19:18.6722420Z Example: 2025-09-07T08:19:18.6722547Z >>> # xdoctest: +SKIP("Undefined variables") 2025-09-07T08:19:18.6722658Z >>> loader, optimizer, model = ... 2025-09-07T08:19:18.6722777Z >>> lr_lambda = lambda epoch: 0.9 2025-09-07T08:19:18.6722994Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-09-07T08:19:18.6723110Z >>> lr_lambda=lr_lambda) 2025-09-07T08:19:18.6723278Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-09-07T08:19:18.6723454Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-09-07T08:19:18.6723607Z >>> swa_start = 160 2025-09-07T08:19:18.6723708Z >>> for i in range(300): 2025-09-07T08:19:18.6723830Z >>> for input, target in loader: 2025-09-07T08:19:18.6723937Z >>> optimizer.zero_grad() 2025-09-07T08:19:18.6724068Z >>> loss_fn(model(input), target).backward() 2025-09-07T08:19:18.6724267Z >>> optimizer.step() 2025-09-07T08:19:18.6724361Z >>> if i > swa_start: 2025-09-07T08:19:18.6724479Z >>> swa_scheduler.step() 2025-09-07T08:19:18.6724565Z >>> else: 2025-09-07T08:19:18.6724664Z >>> scheduler.step() 2025-09-07T08:19:18.6724669Z 2025-09-07T08:19:18.6724905Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T08:19:18.6725021Z https://arxiv.org/abs/1803.05407 2025-09-07T08:19:18.6725119Z 2025-09-07T08:19:18.6725367Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:18.6725372Z 2025-09-07T08:19:19.3077615Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1331. 2025-09-07T08:19:19.3079675Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:19.3080670Z Asserts that ``actual`` and ``expected`` are close. 2025-09-07T08:19:19.3081153Z 2025-09-07T08:19:19.3081842Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-09-07T08:19:19.3082754Z 2025-09-07T08:19:19.3082948Z .. math:: 2025-09-07T08:19:19.3083178Z 2025-09-07T08:19:19.3083852Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-09-07T08:19:19.3084858Z 2025-09-07T08:19:19.3085769Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-09-07T08:19:19.3087012Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-09-07T08:19:19.3087631Z 2025-09-07T08:19:19.3087993Z In addition, they are only considered close if they have the same 2025-09-07T08:19:19.3088587Z 2025-09-07T08:19:19.3088957Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-09-07T08:19:19.3089767Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-09-07T08:19:19.3090472Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-09-07T08:19:19.3091181Z - stride (if ``check_stride`` is ``True``). 2025-09-07T08:19:19.3091630Z 2025-09-07T08:19:19.3092211Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-09-07T08:19:19.3093003Z 2025-09-07T08:19:19.3093603Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-09-07T08:19:19.3095251Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-09-07T08:19:19.3096772Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-09-07T08:19:19.3098161Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-09-07T08:19:19.3099123Z 2025-09-07T08:19:19.3099630Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-09-07T08:19:19.3101086Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-09-07T08:19:19.3102177Z definition above. 2025-09-07T08:19:19.3102493Z 2025-09-07T08:19:19.3103093Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-09-07T08:19:19.3104680Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-09-07T08:19:19.3106558Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-09-07T08:19:19.3108256Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-09-07T08:19:19.3109557Z their elements are considered close according to the above definition. 2025-09-07T08:19:19.3110219Z 2025-09-07T08:19:19.3110388Z .. note:: 2025-09-07T08:19:19.3110629Z 2025-09-07T08:19:19.3111249Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-09-07T08:19:19.3112671Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-09-07T08:19:19.3113943Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-09-07T08:19:19.3114691Z 2025-09-07T08:19:19.3114865Z Args: 2025-09-07T08:19:19.3115248Z actual (Any): Actual input. 2025-09-07T08:19:19.3115816Z expected (Any): Expected input. 2025-09-07T08:19:19.3116886Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-09-07T08:19:19.3117996Z are allowed. Otherwise type equality is required. 2025-09-07T08:19:19.3118673Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-09-07T08:19:19.3119421Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T08:19:19.3120172Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-09-07T08:19:19.3120915Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T08:19:19.3121549Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-09-07T08:19:19.3122294Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-09-07T08:19:19.3122963Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-09-07T08:19:19.3123574Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-09-07T08:19:19.3124384Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-09-07T08:19:19.3125596Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-09-07T08:19:19.3126742Z :func:`torch.promote_types`) before being compared. 2025-09-07T08:19:19.3127917Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-09-07T08:19:19.3129331Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-09-07T08:19:19.3130318Z compared. 2025-09-07T08:19:19.3131247Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-09-07T08:19:19.3132893Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-09-07T08:19:19.3134482Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-09-07T08:19:19.3135543Z should return the new message. 2025-09-07T08:19:19.3135948Z 2025-09-07T08:19:19.3136095Z Raises: 2025-09-07T08:19:19.3136743Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-09-07T08:19:19.3137700Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-09-07T08:19:19.3138832Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-09-07T08:19:19.3140382Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-09-07T08:19:19.3141617Z different types. 2025-09-07T08:19:19.3142658Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-09-07T08:19:19.3144259Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-09-07T08:19:19.3145812Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-09-07T08:19:19.3147216Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-09-07T08:19:19.3148206Z :attr:`~torch.Tensor.layout`. 2025-09-07T08:19:19.3149025Z AssertionError: If only one of corresponding tensors is quantized. 2025-09-07T08:19:19.3150409Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-09-07T08:19:19.3151896Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-09-07T08:19:19.3152886Z :attr:`~torch.Tensor.device`. 2025-09-07T08:19:19.3153921Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-09-07T08:19:19.3155486Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-09-07T08:19:19.3157110Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-09-07T08:19:19.3158027Z 2025-09-07T08:19:19.3158726Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-09-07T08:19:19.3159905Z ``dtype``'s, the maximum of both tolerances is used. 2025-09-07T08:19:19.3160379Z 2025-09-07T08:19:19.3160592Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3161360Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-09-07T08:19:19.3161992Z +===========================+============+==========+ 2025-09-07T08:19:19.3162651Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-09-07T08:19:19.3163323Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3164008Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-09-07T08:19:19.3164789Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3165468Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:19.3166135Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3166815Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-09-07T08:19:19.3167427Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3168088Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-09-07T08:19:19.3168766Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3169437Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:19.3170131Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3170916Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-09-07T08:19:19.3171616Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3172271Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:19.3172943Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3173821Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:19.3174511Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3175177Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:19.3175861Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3176548Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:19.3177212Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3178049Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:19.3178728Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3179373Z | other | ``0.0`` | ``0.0`` | 2025-09-07T08:19:19.3180021Z +---------------------------+------------+----------+ 2025-09-07T08:19:19.3180456Z 2025-09-07T08:19:19.3180635Z .. note:: 2025-09-07T08:19:19.3180862Z 2025-09-07T08:19:19.3181582Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-09-07T08:19:19.3183253Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-09-07T08:19:19.3184678Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-09-07T08:19:19.3185417Z 2025-09-07T08:19:19.3185613Z >>> import functools 2025-09-07T08:19:19.3186448Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-09-07T08:19:19.3187383Z >>> assert_equal(1e-9, 1e-10) 2025-09-07T08:19:19.3187993Z Traceback (most recent call last): 2025-09-07T08:19:19.3188570Z ... 2025-09-07T08:19:19.3189021Z AssertionError: Scalars are not equal! 2025-09-07T08:19:19.3189616Z 2025-09-07T08:19:19.3190072Z Expected 1e-10 but got 1e-09. 2025-09-07T08:19:19.3190687Z Absolute difference: 9.000000000000001e-10 2025-09-07T08:19:19.3191329Z Relative difference: 9.0 2025-09-07T08:19:19.3191687Z 2025-09-07T08:19:19.3191840Z Examples: 2025-09-07T08:19:19.3192280Z >>> # tensor to tensor comparison 2025-09-07T08:19:19.3192933Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-09-07T08:19:19.3193628Z >>> actual = torch.acos(torch.cos(expected)) 2025-09-07T08:19:19.3194339Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:19.3194995Z 2025-09-07T08:19:19.3195196Z >>> # scalar to scalar comparison 2025-09-07T08:19:19.3195779Z >>> import math 2025-09-07T08:19:19.3196270Z >>> expected = math.sqrt(2.0) 2025-09-07T08:19:19.3196851Z >>> actual = 2.0 / math.sqrt(2.0) 2025-09-07T08:19:19.3197506Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:19.3198005Z 2025-09-07T08:19:19.3198228Z >>> # numpy array to numpy array comparison 2025-09-07T08:19:19.3198866Z >>> import numpy as np 2025-09-07T08:19:19.3199423Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-09-07T08:19:19.3200066Z >>> actual = np.arccos(np.cos(expected)) 2025-09-07T08:19:19.3200770Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:19.3201271Z 2025-09-07T08:19:19.3201482Z >>> # sequence to sequence comparison 2025-09-07T08:19:19.3202245Z >>> import numpy as np 2025-09-07T08:19:19.3203397Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-09-07T08:19:19.3204591Z >>> # length and their elements have to match. 2025-09-07T08:19:19.3205523Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-09-07T08:19:19.3206449Z >>> actual = tuple(expected) 2025-09-07T08:19:19.3207226Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:19.3223710Z 2025-09-07T08:19:19.3223872Z >>> # mapping to mapping comparison 2025-09-07T08:19:19.3224256Z >>> from collections import OrderedDict 2025-09-07T08:19:19.3224605Z >>> import numpy as np 2025-09-07T08:19:19.3224891Z >>> foo = torch.tensor(1.0) 2025-09-07T08:19:19.3225194Z >>> bar = 2.0 2025-09-07T08:19:19.3225453Z >>> baz = np.array(3.0) 2025-09-07T08:19:19.3225903Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-09-07T08:19:19.3226474Z >>> # have to have the same set of keys and their elements have to match. 2025-09-07T08:19:19.3227014Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-09-07T08:19:19.3227581Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-09-07T08:19:19.3227989Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:19.3228247Z 2025-09-07T08:19:19.3228387Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T08:19:19.3228730Z >>> actual = expected.clone() 2025-09-07T08:19:19.3229111Z >>> # By default, directly related instances can be compared 2025-09-07T08:19:19.3229892Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-09-07T08:19:19.3230822Z >>> # This check can be made more strict with allow_subclasses=False 2025-09-07T08:19:19.3231548Z >>> torch.testing.assert_close( 2025-09-07T08:19:19.3232307Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-09-07T08:19:19.3233041Z ... ) 2025-09-07T08:19:19.3233466Z Traceback (most recent call last): 2025-09-07T08:19:19.3234024Z ... 2025-09-07T08:19:19.3234620Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T08:19:19.3235624Z and . 2025-09-07T08:19:19.3236672Z >>> # If the inputs are not directly related, they are never considered close 2025-09-07T08:19:19.3237619Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-09-07T08:19:19.3238353Z Traceback (most recent call last): 2025-09-07T08:19:19.3238921Z ... 2025-09-07T08:19:19.3239694Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T08:19:19.3240660Z and . 2025-09-07T08:19:19.3241523Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-09-07T08:19:19.3242465Z >>> # their type if check_dtype=False. 2025-09-07T08:19:19.3243318Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-09-07T08:19:19.3243857Z 2025-09-07T08:19:19.3244060Z >>> # NaN != NaN by default. 2025-09-07T08:19:19.3244749Z >>> expected = torch.tensor(float("Nan")) 2025-09-07T08:19:19.3245379Z >>> actual = expected.clone() 2025-09-07T08:19:19.3246008Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:19.3246698Z Traceback (most recent call last): 2025-09-07T08:19:19.3247251Z ... 2025-09-07T08:19:19.3247703Z AssertionError: Scalars are not close! 2025-09-07T08:19:19.3248283Z 2025-09-07T08:19:19.3248733Z Expected nan but got nan. 2025-09-07T08:19:19.3249337Z Absolute difference: nan (up to 1e-05 allowed) 2025-09-07T08:19:19.3250090Z Relative difference: nan (up to 1.3e-06 allowed) 2025-09-07T08:19:19.3250943Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-09-07T08:19:19.3251574Z 2025-09-07T08:19:19.3251811Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T08:19:19.3252483Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-09-07T08:19:19.3253250Z >>> # The default error message can be overwritten. 2025-09-07T08:19:19.3253945Z >>> torch.testing.assert_close( 2025-09-07T08:19:19.3254690Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-09-07T08:19:19.3255400Z ... ) 2025-09-07T08:19:19.3255834Z Traceback (most recent call last): 2025-09-07T08:19:19.3256383Z ... 2025-09-07T08:19:19.3256870Z AssertionError: Argh, the tensors are not close! 2025-09-07T08:19:19.3257770Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-09-07T08:19:19.3258589Z >>> # extra information 2025-09-07T08:19:19.3259146Z >>> torch.testing.assert_close( 2025-09-07T08:19:19.3259829Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-09-07T08:19:19.3260577Z ... ) 2025-09-07T08:19:19.3260997Z Traceback (most recent call last): 2025-09-07T08:19:19.3261570Z ... 2025-09-07T08:19:19.3262097Z AssertionError: Header 2025-09-07T08:19:19.3262620Z 2025-09-07T08:19:19.3263064Z Tensor-likes are not close! 2025-09-07T08:19:19.3263610Z 2025-09-07T08:19:19.3264072Z Mismatched elements: 2 / 3 (66.7%) 2025-09-07T08:19:19.3264915Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-09-07T08:19:19.3266009Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-09-07T08:19:19.3266852Z 2025-09-07T08:19:19.3267271Z Footer 2025-09-07T08:19:19.3267657Z 2025-09-07T08:19:19.3268325Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:19.3269061Z 2025-09-07T08:19:20.5159671Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=134. 2025-09-07T08:19:20.5160669Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.5161202Z Register a container-like type as pytree node. 2025-09-07T08:19:20.5161470Z 2025-09-07T08:19:20.5161558Z Args: 2025-09-07T08:19:20.5161876Z cls (type): A Python type to treat as an internal pytree node. 2025-09-07T08:19:20.5162458Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-09-07T08:19:20.5163106Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-09-07T08:19:20.5163761Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-09-07T08:19:20.5164375Z passed to the ``unflatten_fn``. 2025-09-07T08:19:20.5164884Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-09-07T08:19:20.5165721Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-09-07T08:19:20.5166256Z The function should return an instance of ``cls``. 2025-09-07T08:19:20.5166795Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-09-07T08:19:20.5167349Z qualified name used when serializing the tree spec. 2025-09-07T08:19:20.5167943Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-09-07T08:19:20.5168654Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-09-07T08:19:20.5169313Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-09-07T08:19:20.5170012Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-09-07T08:19:20.5170702Z how to convert the custom json dumpable representation of the context back to the 2025-09-07T08:19:20.5171349Z original context. This is used for json deserialization, which is being used in 2025-09-07T08:19:20.5171854Z :mod:`torch.export` right now. 2025-09-07T08:19:20.5172161Z 2025-09-07T08:19:20.5172268Z Example:: 2025-09-07T08:19:20.5172414Z 2025-09-07T08:19:20.5172513Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.5172850Z >>> # Registry a Python type with lambda functions 2025-09-07T08:19:20.5173214Z >>> register_pytree_node( 2025-09-07T08:19:20.5173645Z ... set, 2025-09-07T08:19:20.5173924Z ... lambda s: (sorted(s), None, None), 2025-09-07T08:19:20.5174289Z ... lambda children, _: set(children), 2025-09-07T08:19:20.5174613Z ... ) 2025-09-07T08:19:20.5174817Z 2025-09-07T08:19:20.5175188Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.5175568Z 2025-09-07T08:19:20.5813780Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1226. 2025-09-07T08:19:20.5815011Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.5815405Z 2025-09-07T08:19:20.5815636Z Context passed to policy function during selective checkpointing. 2025-09-07T08:19:20.5815961Z 2025-09-07T08:19:20.5816199Z This class is used to pass relevant metadata to the policy function during 2025-09-07T08:19:20.5816803Z selective checkpointing. The metadata includes whether the current invocation 2025-09-07T08:19:20.5817365Z of the policy function is during recomputation or not. 2025-09-07T08:19:20.5817634Z 2025-09-07T08:19:20.5817735Z Example: 2025-09-07T08:19:20.5817963Z >>> # xdoctest: +SKIP(stub) 2025-09-07T08:19:20.5818310Z >>> 2025-09-07T08:19:20.5818705Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T08:19:20.5819132Z >>> print(ctx.is_recompute) 2025-09-07T08:19:20.5819526Z >>> 2025-09-07T08:19:20.5820290Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T08:19:20.5820865Z >>> 2025-09-07T08:19:20.5821116Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T08:19:20.5821461Z >>> fn, x, y, 2025-09-07T08:19:20.5821717Z >>> use_reentrant=False, 2025-09-07T08:19:20.5822014Z >>> context_fn=context_fn, 2025-09-07T08:19:20.5822287Z >>> ) 2025-09-07T08:19:20.5822415Z 2025-09-07T08:19:20.5822669Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.5823049Z 2025-09-07T08:19:20.5823677Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1366. 2025-09-07T08:19:20.5824688Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.5825065Z 2025-09-07T08:19:20.5825315Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-09-07T08:19:20.5825748Z 2025-09-07T08:19:20.5825976Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-09-07T08:19:20.5826460Z operations are recomputed during the backward pass. 2025-09-07T08:19:20.5826744Z 2025-09-07T08:19:20.5826826Z Args: 2025-09-07T08:19:20.5827062Z policy_fn_or_list (Callable or List): 2025-09-07T08:19:20.5827464Z - If a policy function is provided, it should accept a 2025-09-07T08:19:20.5827971Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-09-07T08:19:20.5828539Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-09-07T08:19:20.5829106Z indicating whether the execution of the op should be recomputed or not. 2025-09-07T08:19:20.5829665Z - If a list of operations is provided, it is equivalent to a policy 2025-09-07T08:19:20.5830170Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-09-07T08:19:20.5830679Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-09-07T08:19:20.5831112Z operations. 2025-09-07T08:19:20.5831480Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-09-07T08:19:20.5832083Z raised if any tensors cached by selective activation checkpoint are 2025-09-07T08:19:20.5832616Z mutated in order to ensure correctness. If set to `True`, this check 2025-09-07T08:19:20.5833043Z is disabled. 2025-09-07T08:19:20.5833287Z Returns: 2025-09-07T08:19:20.5833520Z A tuple of two context managers. 2025-09-07T08:19:20.5833727Z 2025-09-07T08:19:20.5833810Z Example: 2025-09-07T08:19:20.5834038Z >>> # xdoctest: +REQUIRES(LINUX) 2025-09-07T08:19:20.5834341Z >>> import functools 2025-09-07T08:19:20.5834591Z >>> 2025-09-07T08:19:20.5834818Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-09-07T08:19:20.5835179Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-09-07T08:19:20.5835501Z >>> 2025-09-07T08:19:20.5835711Z >>> ops_to_save = [ 2025-09-07T08:19:20.5835972Z >>> torch.ops.aten.mm.default, 2025-09-07T08:19:20.5836269Z >>> ] 2025-09-07T08:19:20.5836543Z >>> 2025-09-07T08:19:20.5836787Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T08:19:20.5837112Z >>> if op in ops_to_save: 2025-09-07T08:19:20.5837432Z >>> return CheckpointPolicy.MUST_SAVE 2025-09-07T08:19:20.5837760Z >>> else: 2025-09-07T08:19:20.5838040Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-09-07T08:19:20.5838365Z >>> 2025-09-07T08:19:20.5838753Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T08:19:20.5839225Z >>> 2025-09-07T08:19:20.5839437Z >>> # or equivalently 2025-09-07T08:19:20.5839867Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-09-07T08:19:20.5840342Z >>> 2025-09-07T08:19:20.5840549Z >>> def fn(x, y): 2025-09-07T08:19:20.5840908Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-09-07T08:19:20.5841301Z >>> 2025-09-07T08:19:20.5841559Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T08:19:20.5841905Z >>> fn, x, y, 2025-09-07T08:19:20.5842164Z >>> use_reentrant=False, 2025-09-07T08:19:20.5842447Z >>> context_fn=context_fn, 2025-09-07T08:19:20.5842732Z >>> ) 2025-09-07T08:19:20.5842847Z 2025-09-07T08:19:20.5843110Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.5843477Z 2025-09-07T08:19:20.6081908Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1158. 2025-09-07T08:19:20.6082905Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.6083284Z 2025-09-07T08:19:20.6083427Z Create a :class:`setuptools.Extension` for C++. 2025-09-07T08:19:20.6083692Z 2025-09-07T08:19:20.6083932Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T08:19:20.6084719Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-09-07T08:19:20.6085063Z 2025-09-07T08:19:20.6085288Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T08:19:20.6085764Z constructor. Full list arguments can be found at 2025-09-07T08:19:20.6086361Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T08:19:20.6086813Z 2025-09-07T08:19:20.6086923Z .. warning:: 2025-09-07T08:19:20.6087287Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T08:19:20.6087863Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T08:19:20.6088451Z the user's responsibility in their library to not use APIs from 2025-09-07T08:19:20.6089111Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T08:19:20.6089949Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T08:19:20.6090950Z example, to give access to custom ops from python, the library should 2025-09-07T08:19:20.6091590Z register the ops through the dispatcher. 2025-09-07T08:19:20.6091911Z 2025-09-07T08:19:20.6092151Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T08:19:20.6092689Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T08:19:20.6093227Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T08:19:20.6093776Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T08:19:20.6094328Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T08:19:20.6094873Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T08:19:20.6095426Z CPython version of choice. 2025-09-07T08:19:20.6095698Z 2025-09-07T08:19:20.6095822Z Example: 2025-09-07T08:19:20.6096133Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.6096516Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:20.6096880Z >>> from setuptools import setup 2025-09-07T08:19:20.6097499Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-09-07T08:19:20.6097996Z >>> setup( 2025-09-07T08:19:20.6098231Z ... name='extension', 2025-09-07T08:19:20.6098521Z ... ext_modules=[ 2025-09-07T08:19:20.6098816Z ... CppExtension( 2025-09-07T08:19:20.6099102Z ... name='extension', 2025-09-07T08:19:20.6099477Z ... sources=['extension.cpp'], 2025-09-07T08:19:20.6099812Z ... extra_compile_args=['-g'], 2025-09-07T08:19:20.6100240Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-09-07T08:19:20.6100592Z ... ], 2025-09-07T08:19:20.6100856Z ... cmdclass={ 2025-09-07T08:19:20.6101127Z ... 'build_ext': BuildExtension 2025-09-07T08:19:20.6101442Z ... }) 2025-09-07T08:19:20.6101610Z 2025-09-07T08:19:20.6101886Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.6102255Z 2025-09-07T08:19:20.6102912Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1228. 2025-09-07T08:19:20.6103949Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.6104331Z 2025-09-07T08:19:20.6104503Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-09-07T08:19:20.6104824Z 2025-09-07T08:19:20.6105076Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T08:19:20.6105912Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-09-07T08:19:20.6106669Z extension. This includes the CUDA include path, library path and runtime 2025-09-07T08:19:20.6107238Z library. 2025-09-07T08:19:20.6107356Z 2025-09-07T08:19:20.6107573Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T08:19:20.6108114Z constructor. Full list arguments can be found at 2025-09-07T08:19:20.6108699Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T08:19:20.6109160Z 2025-09-07T08:19:20.6109256Z .. warning:: 2025-09-07T08:19:20.6109617Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T08:19:20.6110176Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T08:19:20.6110690Z the user's responsibility in their library to not use APIs from 2025-09-07T08:19:20.6111241Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T08:19:20.6111813Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T08:19:20.6112363Z example, to give access to custom ops from python, the library should 2025-09-07T08:19:20.6112824Z register the ops through the dispatcher. 2025-09-07T08:19:20.6113054Z 2025-09-07T08:19:20.6113279Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T08:19:20.6113825Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T08:19:20.6114388Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T08:19:20.6114930Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T08:19:20.6115463Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T08:19:20.6116007Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T08:19:20.6116465Z CPython version of choice. 2025-09-07T08:19:20.6116753Z 2025-09-07T08:19:20.6116901Z Example: 2025-09-07T08:19:20.6117237Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.6117563Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:20.6117978Z >>> from setuptools import setup 2025-09-07T08:19:20.6118539Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-09-07T08:19:20.6119051Z >>> setup( 2025-09-07T08:19:20.6119277Z ... name='cuda_extension', 2025-09-07T08:19:20.6119572Z ... ext_modules=[ 2025-09-07T08:19:20.6119918Z ... CUDAExtension( 2025-09-07T08:19:20.6120221Z ... name='cuda_extension', 2025-09-07T08:19:20.6120599Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T08:19:20.6121011Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T08:19:20.6121376Z ... 'nvcc': ['-O2']}, 2025-09-07T08:19:20.6121767Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-09-07T08:19:20.6122115Z ... ], 2025-09-07T08:19:20.6122344Z ... cmdclass={ 2025-09-07T08:19:20.6122613Z ... 'build_ext': BuildExtension 2025-09-07T08:19:20.6122923Z ... }) 2025-09-07T08:19:20.6123049Z 2025-09-07T08:19:20.6123148Z Compute capabilities: 2025-09-07T08:19:20.6123320Z 2025-09-07T08:19:20.6123621Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T08:19:20.6124423Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-09-07T08:19:20.6125135Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-09-07T08:19:20.6125850Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-09-07T08:19:20.6126548Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-09-07T08:19:20.6127070Z support (see below for details on PTX). 2025-09-07T08:19:20.6127301Z 2025-09-07T08:19:20.6127607Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-09-07T08:19:20.6128152Z CCs you want the extension to support: 2025-09-07T08:19:20.6128369Z 2025-09-07T08:19:20.6128567Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-09-07T08:19:20.6129152Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-09-07T08:19:20.6129521Z 2025-09-07T08:19:20.6129843Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-09-07T08:19:20.6130607Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-09-07T08:19:20.6131353Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-09-07T08:19:20.6132076Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-09-07T08:19:20.6132806Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-09-07T08:19:20.6133525Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-09-07T08:19:20.6134241Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-09-07T08:19:20.6134998Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-09-07T08:19:20.6135539Z "8.0 8.6" would be better. 2025-09-07T08:19:20.6135711Z 2025-09-07T08:19:20.6136035Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T08:19:20.6136740Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T08:19:20.6137159Z 2025-09-07T08:19:20.6137492Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-09-07T08:19:20.6138159Z To workaround the issue, move python binding logic to pure C++ file. 2025-09-07T08:19:20.6138483Z 2025-09-07T08:19:20.6138585Z Example use: 2025-09-07T08:19:20.6138809Z #include 2025-09-07T08:19:20.6139142Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-09-07T08:19:20.6139409Z 2025-09-07T08:19:20.6139497Z Instead of: 2025-09-07T08:19:20.6139735Z #include 2025-09-07T08:19:20.6140094Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-09-07T08:19:20.6140356Z 2025-09-07T08:19:20.6140688Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-09-07T08:19:20.6141598Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-09-07T08:19:20.6142226Z 2025-09-07T08:19:20.6142337Z Relocatable device code linking: 2025-09-07T08:19:20.6142531Z 2025-09-07T08:19:20.6142820Z If you want to reference device symbols across compilation units (across object files), 2025-09-07T08:19:20.6143483Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-09-07T08:19:20.6144211Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-09-07T08:19:20.6145021Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-09-07T08:19:20.6145792Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-09-07T08:19:20.6146422Z helps reduce the protentional perf degradation of `-rdc`. 2025-09-07T08:19:20.6146881Z Note that it needs to be used at both steps to be useful. 2025-09-07T08:19:20.6147155Z 2025-09-07T08:19:20.6147519Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-09-07T08:19:20.6148179Z There is also a case where `-dlink` is used without `-rdc`: 2025-09-07T08:19:20.6148724Z when an extension is linked against a static lib containing rdc-compiled objects 2025-09-07T08:19:20.6149319Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-09-07T08:19:20.6149642Z 2025-09-07T08:19:20.6149842Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-09-07T08:19:20.6150171Z 2025-09-07T08:19:20.6150255Z Example: 2025-09-07T08:19:20.6150479Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.6150830Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:20.6151184Z >>> CUDAExtension( 2025-09-07T08:19:20.6151442Z ... name='cuda_extension', 2025-09-07T08:19:20.6151813Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T08:19:20.6152184Z ... dlink=True, 2025-09-07T08:19:20.6152472Z ... dlink_libraries=["dlink_lib"], 2025-09-07T08:19:20.6152810Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T08:19:20.6153173Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-09-07T08:19:20.6153419Z 2025-09-07T08:19:20.6153669Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.6154038Z 2025-09-07T08:19:20.6154600Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1420. 2025-09-07T08:19:20.6155525Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.6155901Z 2025-09-07T08:19:20.6156062Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-09-07T08:19:20.6156343Z 2025-09-07T08:19:20.6156585Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T08:19:20.6157175Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-09-07T08:19:20.6157582Z extension. 2025-09-07T08:19:20.6157703Z 2025-09-07T08:19:20.6157919Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T08:19:20.6158317Z constructor. 2025-09-07T08:19:20.6158455Z 2025-09-07T08:19:20.6158542Z .. warning:: 2025-09-07T08:19:20.6158902Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T08:19:20.6159457Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T08:19:20.6159976Z the user's responsibility in their library to not use APIs from 2025-09-07T08:19:20.6160522Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T08:19:20.6161095Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T08:19:20.6161697Z example, to give access to custom ops from python, the library should 2025-09-07T08:19:20.6162148Z register the ops through the dispatcher. 2025-09-07T08:19:20.6162393Z 2025-09-07T08:19:20.6162613Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T08:19:20.6163162Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T08:19:20.6163692Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T08:19:20.6164330Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T08:19:20.6164887Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T08:19:20.6165439Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T08:19:20.6165882Z CPython version of choice. 2025-09-07T08:19:20.6166071Z 2025-09-07T08:19:20.6166170Z Example: 2025-09-07T08:19:20.6166380Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.6166714Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:20.6167211Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-09-07T08:19:20.6167646Z >>> setup( 2025-09-07T08:19:20.6167877Z ... name='xpu_extension', 2025-09-07T08:19:20.6168176Z ... ext_modules=[ 2025-09-07T08:19:20.6168453Z ... SyclExtension( 2025-09-07T08:19:20.6168744Z ... name='xpu_extension', 2025-09-07T08:19:20.6169127Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-09-07T08:19:20.6169598Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-09-07T08:19:20.6169990Z ... ], 2025-09-07T08:19:20.6170214Z ... cmdclass={ 2025-09-07T08:19:20.6170477Z ... 'build_ext': BuildExtension 2025-09-07T08:19:20.6170788Z ... }) 2025-09-07T08:19:20.6170964Z 2025-09-07T08:19:20.6171259Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T08:19:20.6171944Z building process of the extension. If down the road a new card is installed the 2025-09-07T08:19:20.6172577Z extension may need to be recompiled. You can override the default behavior using 2025-09-07T08:19:20.6173241Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-09-07T08:19:20.6174180Z to support: 2025-09-07T08:19:20.6174309Z 2025-09-07T08:19:20.6174526Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-09-07T08:19:20.6174837Z 2025-09-07T08:19:20.6175146Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T08:19:20.6175842Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T08:19:20.6176264Z 2025-09-07T08:19:20.6176400Z Note: Ninja is required to build SyclExtension. 2025-09-07T08:19:20.6176668Z 2025-09-07T08:19:20.6176915Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.6177295Z 2025-09-07T08:19:20.6177858Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1597. 2025-09-07T08:19:20.6178812Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.6179200Z 2025-09-07T08:19:20.6179343Z Load a PyTorch C++ extension just-in-time (JIT). 2025-09-07T08:19:20.6179606Z 2025-09-07T08:19:20.6179814Z To load an extension, a Ninja build file is emitted, which is used to 2025-09-07T08:19:20.6180349Z compile the given sources into a dynamic library. This library is 2025-09-07T08:19:20.6180879Z subsequently loaded into the current Python process as a module and 2025-09-07T08:19:20.6181347Z returned from this function, ready for use. 2025-09-07T08:19:20.6181589Z 2025-09-07T08:19:20.6181795Z By default, the directory to which the build file is emitted and the 2025-09-07T08:19:20.6182360Z resulting library compiled to is ``/torch_extensions/``, where 2025-09-07T08:19:20.6183002Z ```` is the temporary folder on the current platform and ```` 2025-09-07T08:19:20.6183531Z the name of the extension. This location can be overridden in two ways. 2025-09-07T08:19:20.6184074Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-09-07T08:19:20.6184622Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-09-07T08:19:20.6185176Z into subfolders of this directory. Second, if the ``build_directory`` 2025-09-07T08:19:20.6185747Z argument to this function is supplied, it overrides the entire path, i.e. 2025-09-07T08:19:20.6186249Z the library will be compiled into that folder directly. 2025-09-07T08:19:20.6186531Z 2025-09-07T08:19:20.6186740Z To compile the sources, the default system compiler (``c++``) is used, 2025-09-07T08:19:20.6187308Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-09-07T08:19:20.6187900Z additional arguments to the compilation process, ``extra_cflags`` or 2025-09-07T08:19:20.6188459Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-09-07T08:19:20.6189016Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-09-07T08:19:20.6189500Z ``extra_cflags`` to pass further include directories. 2025-09-07T08:19:20.6189761Z 2025-09-07T08:19:20.6190009Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-09-07T08:19:20.6190654Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-09-07T08:19:20.6191187Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-09-07T08:19:20.6191760Z passing the CUDA lib64 directory as a library directory, and linking 2025-09-07T08:19:20.6192241Z ``cudart``. You can pass additional flags to nvcc via 2025-09-07T08:19:20.6192711Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-09-07T08:19:20.6193311Z heuristics for finding the CUDA install directory are used, which usually 2025-09-07T08:19:20.6193881Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-09-07T08:19:20.6194312Z safest option. 2025-09-07T08:19:20.6194447Z 2025-09-07T08:19:20.6194694Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-09-07T08:19:20.6195255Z files (``.sycl``) along with other sources. Such files will be detected 2025-09-07T08:19:20.6195783Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-09-07T08:19:20.6196329Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-09-07T08:19:20.6196848Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-09-07T08:19:20.6197368Z SYCL compiler is expected to be found via system PATH environment 2025-09-07T08:19:20.6197765Z variable. 2025-09-07T08:19:20.6197894Z 2025-09-07T08:19:20.6197974Z Args: 2025-09-07T08:19:20.6198304Z name: The name of the extension to build. This MUST be the same as the 2025-09-07T08:19:20.6198742Z name of the pybind11 module! 2025-09-07T08:19:20.6199143Z sources: A list of relative or absolute paths to C++ source files. 2025-09-07T08:19:20.6199717Z extra_cflags: optional list of compiler flags to forward to the build. 2025-09-07T08:19:20.6200284Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-09-07T08:19:20.6200730Z when building CUDA sources. 2025-09-07T08:19:20.6201155Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-09-07T08:19:20.6201607Z compiler when building SYCL sources. 2025-09-07T08:19:20.6202052Z extra_ldflags: optional list of linker flags to forward to the build. 2025-09-07T08:19:20.6202613Z extra_include_paths: optional list of include directories to forward 2025-09-07T08:19:20.6203049Z to the build. 2025-09-07T08:19:20.6203374Z build_directory: optional path to use as build workspace. 2025-09-07T08:19:20.6203855Z verbose: If ``True``, turns on verbose logging of load steps. 2025-09-07T08:19:20.6204527Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T08:19:20.6205037Z the build. If set to ``None`` (default), this value is 2025-09-07T08:19:20.6205507Z automatically determined based on the existence of ``.cu`` or 2025-09-07T08:19:20.6206003Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-09-07T08:19:20.6206403Z and libraries to be included. 2025-09-07T08:19:20.6206838Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T08:19:20.6207343Z the build. If set to ``None`` (default), this value is 2025-09-07T08:19:20.6207820Z automatically determined based on the existence of ``.sycl`` in 2025-09-07T08:19:20.6208316Z ``sources``. Set it to `True`` to force SYCL headers and 2025-09-07T08:19:20.6208699Z libraries to be included. 2025-09-07T08:19:20.6209107Z is_python_module: If ``True`` (default), imports the produced shared 2025-09-07T08:19:20.6209614Z library as a Python module. If ``False``, behavior depends on 2025-09-07T08:19:20.6210023Z ``is_standalone``. 2025-09-07T08:19:20.6210410Z is_standalone: If ``False`` (default) loads the constructed extension 2025-09-07T08:19:20.6210940Z into the process as a plain dynamic library. If ``True``, build a 2025-09-07T08:19:20.6211365Z standalone executable. 2025-09-07T08:19:20.6211548Z 2025-09-07T08:19:20.6211633Z Returns: 2025-09-07T08:19:20.6211869Z If ``is_python_module`` is ``True``: 2025-09-07T08:19:20.6212270Z Returns the loaded PyTorch extension as a Python module. 2025-09-07T08:19:20.6212562Z 2025-09-07T08:19:20.6212780Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-09-07T08:19:20.6213310Z Returns nothing. (The shared library is loaded into the process as 2025-09-07T08:19:20.6214148Z a side effect.) 2025-09-07T08:19:20.6214321Z 2025-09-07T08:19:20.6214430Z If ``is_standalone`` is ``True``. 2025-09-07T08:19:20.6214852Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-09-07T08:19:20.6215344Z added to the PATH environment variable as a side effect.) 2025-09-07T08:19:20.6215648Z 2025-09-07T08:19:20.6215734Z Example: 2025-09-07T08:19:20.6215954Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.6216265Z >>> from torch.utils.cpp_extension import load 2025-09-07T08:19:20.6216613Z >>> module = load( 2025-09-07T08:19:20.6216859Z ... name='extension', 2025-09-07T08:19:20.6217200Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T08:19:20.6217579Z ... extra_cflags=['-O2'], 2025-09-07T08:19:20.6217867Z ... verbose=True) 2025-09-07T08:19:20.6218027Z 2025-09-07T08:19:20.6218278Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.6218659Z 2025-09-07T08:19:20.6219192Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1885. 2025-09-07T08:19:20.6220139Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.6220522Z 2025-09-07T08:19:20.6220741Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-09-07T08:19:20.6221061Z 2025-09-07T08:19:20.6221303Z This function behaves exactly like :func:`load`, but takes its sources as 2025-09-07T08:19:20.6221870Z strings rather than filenames. These strings are stored to files in the 2025-09-07T08:19:20.6222432Z build directory, after which the behavior of :func:`load_inline` is 2025-09-07T08:19:20.6222862Z identical to :func:`load`. 2025-09-07T08:19:20.6223034Z 2025-09-07T08:19:20.6223128Z See `the 2025-09-07T08:19:20.6223569Z tests `_ 2025-09-07T08:19:20.6224144Z for good examples of using this function. 2025-09-07T08:19:20.6224389Z 2025-09-07T08:19:20.6224618Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-09-07T08:19:20.6225273Z the necessary header includes, as well as the (pybind11) binding code. More 2025-09-07T08:19:20.6225882Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-09-07T08:19:20.6226421Z single ``.cpp`` file. This file is then prepended with ``#include 2025-09-07T08:19:20.6226830Z `` 2025-09-07T08:19:20.6226999Z 2025-09-07T08:19:20.6227222Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-09-07T08:19:20.6227807Z automatically generated for each function specified. ``functions`` can 2025-09-07T08:19:20.6228387Z either be a list of function names, or a dictionary mapping from function 2025-09-07T08:19:20.6228942Z names to docstrings. If a list is given, the name of each function is used 2025-09-07T08:19:20.6229389Z as its docstring. 2025-09-07T08:19:20.6229534Z 2025-09-07T08:19:20.6229760Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-09-07T08:19:20.6230274Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-09-07T08:19:20.6230762Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-09-07T08:19:20.6231313Z separately, but ultimately linked into a single library. Note that no 2025-09-07T08:19:20.6231887Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-09-07T08:19:20.6232454Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-09-07T08:19:20.6233008Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-09-07T08:19:20.6233435Z include its name in ``functions``). 2025-09-07T08:19:20.6233653Z 2025-09-07T08:19:20.6233867Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-09-07T08:19:20.6234423Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-09-07T08:19:20.6234989Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-09-07T08:19:20.6235511Z linked into a single library. Note that no bindings are generated for 2025-09-07T08:19:20.6236062Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-09-07T08:19:20.6236606Z create a C++ function that calls it, and either declare or define this 2025-09-07T08:19:20.6237120Z C++ function in one of the ``cpp_sources`` (and include its name 2025-09-07T08:19:20.6237515Z in ``functions``). 2025-09-07T08:19:20.6237660Z 2025-09-07T08:19:20.6237664Z 2025-09-07T08:19:20.6237668Z 2025-09-07T08:19:20.6237848Z See :func:`load` for a description of arguments omitted below. 2025-09-07T08:19:20.6238151Z 2025-09-07T08:19:20.6238231Z Args: 2025-09-07T08:19:20.6238566Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-09-07T08:19:20.6239126Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-09-07T08:19:20.6239680Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-09-07T08:19:20.6240232Z functions: A list of function names for which to generate function 2025-09-07T08:19:20.6240774Z bindings. If a dictionary is given, it should map function names to 2025-09-07T08:19:20.6241321Z docstrings (which are otherwise just the function names). 2025-09-07T08:19:20.6241843Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T08:19:20.6242332Z the build. If set to ``None`` (default), this value is 2025-09-07T08:19:20.6242810Z automatically determined based on whether ``cuda_sources`` is 2025-09-07T08:19:20.6243286Z provided. Set it to ``True`` to force CUDA headers 2025-09-07T08:19:20.6243665Z and libraries to be included. 2025-09-07T08:19:20.6244170Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T08:19:20.6244679Z the build. If set to ``None`` (default), this value is 2025-09-07T08:19:20.6245163Z automatically determined based on whether ``sycl_sources`` is 2025-09-07T08:19:20.6245731Z provided. Set it to ``True`` to force SYCL headers 2025-09-07T08:19:20.6246111Z and libraries to be included. 2025-09-07T08:19:20.6246516Z with_pytorch_error_handling: Determines whether pytorch error and 2025-09-07T08:19:20.6247045Z warning macros are handled by pytorch instead of pybind. To do 2025-09-07T08:19:20.6247585Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-09-07T08:19:20.6248130Z function. This redirection might cause issues in obscure cases 2025-09-07T08:19:20.6248628Z of cpp. This flag should be set to ``False`` when this redirect 2025-09-07T08:19:20.6249029Z causes issues. 2025-09-07T08:19:20.6249442Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-09-07T08:19:20.6250045Z ``#include `` and ``#include `` lines. 2025-09-07T08:19:20.6250554Z Use this option to improve cold start times when you 2025-09-07T08:19:20.6251078Z already include the necessary headers in your source code. Default: ``False``. 2025-09-07T08:19:20.6251474Z 2025-09-07T08:19:20.6251558Z Example: 2025-09-07T08:19:20.6251829Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:20.6252245Z >>> from torch.utils.cpp_extension import load_inline 2025-09-07T08:19:20.6252594Z >>> source = """ 2025-09-07T08:19:20.6252907Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-09-07T08:19:20.6253262Z return x.sin() + y.sin(); 2025-09-07T08:19:20.6253536Z } 2025-09-07T08:19:20.6253727Z """ 2025-09-07T08:19:20.6253989Z >>> module = load_inline(name='inline_extension', 2025-09-07T08:19:20.6254363Z ... cpp_sources=[source], 2025-09-07T08:19:20.6254717Z ... functions=['sin_add']) 2025-09-07T08:19:20.6254946Z 2025-09-07T08:19:20.6255032Z .. note:: 2025-09-07T08:19:20.6255429Z Since load_inline will just-in-time compile the source code, please ensure 2025-09-07T08:19:20.6256030Z that you have the right toolchains installed in the runtime. For example, 2025-09-07T08:19:20.6256609Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-09-07T08:19:20.6257199Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-09-07T08:19:20.6257809Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-09-07T08:19:20.6258426Z are not included when you install torch and must be additionally installed. 2025-09-07T08:19:20.6258797Z 2025-09-07T08:19:20.6259053Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-09-07T08:19:20.6259657Z the extension. This may use up too many resources on some systems. One 2025-09-07T08:19:20.6260209Z can control the number of workers by setting the `MAX_JOBS` environment 2025-09-07T08:19:20.6260679Z variable to a non-negative number. 2025-09-07T08:19:20.6260905Z 2025-09-07T08:19:20.6261156Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.6261552Z 2025-09-07T08:19:20.6365988Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61. 2025-09-07T08:19:20.6366990Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.6367369Z 2025-09-07T08:19:20.6367677Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-09-07T08:19:20.6368081Z 2025-09-07T08:19:20.6368375Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-09-07T08:19:20.6369043Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-09-07T08:19:20.6369641Z server like load. It can emulate multiple calling threads to a single module 2025-09-07T08:19:20.6370235Z provided. In the future we plan to enhance this component to support inter and 2025-09-07T08:19:20.6371014Z intra-op parallelism as well as multiple models running in a single process. 2025-09-07T08:19:20.6371384Z 2025-09-07T08:19:20.6371636Z Please note that even though nn.Module is supported, it might incur an overhead 2025-09-07T08:19:20.6372231Z from the need to hold GIL every time we execute Python code or pass around 2025-09-07T08:19:20.6372813Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-09-07T08:19:20.6373625Z model for inference deployment it is better to switch to using it in this 2025-09-07T08:19:20.6374075Z benchmark. 2025-09-07T08:19:20.6374198Z 2025-09-07T08:19:20.6374285Z Example:: 2025-09-07T08:19:20.6374415Z 2025-09-07T08:19:20.6374543Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:20.6374917Z >>> from torch.utils import ThroughputBenchmark 2025-09-07T08:19:20.6375302Z >>> bench = ThroughputBenchmark(my_module) 2025-09-07T08:19:20.6375691Z >>> # Pre-populate benchmark's data set with the inputs 2025-09-07T08:19:20.6376069Z >>> for input in inputs: 2025-09-07T08:19:20.6376476Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-09-07T08:19:20.6376950Z ... bench.add_input(input[0], x2=input[1]) 2025-09-07T08:19:20.6377392Z >>> # Inputs supplied above are randomly used during the execution 2025-09-07T08:19:20.6377802Z >>> stats = bench.benchmark( 2025-09-07T08:19:20.6378100Z ... num_calling_threads=4, 2025-09-07T08:19:20.6378403Z ... num_warmup_iters = 100, 2025-09-07T08:19:20.6378704Z ... num_iters = 1000, 2025-09-07T08:19:20.6378962Z ... ) 2025-09-07T08:19:20.6379264Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-09-07T08:19:20.6379735Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-09-07T08:19:20.6380021Z 2025-09-07T08:19:20.6380283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.6380706Z 2025-09-07T08:19:20.7529552Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=18. 2025-09-07T08:19:20.7530519Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.7531110Z Sampler that restricts data loading to a subset of the dataset. 2025-09-07T08:19:20.7531433Z 2025-09-07T08:19:20.7531567Z It is especially useful in conjunction with 2025-09-07T08:19:20.7532066Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-09-07T08:19:20.7532700Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-09-07T08:19:20.7533300Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-09-07T08:19:20.7533779Z original dataset that is exclusive to it. 2025-09-07T08:19:20.7534024Z 2025-09-07T08:19:20.7534127Z .. note:: 2025-09-07T08:19:20.7534500Z Dataset is assumed to be of constant size and that any instance of it always 2025-09-07T08:19:20.7535001Z returns the same elements in the same order. 2025-09-07T08:19:20.7535381Z 2025-09-07T08:19:20.7535464Z Args: 2025-09-07T08:19:20.7535708Z dataset: Dataset used for sampling. 2025-09-07T08:19:20.7536159Z num_replicas (int, optional): Number of processes participating in 2025-09-07T08:19:20.7536744Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-09-07T08:19:20.7537218Z current distributed group. 2025-09-07T08:19:20.7537675Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-09-07T08:19:20.7538235Z By default, :attr:`rank` is retrieved from the current distributed 2025-09-07T08:19:20.7538643Z group. 2025-09-07T08:19:20.7538998Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-09-07T08:19:20.7539435Z indices. 2025-09-07T08:19:20.7539777Z seed (int, optional): random seed used to shuffle the sampler if 2025-09-07T08:19:20.7540396Z :attr:`shuffle=True`. This number should be identical across all 2025-09-07T08:19:20.7540886Z processes in the distributed group. Default: ``0``. 2025-09-07T08:19:20.7541368Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-09-07T08:19:20.7541899Z tail of the data to make it evenly divisible across the number of 2025-09-07T08:19:20.7542415Z replicas. If ``False``, the sampler will add extra indices to make 2025-09-07T08:19:20.7542941Z the data evenly divisible across the replicas. Default: ``False``. 2025-09-07T08:19:20.7543264Z 2025-09-07T08:19:20.7543353Z .. warning:: 2025-09-07T08:19:20.7543690Z In distributed mode, calling the :meth:`set_epoch` method at 2025-09-07T08:19:20.7544268Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-09-07T08:19:20.7544904Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-09-07T08:19:20.7545418Z the same ordering will be always used. 2025-09-07T08:19:20.7545647Z 2025-09-07T08:19:20.7545737Z Example:: 2025-09-07T08:19:20.7545877Z 2025-09-07T08:19:20.7545976Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.7546382Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-09-07T08:19:20.7546893Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-09-07T08:19:20.7547301Z ... sampler=sampler) 2025-09-07T08:19:20.7547652Z >>> for epoch in range(start_epoch, n_epochs): 2025-09-07T08:19:20.7548000Z ... if is_distributed: 2025-09-07T08:19:20.7548311Z ... sampler.set_epoch(epoch) 2025-09-07T08:19:20.7551487Z ... train(loader) 2025-09-07T08:19:20.7551816Z 2025-09-07T08:19:20.7552278Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.7552654Z 2025-09-07T08:19:20.7608014Z msg = Cannot scrape callname=WeightedRandomSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=227. 2025-09-07T08:19:20.7608996Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.7609640Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-09-07T08:19:20.7610018Z 2025-09-07T08:19:20.7610103Z Args: 2025-09-07T08:19:20.7610479Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-09-07T08:19:20.7610991Z num_samples (int): number of samples to draw 2025-09-07T08:19:20.7611545Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-09-07T08:19:20.7612110Z If not, they are drawn without replacement, which means that when a 2025-09-07T08:19:20.7612658Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-09-07T08:19:20.7613152Z generator (Generator): Generator used in sampling. 2025-09-07T08:19:20.7613437Z 2025-09-07T08:19:20.7613632Z Example: 2025-09-07T08:19:20.7613909Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:20.7614256Z >>> list( 2025-09-07T08:19:20.7614497Z ... WeightedRandomSampler( 2025-09-07T08:19:20.7614862Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-09-07T08:19:20.7615210Z ... ) 2025-09-07T08:19:20.7615436Z ... ) 2025-09-07T08:19:20.7615640Z [4, 4, 1, 4, 5] 2025-09-07T08:19:20.7615880Z >>> list( 2025-09-07T08:19:20.7616129Z ... WeightedRandomSampler( 2025-09-07T08:19:20.7616489Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-09-07T08:19:20.7616835Z ... ) 2025-09-07T08:19:20.7617046Z ... ) 2025-09-07T08:19:20.7617264Z [0, 1, 4, 3, 2] 2025-09-07T08:19:20.7617499Z 2025-09-07T08:19:20.7617851Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.7618277Z 2025-09-07T08:19:20.7618830Z msg = Cannot scrape callname=BatchSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=300. 2025-09-07T08:19:20.7619745Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.7620300Z Wraps another sampler to yield a mini-batch of indices. 2025-09-07T08:19:20.7620578Z 2025-09-07T08:19:20.7620674Z Args: 2025-09-07T08:19:20.7621027Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-09-07T08:19:20.7621479Z batch_size (int): Size of mini-batch. 2025-09-07T08:19:20.7621914Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-09-07T08:19:20.7622374Z its size would be less than ``batch_size`` 2025-09-07T08:19:20.7622614Z 2025-09-07T08:19:20.7622712Z Example: 2025-09-07T08:19:20.7622919Z >>> list( 2025-09-07T08:19:20.7623157Z ... BatchSampler( 2025-09-07T08:19:20.7623546Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-09-07T08:19:20.7623951Z ... ) 2025-09-07T08:19:20.7624162Z ... ) 2025-09-07T08:19:20.7624390Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-09-07T08:19:20.7624688Z >>> list( 2025-09-07T08:19:20.7625084Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-09-07T08:19:20.7625533Z ... ) 2025-09-07T08:19:20.7625757Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-09-07T08:19:20.7626045Z 2025-09-07T08:19:20.7626411Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.7626776Z 2025-09-07T08:19:20.7852545Z msg = Cannot scrape callname=IterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py line=56. 2025-09-07T08:19:20.7853561Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.7853959Z 2025-09-07T08:19:20.7854068Z Iterable-style DataPipe. 2025-09-07T08:19:20.7854253Z 2025-09-07T08:19:20.7854507Z All DataPipes that represent an iterable of data samples should subclass this. 2025-09-07T08:19:20.7855140Z This style of DataPipes is particularly useful when data come from a stream, or 2025-09-07T08:19:20.7855871Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-09-07T08:19:20.7856630Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-09-07T08:19:20.7857053Z 2025-09-07T08:19:20.7857280Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-09-07T08:19:20.7857976Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-09-07T08:19:20.7858810Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-09-07T08:19:20.7859579Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-09-07T08:19:20.7860241Z and various state variables within the custom ``IterDataPipe``. 2025-09-07T08:19:20.7860564Z 2025-09-07T08:19:20.7860645Z Note: 2025-09-07T08:19:20.7860974Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-09-07T08:19:20.7861656Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-09-07T08:19:20.7862503Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-09-07T08:19:20.7863259Z The code example below presents details on how this constraint looks in practice. 2025-09-07T08:19:20.7864021Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-09-07T08:19:20.7864521Z 2025-09-07T08:19:20.7864842Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-09-07T08:19:20.7865611Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-09-07T08:19:20.7866404Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-09-07T08:19:20.7866931Z operations in succession. 2025-09-07T08:19:20.7867114Z 2025-09-07T08:19:20.7867248Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-09-07T08:19:20.7867661Z https://github.com/pytorch/data/issues/45 2025-09-07T08:19:20.7867915Z 2025-09-07T08:19:20.7867993Z Note: 2025-09-07T08:19:20.7868340Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-09-07T08:19:20.7868939Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-09-07T08:19:20.7869551Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-09-07T08:19:20.7870129Z different copy of the DataPipe object, so it is often desired to configure 2025-09-07T08:19:20.7870724Z each copy independently to avoid having duplicate data returned from the 2025-09-07T08:19:20.7871321Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-09-07T08:19:20.7871923Z process, returns information about the worker. It can be used in either the 2025-09-07T08:19:20.7872546Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-09-07T08:19:20.7873102Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-09-07T08:19:20.7873621Z 2025-09-07T08:19:20.7873726Z Examples: 2025-09-07T08:19:20.7873936Z General Usage: 2025-09-07T08:19:20.7874183Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.7874576Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T08:19:20.7875101Z >>> dp = IterableWrapper(range(10)) 2025-09-07T08:19:20.7875549Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-09-07T08:19:20.7875983Z >>> map_dp_2 = dp.map( 2025-09-07T08:19:20.7876261Z ... lambda x: x + 1 2025-09-07T08:19:20.7876582Z ... ) # Using functional form (recommended) 2025-09-07T08:19:20.7876926Z >>> list(map_dp_1) 2025-09-07T08:19:20.7877199Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T08:19:20.7877489Z >>> list(map_dp_2) 2025-09-07T08:19:20.7877757Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T08:19:20.7878115Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-09-07T08:19:20.7878480Z >>> list(filter_dp) 2025-09-07T08:19:20.7878733Z [2, 4, 6, 8, 10] 2025-09-07T08:19:20.7879018Z Single Iterator Constraint Example: 2025-09-07T08:19:20.7879452Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T08:19:20.7879915Z >>> source_dp = IterableWrapper(range(10)) 2025-09-07T08:19:20.7880271Z >>> it1 = iter(source_dp) 2025-09-07T08:19:20.7880544Z >>> list(it1) 2025-09-07T08:19:20.7880799Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-09-07T08:19:20.7881137Z >>> it1 = iter(source_dp) 2025-09-07T08:19:20.7881423Z >>> it2 = iter( 2025-09-07T08:19:20.7881661Z ... source_dp 2025-09-07T08:19:20.7881984Z ... ) # The creation of a new iterator invalidates `it1` 2025-09-07T08:19:20.7882348Z >>> next(it2) 2025-09-07T08:19:20.7882587Z 0 2025-09-07T08:19:20.7882877Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-09-07T08:19:20.7883189Z 2025-09-07T08:19:20.7883438Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.7883819Z 2025-09-07T08:19:20.8129293Z msg = Cannot scrape callname=DemultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=375. 2025-09-07T08:19:20.8131371Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.8132093Z 2025-09-07T08:19:20.8133539Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-09-07T08:19:20.8134736Z 2025-09-07T08:19:20.8135064Z A list of the child DataPipes is returned from this operation. 2025-09-07T08:19:20.8135642Z 2025-09-07T08:19:20.8135773Z Args: 2025-09-07T08:19:20.8136204Z datapipe: Iterable DataPipe being filtered 2025-09-07T08:19:20.8136980Z num_instances: number of instances of the DataPipe to create 2025-09-07T08:19:20.8138243Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-09-07T08:19:20.8139730Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-09-07T08:19:20.8140812Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-09-07T08:19:20.8141457Z DataPipes while waiting for their values to be yielded. 2025-09-07T08:19:20.8141928Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-09-07T08:19:20.8142217Z 2025-09-07T08:19:20.8142319Z Examples: 2025-09-07T08:19:20.8142558Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T08:19:20.8142973Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T08:19:20.8143358Z >>> def odd_or_even(n): 2025-09-07T08:19:20.8143631Z ... return n % 2 2025-09-07T08:19:20.8143912Z >>> source_dp = IterableWrapper(range(5)) 2025-09-07T08:19:20.8144360Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-09-07T08:19:20.8144779Z >>> list(dp1) 2025-09-07T08:19:20.8145008Z [0, 2, 4] 2025-09-07T08:19:20.8145212Z >>> list(dp2) 2025-09-07T08:19:20.8145435Z [1, 3] 2025-09-07T08:19:20.8145911Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-09-07T08:19:20.8146876Z >>> def odd_or_even_no_zero(n): 2025-09-07T08:19:20.8147418Z ... return n % 2 if n != 0 else None 2025-09-07T08:19:20.8148010Z >>> dp1, dp2 = source_dp.demux( 2025-09-07T08:19:20.8148782Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-09-07T08:19:20.8149552Z ... ) 2025-09-07T08:19:20.8149907Z >>> list(dp1) 2025-09-07T08:19:20.8150313Z [2, 4] 2025-09-07T08:19:20.8150682Z >>> list(dp2) 2025-09-07T08:19:20.8151058Z [1, 3] 2025-09-07T08:19:20.8151270Z 2025-09-07T08:19:20.8151693Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.8152399Z 2025-09-07T08:19:20.8153693Z msg = Cannot scrape callname=MultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=594. 2025-09-07T08:19:20.8155733Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.8156463Z 2025-09-07T08:19:20.8157035Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-09-07T08:19:20.8157855Z 2025-09-07T08:19:20.8158633Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-09-07T08:19:20.8159844Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-09-07T08:19:20.8160392Z 2025-09-07T08:19:20.8160540Z Args: 2025-09-07T08:19:20.8161504Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-09-07T08:19:20.8162188Z 2025-09-07T08:19:20.8162278Z Example: 2025-09-07T08:19:20.8162532Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T08:19:20.8162951Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T08:19:20.8163326Z >>> dp1, dp2, dp3 = ( 2025-09-07T08:19:20.8163620Z ... IterableWrapper(range(3)), 2025-09-07T08:19:20.8163958Z ... IterableWrapper(range(10, 15)), 2025-09-07T08:19:20.8164414Z ... IterableWrapper(range(20, 25)), 2025-09-07T08:19:20.8164713Z ... ) 2025-09-07T08:19:20.8165019Z >>> list(dp1.mux(dp2, dp3)) 2025-09-07T08:19:20.8165310Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-09-07T08:19:20.8165503Z 2025-09-07T08:19:20.8165766Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.8166136Z 2025-09-07T08:19:20.8166842Z msg = Cannot scrape callname=ZipperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=665. 2025-09-07T08:19:20.8168220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.8168935Z 2025-09-07T08:19:20.8169507Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-09-07T08:19:20.8170324Z 2025-09-07T08:19:20.8170746Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-09-07T08:19:20.8171416Z 2025-09-07T08:19:20.8171577Z Args: 2025-09-07T08:19:20.8172044Z *datapipes: Iterable DataPipes being aggregated 2025-09-07T08:19:20.8172489Z 2025-09-07T08:19:20.8172639Z Example: 2025-09-07T08:19:20.8173073Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T08:19:20.8173990Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T08:19:20.8174716Z >>> dp1, dp2, dp3 = ( 2025-09-07T08:19:20.8175204Z ... IterableWrapper(range(5)), 2025-09-07T08:19:20.8175814Z ... IterableWrapper(range(10, 15)), 2025-09-07T08:19:20.8176447Z ... IterableWrapper(range(20, 25)), 2025-09-07T08:19:20.8176986Z ... ) 2025-09-07T08:19:20.8177376Z >>> list(dp1.zip(dp2, dp3)) 2025-09-07T08:19:20.8177913Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-09-07T08:19:20.8178359Z 2025-09-07T08:19:20.8178972Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.8179684Z 2025-09-07T08:19:20.8182780Z msg = Cannot scrape callname=FileOpenerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py line=18. 2025-09-07T08:19:20.8184122Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.8184578Z 2025-09-07T08:19:20.8184988Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-09-07T08:19:20.8185562Z 2025-09-07T08:19:20.8200209Z Args: 2025-09-07T08:19:20.8200813Z datapipe: Iterable datapipe that provides pathnames 2025-09-07T08:19:20.8201663Z mode: An optional string that specifies the mode in which 2025-09-07T08:19:20.8202614Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-09-07T08:19:20.8203553Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-09-07T08:19:20.8204532Z encoding: An optional string that specifies the encoding of the 2025-09-07T08:19:20.8205639Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-09-07T08:19:20.8206554Z length: Nominal length of the datapipe 2025-09-07T08:19:20.8207096Z 2025-09-07T08:19:20.8207250Z Note: 2025-09-07T08:19:20.8207974Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-09-07T08:19:20.8208894Z to close them explicitly. 2025-09-07T08:19:20.8209257Z 2025-09-07T08:19:20.8209412Z Example: 2025-09-07T08:19:20.8209804Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.8210347Z >>> from torchdata.datapipes.iter import ( 2025-09-07T08:19:20.8210933Z ... FileLister, 2025-09-07T08:19:20.8211374Z ... FileOpener, 2025-09-07T08:19:20.8211823Z ... StreamReader, 2025-09-07T08:19:20.8212265Z ... ) 2025-09-07T08:19:20.8212859Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-09-07T08:19:20.8213634Z >>> dp = FileOpener(dp) 2025-09-07T08:19:20.8214129Z >>> dp = StreamReader(dp) 2025-09-07T08:19:20.8214599Z >>> list(dp) 2025-09-07T08:19:20.8215009Z [('./abc.txt', 'abc')] 2025-09-07T08:19:20.8215332Z 2025-09-07T08:19:20.8215920Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.8216616Z 2025-09-07T08:19:20.8232982Z msg = Cannot scrape callname=GrouperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py line=155. 2025-09-07T08:19:20.8234075Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:20.8234456Z 2025-09-07T08:19:20.8234879Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-09-07T08:19:20.8235399Z 2025-09-07T08:19:20.8235521Z (functional name: ``groupby``). 2025-09-07T08:19:20.8235716Z 2025-09-07T08:19:20.8236119Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-09-07T08:19:20.8236908Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-09-07T08:19:20.8237637Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-09-07T08:19:20.8238388Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-09-07T08:19:20.8238965Z 2025-09-07T08:19:20.8239646Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-09-07T08:19:20.8241164Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-09-07T08:19:20.8241985Z 2025-09-07T08:19:20.8242132Z Args: 2025-09-07T08:19:20.8242568Z datapipe: Iterable datapipe to be grouped 2025-09-07T08:19:20.8243660Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-09-07T08:19:20.8244890Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-09-07T08:19:20.8245942Z resulting in `(key, [items])` otherwise returning [items] 2025-09-07T08:19:20.8246762Z buffer_size: The size of buffer for ungrouped data 2025-09-07T08:19:20.8247767Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-09-07T08:19:20.8249140Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-09-07T08:19:20.8250639Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-09-07T08:19:20.8251226Z when the buffer is full 2025-09-07T08:19:20.8251434Z 2025-09-07T08:19:20.8251521Z Example: 2025-09-07T08:19:20.8251738Z >>> import os 2025-09-07T08:19:20.8251977Z >>> # xdoctest: +SKIP 2025-09-07T08:19:20.8252315Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T08:19:20.8252716Z >>> def group_fn(file): 2025-09-07T08:19:20.8253040Z ... return os.path.basename(file).split(".")[0] 2025-09-07T08:19:20.8253409Z >>> source_dp = IterableWrapper( 2025-09-07T08:19:20.8253769Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-09-07T08:19:20.8254206Z ... ) 2025-09-07T08:19:20.8254478Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-09-07T08:19:20.8254826Z >>> list(dp0) 2025-09-07T08:19:20.8255120Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-09-07T08:19:20.8255588Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-09-07T08:19:20.8256546Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-09-07T08:19:20.8257248Z >>> list(dp1) 2025-09-07T08:19:20.8257794Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T08:19:20.8258945Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-09-07T08:19:20.8260021Z >>> dp2 = source_dp.groupby( 2025-09-07T08:19:20.8260564Z ... group_key_fn=group_fn, 2025-09-07T08:19:20.8261106Z ... buffer_size=3, 2025-09-07T08:19:20.8261663Z ... group_size=3, 2025-09-07T08:19:20.8262158Z ... guaranteed_group_size=2, 2025-09-07T08:19:20.8262691Z ... ) 2025-09-07T08:19:20.8263037Z >>> list(dp2) 2025-09-07T08:19:20.8263547Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T08:19:20.8264085Z 2025-09-07T08:19:20.8264554Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:20.8265283Z 2025-09-07T08:19:20.9855781Z gathering tests 2025-09-07T08:19:20.9867995Z running 732 test(s) 2025-09-07T08:19:20.9899506Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1082 <- wrt source file 2025-09-07T08:19:20.9908986Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0 2025-09-07T08:19:20.9910113Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1118 <- wrt source file 2025-09-07T08:19:20.9914601Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0 2025-09-07T08:19:20.9916776Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1203 <- wrt source file 2025-09-07T08:19:20.9919118Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0 2025-09-07T08:19:20.9921359Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1252 <- wrt source file 2025-09-07T08:19:20.9924003Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0 2025-09-07T08:19:20.9926186Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1289 <- wrt source file 2025-09-07T08:19:20.9928502Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0 2025-09-07T08:19:20.9930778Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1444 <- wrt source file 2025-09-07T08:19:20.9933303Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2025-09-07T08:19:20.9935631Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2568 <- wrt source file 2025-09-07T08:19:20.9937702Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0 2025-09-07T08:19:20.9939972Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2841 <- wrt source file 2025-09-07T08:19:20.9942512Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2025-09-07T08:19:20.9944921Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2025-09-07T08:19:20.9947437Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0 2025-09-07T08:19:20.9949954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2025-09-07T08:19:20.9952322Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0 2025-09-07T08:19:20.9954381Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2025-09-07T08:19:20.9956283Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0 2025-09-07T08:19:20.9957999Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 138 <- wrt source file 2025-09-07T08:19:20.9959935Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0 2025-09-07T08:19:20.9962178Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 208 <- wrt source file 2025-09-07T08:19:21.0502779Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0 2025-09-07T08:19:21.0504979Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2025-09-07T08:19:21.0507087Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0 2025-09-07T08:19:21.0508975Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 649 <- wrt source file 2025-09-07T08:19:21.0517705Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0 2025-09-07T08:19:21.0519990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 706 <- wrt source file 2025-09-07T08:19:21.0539856Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2025-09-07T08:19:21.0542373Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1333 <- wrt source file 2025-09-07T08:19:21.0652040Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0 2025-09-07T08:19:21.0656506Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1378 <- wrt source file 2025-09-07T08:19:21.0662380Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0 2025-09-07T08:19:21.0664882Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1451 <- wrt source file 2025-09-07T08:19:21.0678836Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0 2025-09-07T08:19:21.0680797Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1481 <- wrt source file 2025-09-07T08:19:21.0686210Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2025-09-07T08:19:21.0688437Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2025-09-07T08:19:21.0708210Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0 2025-09-07T08:19:21.0710302Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file 2025-09-07T08:19:21.0716241Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0 2025-09-07T08:19:21.0718373Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file 2025-09-07T08:19:21.0720696Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0 2025-09-07T08:19:21.0722860Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 144 <- wrt source file 2025-09-07T08:19:21.0734786Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0 2025-09-07T08:19:21.0736660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 258 <- wrt source file 2025-09-07T08:19:21.0753142Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0 2025-09-07T08:19:21.0755249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 992 <- wrt source file 2025-09-07T08:19:21.0766588Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0 2025-09-07T08:19:21.0768774Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1267 <- wrt source file 2025-09-07T08:19:21.0779811Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0 2025-09-07T08:19:21.0781976Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1351 <- wrt source file 2025-09-07T08:19:21.0788541Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0 2025-09-07T08:19:21.0790720Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1385 <- wrt source file 2025-09-07T08:19:21.0800415Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0 2025-09-07T08:19:21.0802526Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1441 <- wrt source file 2025-09-07T08:19:21.0815643Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0 2025-09-07T08:19:21.0817616Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1482 <- wrt source file 2025-09-07T08:19:21.0834041Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0 2025-09-07T08:19:21.0836007Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1520 <- wrt source file 2025-09-07T08:19:21.0853075Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0 2025-09-07T08:19:21.0855060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1560 <- wrt source file 2025-09-07T08:19:21.0876057Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0 2025-09-07T08:19:21.0878128Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1735 <- wrt source file 2025-09-07T08:19:21.0909707Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0 2025-09-07T08:19:21.0911698Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1903 <- wrt source file 2025-09-07T08:19:21.0937638Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0 2025-09-07T08:19:21.0939852Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2003 <- wrt source file 2025-09-07T08:19:21.0942089Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0 2025-09-07T08:19:21.0944183Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2104 <- wrt source file 2025-09-07T08:19:21.0946181Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0 2025-09-07T08:19:21.0948111Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 473 <- wrt source file 2025-09-07T08:19:21.0949904Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0 2025-09-07T08:19:21.0951610Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 533 <- wrt source file 2025-09-07T08:19:21.0953040Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0 2025-09-07T08:19:21.0954137Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 153 <- wrt source file 2025-09-07T08:19:21.0955315Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0 2025-09-07T08:19:21.0956528Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 247 <- wrt source file 2025-09-07T08:19:21.0968143Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2025-09-07T08:19:21.0970345Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 307 <- wrt source file 2025-09-07T08:19:21.0973484Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0 2025-09-07T08:19:21.0975633Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 512 <- wrt source file 2025-09-07T08:19:21.0987393Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0 2025-09-07T08:19:21.0989726Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 618 <- wrt source file 2025-09-07T08:19:21.1004440Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0 2025-09-07T08:19:21.1006980Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 799 <- wrt source file 2025-09-07T08:19:21.1009370Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0 2025-09-07T08:19:21.1011516Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0, line 867 <- wrt source file 2025-09-07T08:19:21.1013657Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0 2025-09-07T08:19:21.1015813Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 1232 <- wrt source file 2025-09-07T08:19:21.1095668Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0 2025-09-07T08:19:21.1097995Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1321 <- wrt source file 2025-09-07T08:19:21.1240271Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0 2025-09-07T08:19:21.1242439Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 116 <- wrt source file 2025-09-07T08:19:21.1248282Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0 2025-09-07T08:19:21.1250328Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 423 <- wrt source file 2025-09-07T08:19:21.1287387Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0 2025-09-07T08:19:21.1289637Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1578 <- wrt source file 2025-09-07T08:19:21.1292044Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0 2025-09-07T08:19:21.1294340Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1713 <- wrt source file 2025-09-07T08:19:21.1296689Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0 2025-09-07T08:19:21.1299095Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1961 <- wrt source file 2025-09-07T08:19:21.1323292Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2025-09-07T08:19:21.1325610Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1980 <- wrt source file 2025-09-07T08:19:21.1332017Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0 2025-09-07T08:19:21.1334470Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2025-09-07T08:19:21.1336849Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0 2025-09-07T08:19:21.1339031Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file 2025-09-07T08:19:21.1341321Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0 2025-09-07T08:19:21.1343561Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file 2025-09-07T08:19:21.1345836Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0 2025-09-07T08:19:21.1348068Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file 2025-09-07T08:19:21.1350255Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0 2025-09-07T08:19:21.1352577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file 2025-09-07T08:19:21.1355167Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0 2025-09-07T08:19:21.1357397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 950 <- wrt source file 2025-09-07T08:19:21.1359559Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0 2025-09-07T08:19:21.1361762Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file 2025-09-07T08:19:21.1364185Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0 2025-09-07T08:19:21.1366673Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 320 <- wrt source file 2025-09-07T08:19:21.1369032Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2025-09-07T08:19:21.1371345Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 357 <- wrt source file 2025-09-07T08:19:21.1373706Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0 2025-09-07T08:19:21.1375845Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 113 <- wrt source file 2025-09-07T08:19:21.1378384Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2025-09-07T08:19:21.1380758Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0, line 249 <- wrt source file 2025-09-07T08:19:21.1383193Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0 2025-09-07T08:19:21.1385372Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 127 <- wrt source file 2025-09-07T08:19:21.1387619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2025-09-07T08:19:21.1389838Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 183 <- wrt source file 2025-09-07T08:19:21.3889523Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2025-09-07T08:19:21.3891152Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 413 <- wrt source file 2025-09-07T08:19:21.3892555Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2025-09-07T08:19:21.3893812Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 445 <- wrt source file 2025-09-07T08:19:21.3895235Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0 2025-09-07T08:19:21.3896629Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 466 <- wrt source file 2025-09-07T08:19:21.3898104Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2025-09-07T08:19:21.3899968Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0, line 484 <- wrt source file 2025-09-07T08:19:21.3901954Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0 2025-09-07T08:19:21.3903711Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 499 <- wrt source file 2025-09-07T08:19:21.3905570Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0 2025-09-07T08:19:21.3907265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 514 <- wrt source file 2025-09-07T08:19:21.3909010Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0 2025-09-07T08:19:21.3910757Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py::_compile_kernel:0, line 1760 <- wrt source file 2025-09-07T08:19:21.3912021Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py::_compile_kernel:0 2025-09-07T08:19:21.3913184Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0, line 349 <- wrt source file 2025-09-07T08:19:21.3914319Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0 2025-09-07T08:19:21.3915438Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0, line 419 <- wrt source file 2025-09-07T08:19:21.3916578Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0 2025-09-07T08:19:21.3917775Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0, line 576 <- wrt source file 2025-09-07T08:19:21.3919063Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0 2025-09-07T08:19:21.3920366Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 197 <- wrt source file 2025-09-07T08:19:21.3921796Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2025-09-07T08:19:21.3923098Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 261 <- wrt source file 2025-09-07T08:19:21.3924570Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0 2025-09-07T08:19:21.3925866Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file 2025-09-07T08:19:21.3927135Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0 2025-09-07T08:19:21.3928381Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file 2025-09-07T08:19:21.3929676Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0 2025-09-07T08:19:21.3931209Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file 2025-09-07T08:19:21.3935257Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2025-09-07T08:19:21.3936577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 61 <- wrt source file 2025-09-07T08:19:21.3960849Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2025-09-07T08:19:21.3962217Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file 2025-09-07T08:19:21.3966490Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0 2025-09-07T08:19:21.3967653Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file 2025-09-07T08:19:21.4031084Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0 2025-09-07T08:19:21.4032491Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 405 <- wrt source file 2025-09-07T08:19:21.4040938Z W0907 08:19:21.403000 854 site-packages/torch/fx/_symbolic_trace.py:52] is_fx_tracing will return true for both fx.symbolic_trace and torch.export. Please use is_fx_tracing_symbolic_tracing() for specifically fx.symbolic_trace or torch.compiler.is_compiling() for specifically torch.export/compile. 2025-09-07T08:19:21.4060482Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2025-09-07T08:19:21.4061752Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0, line 481 <- wrt source file 2025-09-07T08:19:21.4081927Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0 2025-09-07T08:19:21.4083245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 475 <- wrt source file 2025-09-07T08:19:21.4094723Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2025-09-07T08:19:21.4096325Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 349 <- wrt source file 2025-09-07T08:19:21.4100810Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2025-09-07T08:19:21.4103601Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 322 <- wrt source file 2025-09-07T08:19:21.4106683Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2025-09-07T08:19:21.4109586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1410 <- wrt source file 2025-09-07T08:19:21.4112448Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0 2025-09-07T08:19:21.4115454Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0, line 158 <- wrt source file 2025-09-07T08:19:21.4118646Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0 2025-09-07T08:19:21.4121461Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 768 <- wrt source file 2025-09-07T08:19:21.5985212Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2025-09-07T08:19:21.5986777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file 2025-09-07T08:19:21.5988600Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2025-09-07T08:19:21.5990050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 233 <- wrt source file 2025-09-07T08:19:21.6025051Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2025-09-07T08:19:21.6026370Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 475 <- wrt source file 2025-09-07T08:19:21.6084598Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2025-09-07T08:19:21.6086017Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2025-09-07T08:19:21.6556836Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2025-09-07T08:19:21.6558132Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2025-09-07T08:19:21.6617222Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2025-09-07T08:19:21.6618517Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2025-09-07T08:19:21.6635715Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2025-09-07T08:19:21.6637205Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2025-09-07T08:19:21.6638752Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2025-09-07T08:19:21.6640075Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file 2025-09-07T08:19:21.6810820Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2025-09-07T08:19:21.6812351Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file 2025-09-07T08:19:21.6814494Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0 2025-09-07T08:19:21.6816259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2025-09-07T08:19:21.6818357Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2025-09-07T08:19:21.6820815Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0, line 1131 <- wrt source file 2025-09-07T08:19:21.6822560Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0 2025-09-07T08:19:21.6824224Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0, line 1186 <- wrt source file 2025-09-07T08:19:21.6826772Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0 2025-09-07T08:19:21.6828625Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 186 <- wrt source file 2025-09-07T08:19:21.6830988Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2025-09-07T08:19:21.6832943Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 322 <- wrt source file 2025-09-07T08:19:21.6834506Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2025-09-07T08:19:21.6835950Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 155 <- wrt source file 2025-09-07T08:19:21.6837183Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0 2025-09-07T08:19:21.6838460Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0, line 80 <- wrt source file 2025-09-07T08:19:21.6839873Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0 2025-09-07T08:19:21.6841142Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0, line 79 <- wrt source file 2025-09-07T08:19:21.6842342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0 2025-09-07T08:19:21.6843522Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 156 <- wrt source file 2025-09-07T08:19:21.6844825Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0 2025-09-07T08:19:21.6846079Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0, line 474 <- wrt source file 2025-09-07T08:19:21.6847406Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0 2025-09-07T08:19:21.6848717Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/codecache.py::WritableTempFile:0, line 374 <- wrt source file 2025-09-07T08:19:21.6850103Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/codecache.py::WritableTempFile:0 2025-09-07T08:19:21.6851557Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1721 <- wrt source file 2025-09-07T08:19:21.6853087Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0 2025-09-07T08:19:21.6854539Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0, line 3473 <- wrt source file 2025-09-07T08:19:21.6855978Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0 2025-09-07T08:19:21.6857490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/template_heuristics/registry.py::register_template_heuristic:0, line 54 <- wrt source file 2025-09-07T08:19:21.6859107Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/template_heuristics/registry.py::register_template_heuristic:0 2025-09-07T08:19:21.6860494Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 98 <- wrt source file 2025-09-07T08:19:21.7320008Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0 2025-09-07T08:19:21.7321492Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 238 <- wrt source file 2025-09-07T08:19:21.7401212Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2025-09-07T08:19:21.7402699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 307 <- wrt source file 2025-09-07T08:19:21.7404338Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2025-09-07T08:19:21.7405766Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 541 <- wrt source file 2025-09-07T08:19:21.7559755Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2025-09-07T08:19:21.7561158Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 709 <- wrt source file 2025-09-07T08:19:21.7721072Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2025-09-07T08:19:21.7723006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0, line 795 <- wrt source file 2025-09-07T08:19:21.7724607Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0 2025-09-07T08:19:21.7726007Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 230 <- wrt source file 2025-09-07T08:19:21.7727421Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2025-09-07T08:19:21.7728802Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 175 <- wrt source file 2025-09-07T08:19:21.7792498Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2025-09-07T08:19:21.7793883Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 51 <- wrt source file 2025-09-07T08:19:21.7798523Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0 2025-09-07T08:19:21.7799973Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 459 <- wrt source file 2025-09-07T08:19:21.7801197Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0 2025-09-07T08:19:21.7802423Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 171 <- wrt source file 2025-09-07T08:19:21.7853255Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2025-09-07T08:19:21.7854549Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 1008 <- wrt source file 2025-09-07T08:19:21.7904146Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2025-09-07T08:19:21.7905477Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1073 <- wrt source file 2025-09-07T08:19:21.7906838Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2025-09-07T08:19:21.7908148Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1294 <- wrt source file 2025-09-07T08:19:21.7923059Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2025-09-07T08:19:21.7924603Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1360 <- wrt source file 2025-09-07T08:19:21.7927288Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2025-09-07T08:19:21.7928660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1423 <- wrt source file 2025-09-07T08:19:21.7931646Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2025-09-07T08:19:21.7932995Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1468 <- wrt source file 2025-09-07T08:19:21.7934249Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2025-09-07T08:19:21.7935484Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1578 <- wrt source file 2025-09-07T08:19:21.7937689Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2025-09-07T08:19:21.7939433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 95 <- wrt source file 2025-09-07T08:19:21.7941081Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0 2025-09-07T08:19:21.7942624Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file 2025-09-07T08:19:21.7944313Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2025-09-07T08:19:21.7945730Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 30 <- wrt source file 2025-09-07T08:19:21.7947256Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2025-09-07T08:19:21.7948818Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 24 <- wrt source file 2025-09-07T08:19:21.7950486Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2025-09-07T08:19:21.7952077Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file 2025-09-07T08:19:21.7953658Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2025-09-07T08:19:21.7955238Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 67 <- wrt source file 2025-09-07T08:19:21.7956923Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2025-09-07T08:19:21.7958493Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 142 <- wrt source file 2025-09-07T08:19:21.7960073Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2025-09-07T08:19:21.7961490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 30 <- wrt source file 2025-09-07T08:19:21.7964690Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2025-09-07T08:19:21.7966263Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 413 <- wrt source file 2025-09-07T08:19:21.7995701Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2025-09-07T08:19:21.7997945Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 211 <- wrt source file 2025-09-07T08:19:21.7999745Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2025-09-07T08:19:21.8001111Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 283 <- wrt source file 2025-09-07T08:19:21.8002415Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2025-09-07T08:19:21.8003685Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 359 <- wrt source file 2025-09-07T08:19:21.8005303Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2025-09-07T08:19:21.8006692Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file 2025-09-07T08:19:21.8008158Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2025-09-07T08:19:21.8009972Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file 2025-09-07T08:19:21.8012128Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2025-09-07T08:19:21.8014177Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 43 <- wrt source file 2025-09-07T08:19:21.8016428Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2025-09-07T08:19:21.8018520Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 124 <- wrt source file 2025-09-07T08:19:21.8019974Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2025-09-07T08:19:21.8021396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 209 <- wrt source file 2025-09-07T08:19:21.8023663Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2025-09-07T08:19:21.8025651Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 296 <- wrt source file 2025-09-07T08:19:21.8027697Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2025-09-07T08:19:21.8029288Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 378 <- wrt source file 2025-09-07T08:19:21.8030828Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2025-09-07T08:19:21.8032514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 460 <- wrt source file 2025-09-07T08:19:21.8035594Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2025-09-07T08:19:21.8038506Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file 2025-09-07T08:19:21.8041403Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2025-09-07T08:19:21.8044258Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 515 <- wrt source file 2025-09-07T08:19:21.8047196Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2025-09-07T08:19:21.8050387Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file 2025-09-07T08:19:21.8053582Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2025-09-07T08:19:21.8056557Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1206 <- wrt source file 2025-09-07T08:19:21.8059858Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2025-09-07T08:19:21.8063172Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1273 <- wrt source file 2025-09-07T08:19:21.8066453Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2025-09-07T08:19:21.8069270Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1326 <- wrt source file 2025-09-07T08:19:21.8072117Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2025-09-07T08:19:21.8075006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file 2025-09-07T08:19:21.8077784Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2025-09-07T08:19:21.8080412Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 505 <- wrt source file 2025-09-07T08:19:21.8083175Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2025-09-07T08:19:21.8085880Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file 2025-09-07T08:19:21.8088491Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2025-09-07T08:19:21.8091262Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 892 <- wrt source file 2025-09-07T08:19:21.8094106Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2025-09-07T08:19:21.8096918Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1014 <- wrt source file 2025-09-07T08:19:21.8099921Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2025-09-07T08:19:21.8102727Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1140 <- wrt source file 2025-09-07T08:19:21.8105661Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2025-09-07T08:19:21.8108497Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 111 <- wrt source file 2025-09-07T08:19:21.8111403Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2025-09-07T08:19:21.8114363Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 275 <- wrt source file 2025-09-07T08:19:21.8117357Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2025-09-07T08:19:21.8120445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 23 <- wrt source file 2025-09-07T08:19:21.8123637Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2025-09-07T08:19:21.8126795Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 176 <- wrt source file 2025-09-07T08:19:21.8129964Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2025-09-07T08:19:21.8132836Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file 2025-09-07T08:19:21.8135515Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2025-09-07T08:19:21.8138686Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file 2025-09-07T08:19:21.8142278Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2025-09-07T08:19:21.8145503Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 24 <- wrt source file 2025-09-07T08:19:21.8166007Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2025-09-07T08:19:21.8168857Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file 2025-09-07T08:19:21.8171360Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2025-09-07T08:19:21.8173912Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 176 <- wrt source file 2025-09-07T08:19:21.8176317Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2025-09-07T08:19:21.8178803Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file 2025-09-07T08:19:21.8181782Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2025-09-07T08:19:21.8184397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file 2025-09-07T08:19:21.8188901Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2025-09-07T08:19:21.8191894Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file 2025-09-07T08:19:21.8194911Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2025-09-07T08:19:21.8197936Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file 2025-09-07T08:19:21.8201099Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2025-09-07T08:19:21.8203904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 110 <- wrt source file 2025-09-07T08:19:21.8206603Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0 2025-09-07T08:19:21.8209364Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 132 <- wrt source file 2025-09-07T08:19:21.8212198Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2025-09-07T08:19:21.8214915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file 2025-09-07T08:19:21.8217509Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2025-09-07T08:19:21.8220108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 288 <- wrt source file 2025-09-07T08:19:21.8222814Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2025-09-07T08:19:21.8225501Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 427 <- wrt source file 2025-09-07T08:19:21.8228250Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2025-09-07T08:19:21.8230932Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 608 <- wrt source file 2025-09-07T08:19:21.8233674Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2025-09-07T08:19:21.8236452Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 668 <- wrt source file 2025-09-07T08:19:21.8239420Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2025-09-07T08:19:21.8242472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 720 <- wrt source file 2025-09-07T08:19:21.8245770Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2025-09-07T08:19:21.8248721Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 51 <- wrt source file 2025-09-07T08:19:21.8251517Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2025-09-07T08:19:21.8254285Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 130 <- wrt source file 2025-09-07T08:19:21.8257157Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2025-09-07T08:19:21.8259930Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 228 <- wrt source file 2025-09-07T08:19:21.8262702Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2025-09-07T08:19:21.8265382Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 172 <- wrt source file 2025-09-07T08:19:21.8268066Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2025-09-07T08:19:21.8270710Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 544 <- wrt source file 2025-09-07T08:19:21.8273672Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2025-09-07T08:19:21.8276437Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 566 <- wrt source file 2025-09-07T08:19:21.8279184Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2025-09-07T08:19:21.8281858Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 580 <- wrt source file 2025-09-07T08:19:21.8284639Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2025-09-07T08:19:21.8287304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 602 <- wrt source file 2025-09-07T08:19:21.8290033Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2025-09-07T08:19:21.8292647Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 729 <- wrt source file 2025-09-07T08:19:21.8295369Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2025-09-07T08:19:21.8298244Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file 2025-09-07T08:19:21.8301453Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0 2025-09-07T08:19:21.8304607Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0, line 102 <- wrt source file 2025-09-07T08:19:21.8307860Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0 2025-09-07T08:19:21.8310991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0, line 148 <- wrt source file 2025-09-07T08:19:21.8314163Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0 2025-09-07T08:19:21.8317216Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 189 <- wrt source file 2025-09-07T08:19:21.8320280Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0 2025-09-07T08:19:21.8323428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 436 <- wrt source file 2025-09-07T08:19:21.8326812Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2025-09-07T08:19:21.8329676Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 28 <- wrt source file 2025-09-07T08:19:21.8332287Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2025-09-07T08:19:21.8334753Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 82 <- wrt source file 2025-09-07T08:19:21.8337209Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0 2025-09-07T08:19:21.8339680Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 151 <- wrt source file 2025-09-07T08:19:21.8342170Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2025-09-07T08:19:21.8344602Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 187 <- wrt source file 2025-09-07T08:19:21.8347069Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0 2025-09-07T08:19:21.8349695Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 71 <- wrt source file 2025-09-07T08:19:21.8352544Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2025-09-07T08:19:21.8355361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 115 <- wrt source file 2025-09-07T08:19:21.8358236Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2025-09-07T08:19:21.8360961Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 167 <- wrt source file 2025-09-07T08:19:21.8363682Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2025-09-07T08:19:21.8366586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 214 <- wrt source file 2025-09-07T08:19:21.8369598Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2025-09-07T08:19:21.8372568Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 243 <- wrt source file 2025-09-07T08:19:21.8375683Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2025-09-07T08:19:21.8378309Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 485 <- wrt source file 2025-09-07T08:19:21.8380725Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0 2025-09-07T08:19:21.8383056Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file 2025-09-07T08:19:21.8385390Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0 2025-09-07T08:19:21.8387707Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file 2025-09-07T08:19:21.8390055Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0 2025-09-07T08:19:21.8392396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file 2025-09-07T08:19:21.8394850Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0 2025-09-07T08:19:21.8397244Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 894 <- wrt source file 2025-09-07T08:19:21.8399736Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0 2025-09-07T08:19:21.8402123Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1010 <- wrt source file 2025-09-07T08:19:21.8404551Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0 2025-09-07T08:19:21.8406865Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1109 <- wrt source file 2025-09-07T08:19:21.8409210Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0 2025-09-07T08:19:21.8411482Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2025-09-07T08:19:21.8413840Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0 2025-09-07T08:19:21.8416217Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file 2025-09-07T08:19:21.8418678Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2025-09-07T08:19:21.8421211Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file 2025-09-07T08:19:21.8423782Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2025-09-07T08:19:21.8426293Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 246 <- wrt source file 2025-09-07T08:19:21.8428862Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2025-09-07T08:19:21.8431274Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file 2025-09-07T08:19:21.8433725Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0 2025-09-07T08:19:21.8436160Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 110 <- wrt source file 2025-09-07T08:19:21.8438739Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0 2025-09-07T08:19:21.8441266Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 147 <- wrt source file 2025-09-07T08:19:21.8443973Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2025-09-07T08:19:21.8446576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 283 <- wrt source file 2025-09-07T08:19:21.8449130Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2025-09-07T08:19:21.8451548Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 353 <- wrt source file 2025-09-07T08:19:21.8453917Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0 2025-09-07T08:19:21.8456437Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 410 <- wrt source file 2025-09-07T08:19:21.8459222Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2025-09-07T08:19:21.8461884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 487 <- wrt source file 2025-09-07T08:19:21.8464577Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2025-09-07T08:19:21.8467283Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 753 <- wrt source file 2025-09-07T08:19:21.8470092Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2025-09-07T08:19:21.8472647Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 182 <- wrt source file 2025-09-07T08:19:21.8475169Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0 2025-09-07T08:19:21.8477517Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0, line 880 <- wrt source file 2025-09-07T08:19:21.8479916Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0 2025-09-07T08:19:21.8482325Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 953 <- wrt source file 2025-09-07T08:19:21.8484842Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2025-09-07T08:19:21.8487188Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0, line 42 <- wrt source file 2025-09-07T08:19:21.8489569Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0 2025-09-07T08:19:21.8491941Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0, line 58 <- wrt source file 2025-09-07T08:19:21.8494450Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0 2025-09-07T08:19:21.8496693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file 2025-09-07T08:19:21.8498850Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0 2025-09-07T08:19:21.8501117Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file 2025-09-07T08:19:21.8503537Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0 2025-09-07T08:19:21.8505907Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file 2025-09-07T08:19:21.8508342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2025-09-07T08:19:21.8510732Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 140 <- wrt source file 2025-09-07T08:19:21.8513162Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2025-09-07T08:19:21.8515679Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 173 <- wrt source file 2025-09-07T08:19:21.8518388Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2025-09-07T08:19:21.8520885Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2025-09-07T08:19:21.8523230Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0 2025-09-07T08:19:21.8525668Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_distributed_c10d.py::__doc__:0, line 11 <- wrt source file 2025-09-07T08:19:21.8528268Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_distributed_c10d.py::__doc__:0 2025-09-07T08:19:21.8530822Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 410 <- wrt source file 2025-09-07T08:19:21.8533408Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2025-09-07T08:19:21.8536100Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 955 <- wrt source file 2025-09-07T08:19:21.8539005Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2025-09-07T08:19:21.8541812Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 1101 <- wrt source file 2025-09-07T08:19:21.8544526Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2025-09-07T08:19:21.8547316Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2573 <- wrt source file 2025-09-07T08:19:21.8550221Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2025-09-07T08:19:21.8553027Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0, line 2675 <- wrt source file 2025-09-07T08:19:21.8555876Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0 2025-09-07T08:19:21.8558645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3146 <- wrt source file 2025-09-07T08:19:21.8561503Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2025-09-07T08:19:21.8564361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3380 <- wrt source file 2025-09-07T08:19:21.8567201Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2025-09-07T08:19:21.8569973Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3497 <- wrt source file 2025-09-07T08:19:21.8572808Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0 2025-09-07T08:19:21.8575780Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3643 <- wrt source file 2025-09-07T08:19:21.8578754Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0 2025-09-07T08:19:21.8581645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3766 <- wrt source file 2025-09-07T08:19:21.8584619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 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* DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0, line 47 <- wrt source file 2025-09-07T08:19:21.8641342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0 2025-09-07T08:19:21.8644231Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 53 <- wrt source file 2025-09-07T08:19:21.8647414Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2025-09-07T08:19:21.8650330Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0, line 66 <- wrt source file 2025-09-07T08:19:21.8653106Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0 2025-09-07T08:19:21.8655856Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file 2025-09-07T08:19:21.8658657Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2025-09-07T08:19:21.8661509Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate_with_fsdp.py::replicate:0, line 247 <- wrt source file 2025-09-07T08:19:21.8664529Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate_with_fsdp.py::replicate:0 2025-09-07T08:19:21.8667643Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 31 <- wrt source file 2025-09-07T08:19:21.8671035Z * SKIPPED: 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file 2025-09-07T08:19:21.9140273Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2025-09-07T08:19:21.9142657Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 21 <- wrt source file 2025-09-07T08:19:21.9144984Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0 2025-09-07T08:19:21.9147394Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 31 <- wrt source file 2025-09-07T08:19:21.9149962Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0 2025-09-07T08:19:21.9152529Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0, line 42 <- wrt source file 2025-09-07T08:19:21.9155228Z * SUCCESS: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0 2025-09-07T08:19:21.9205867Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0, line 26 <- wrt source file 2025-09-07T08:19:21.9208846Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0 2025-09-07T08:19:21.9211562Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0, line 36 <- wrt source file 2025-09-07T08:19:21.9214199Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0 2025-09-07T08:19:21.9216645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 23 <- wrt source file 2025-09-07T08:19:21.9219076Z * SUCCESS: 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2025-09-07T08:19:21.9348338Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::Dim:0, line 103 <- wrt source file 2025-09-07T08:19:21.9350764Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::Dim:0 2025-09-07T08:19:21.9353291Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 715 <- wrt source file 2025-09-07T08:19:21.9355983Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0 2025-09-07T08:19:21.9358639Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1, line 731 <- wrt source file 2025-09-07T08:19:21.9361352Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1 2025-09-07T08:19:21.9364027Z * DOCTEST : 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2025-09-07T08:19:21.9517545Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0 2025-09-07T08:19:21.9520876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 15 <- wrt source file 2025-09-07T08:19:21.9524340Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0 2025-09-07T08:19:21.9527546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 42 <- wrt source file 2025-09-07T08:19:21.9530756Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0 2025-09-07T08:19:21.9533921Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 70 <- wrt source file 2025-09-07T08:19:21.9537297Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0 2025-09-07T08:19:21.9540461Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 95 <- wrt source file 2025-09-07T08:19:21.9543599Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0 2025-09-07T08:19:21.9546767Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 67 <- wrt source file 2025-09-07T08:19:21.9550085Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0 2025-09-07T08:19:21.9553588Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 20 <- wrt source file 2025-09-07T08:19:21.9557262Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0 2025-09-07T08:19:21.9561014Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 113 <- wrt source file 2025-09-07T08:19:21.9564997Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0 2025-09-07T08:19:21.9568980Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0, line 138 <- wrt source file 2025-09-07T08:19:21.9573153Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0 2025-09-07T08:19:21.9577280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0, line 191 <- wrt source file 2025-09-07T08:19:21.9581317Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0 2025-09-07T08:19:21.9585466Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 304 <- wrt source file 2025-09-07T08:19:21.9589668Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0 2025-09-07T08:19:21.9593740Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 434 <- wrt source file 2025-09-07T08:19:21.9597736Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0 2025-09-07T08:19:21.9601570Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 18 <- wrt source file 2025-09-07T08:19:21.9605480Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0 2025-09-07T08:19:21.9609174Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 41 <- wrt source file 2025-09-07T08:19:21.9612895Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0 2025-09-07T08:19:21.9616684Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 68 <- wrt source file 2025-09-07T08:19:21.9620478Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0 2025-09-07T08:19:21.9624156Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 87 <- wrt source file 2025-09-07T08:19:21.9627820Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2025-09-07T08:19:21.9631519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 117 <- wrt source file 2025-09-07T08:19:21.9635243Z * SUCCESS: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 129 <- wrt source file 2025-09-07T08:19:21.9657131Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0 2025-09-07T08:19:21.9660136Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 99 <- wrt source file 2025-09-07T08:19:21.9663038Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2025-09-07T08:19:21.9665872Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0, line 89 <- wrt source file 2025-09-07T08:19:21.9668862Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0 2025-09-07T08:19:21.9672334Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 51 <- wrt source file 2025-09-07T08:19:21.9676524Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0 2025-09-07T08:19:21.9680039Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2025-09-07T08:19:21.9683191Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2025-09-07T08:19:21.9686353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file 2025-09-07T08:19:21.9689576Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2025-09-07T08:19:22.0261342Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2174 <- wrt source file 2025-09-07T08:19:22.0270617Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0 2025-09-07T08:19:22.0273699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0, line 2478 <- wrt source file 2025-09-07T08:19:22.0279629Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0 2025-09-07T08:19:22.0282008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0, line 2618 <- wrt source file 2025-09-07T08:19:22.0289927Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0 2025-09-07T08:19:22.0292233Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3051 <- wrt source file 2025-09-07T08:19:22.0307852Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0 2025-09-07T08:19:22.0309907Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3121 <- wrt source file 2025-09-07T08:19:22.0315916Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0 2025-09-07T08:19:22.0318659Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3430 <- wrt source file 2025-09-07T08:19:22.0325227Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0 2025-09-07T08:19:22.0327770Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3495 <- wrt source file 2025-09-07T08:19:22.0333973Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2025-09-07T08:19:22.0336630Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3565 <- wrt source file 2025-09-07T08:19:22.0341956Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2025-09-07T08:19:22.0344839Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5263 <- wrt source file 2025-09-07T08:19:22.0353829Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0 2025-09-07T08:19:22.0355862Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2025-09-07T08:19:22.0363963Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0 2025-09-07T08:19:22.0366377Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2025-09-07T08:19:22.0370264Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0 2025-09-07T08:19:22.0372694Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2025-09-07T08:19:22.0380150Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0 2025-09-07T08:19:22.0382593Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2025-09-07T08:19:22.0386529Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0 2025-09-07T08:19:22.0388782Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2025-09-07T08:19:22.0422889Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0 2025-09-07T08:19:22.0425858Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2025-09-07T08:19:22.0442612Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0 2025-09-07T08:19:22.0445232Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 230 <- wrt source file 2025-09-07T08:19:22.0447552Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0 2025-09-07T08:19:22.0449671Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 257 <- wrt source file 2025-09-07T08:19:22.0451813Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0 2025-09-07T08:19:22.0453952Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 292 <- wrt source file 2025-09-07T08:19:22.0456205Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0 2025-09-07T08:19:22.0458383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 306 <- wrt source file 2025-09-07T08:19:22.0460709Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0 2025-09-07T08:19:22.0462813Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 323 <- wrt source file 2025-09-07T08:19:22.0464926Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0 2025-09-07T08:19:22.0466996Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 336 <- wrt source file 2025-09-07T08:19:22.0469121Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0 2025-09-07T08:19:22.0471245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 352 <- wrt source file 2025-09-07T08:19:22.0473480Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0 2025-09-07T08:19:22.0475544Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 374 <- wrt source file 2025-09-07T08:19:22.0477662Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0 2025-09-07T08:19:22.0479808Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 460 <- wrt source file 2025-09-07T08:19:22.0482098Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0 2025-09-07T08:19:22.0484411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 492 <- wrt source file 2025-09-07T08:19:22.0486680Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0 2025-09-07T08:19:22.0488930Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 543 <- wrt source file 2025-09-07T08:19:22.0491254Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0 2025-09-07T08:19:22.0493512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 608 <- wrt source file 2025-09-07T08:19:22.0495784Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0 2025-09-07T08:19:22.0498068Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 647 <- wrt source file 2025-09-07T08:19:22.0500357Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0 2025-09-07T08:19:22.0502490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 700 <- wrt source file 2025-09-07T08:19:22.0504639Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0 2025-09-07T08:19:22.0506915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 120 <- wrt source file 2025-09-07T08:19:22.0509438Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2025-09-07T08:19:22.0511863Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 95 <- wrt source file 2025-09-07T08:19:22.0514299Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0 2025-09-07T08:19:22.0516736Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 72 <- wrt source file 2025-09-07T08:19:22.0519315Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0 2025-09-07T08:19:22.0521704Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 120 <- wrt source file 2025-09-07T08:19:22.0524181Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0 2025-09-07T08:19:22.0526566Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 185 <- wrt source file 2025-09-07T08:19:22.0529007Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0 2025-09-07T08:19:22.0531463Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 247 <- wrt source file 2025-09-07T08:19:22.0534021Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2025-09-07T08:19:22.0536417Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 318 <- wrt source file 2025-09-07T08:19:22.0538838Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0 2025-09-07T08:19:22.0541238Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 349 <- wrt source file 2025-09-07T08:19:22.0543718Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2025-09-07T08:19:22.0546193Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 384 <- wrt source file 2025-09-07T08:19:22.0548797Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2025-09-07T08:19:22.0551199Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 420 <- wrt source file 2025-09-07T08:19:22.0553597Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0 2025-09-07T08:19:22.0555947Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 456 <- wrt source file 2025-09-07T08:19:22.0558418Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0 2025-09-07T08:19:22.0560806Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 501 <- wrt source file 2025-09-07T08:19:22.0563215Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0 2025-09-07T08:19:22.0565656Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 552 <- wrt source file 2025-09-07T08:19:22.0568179Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0 2025-09-07T08:19:22.0570560Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 598 <- wrt source file 2025-09-07T08:19:22.0572951Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0 2025-09-07T08:19:22.0575420Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 646 <- wrt source file 2025-09-07T08:19:22.0577891Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0 2025-09-07T08:19:22.0580235Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 705 <- wrt source file 2025-09-07T08:19:22.0582625Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0 2025-09-07T08:19:22.0584958Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 751 <- wrt source file 2025-09-07T08:19:22.0587361Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0 2025-09-07T08:19:22.0589708Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 799 <- wrt source file 2025-09-07T08:19:22.0592170Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0 2025-09-07T08:19:22.0594595Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 848 <- wrt source file 2025-09-07T08:19:22.0597138Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2025-09-07T08:19:22.0599622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 903 <- wrt source file 2025-09-07T08:19:22.0602145Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2025-09-07T08:19:22.0604691Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 945 <- wrt source file 2025-09-07T08:19:22.0607251Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2025-09-07T08:19:22.0609707Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 981 <- wrt source file 2025-09-07T08:19:22.0612190Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0 2025-09-07T08:19:22.0614679Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 1030 <- wrt source file 2025-09-07T08:19:22.0617255Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0 2025-09-07T08:19:22.0619978Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1144 <- wrt source file 2025-09-07T08:19:22.0622815Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2025-09-07T08:19:22.0625343Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1609 <- wrt source file 2025-09-07T08:19:22.0627784Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0 2025-09-07T08:19:22.0630222Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1660 <- wrt source file 2025-09-07T08:19:22.0632736Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0 2025-09-07T08:19:22.0635214Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1686 <- wrt source file 2025-09-07T08:19:22.0638115Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2025-09-07T08:19:22.0640995Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1724 <- wrt source file 2025-09-07T08:19:22.0643845Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0 2025-09-07T08:19:22.0646360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1788 <- wrt source file 2025-09-07T08:19:22.0648811Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0 2025-09-07T08:19:22.0651745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1835 <- wrt source file 2025-09-07T08:19:22.0654736Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2025-09-07T08:19:22.0657460Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1874 <- wrt source file 2025-09-07T08:19:22.0660032Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2025-09-07T08:19:22.0662531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 332 <- wrt source file 2025-09-07T08:19:22.0665085Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2025-09-07T08:19:22.0667599Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 443 <- wrt source file 2025-09-07T08:19:22.0902390Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2025-09-07T08:19:22.0905135Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 554 <- wrt source file 2025-09-07T08:19:22.3357384Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2025-09-07T08:19:22.3525333Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2025-09-07T08:19:22.3547022Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2025-09-07T08:19:22.3549897Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 81 <- wrt source file 2025-09-07T08:19:22.3552493Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0 2025-09-07T08:19:22.3555070Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0, line 260 <- wrt source file 2025-09-07T08:19:22.3557842Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0 2025-09-07T08:19:22.3560480Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0, line 283 <- wrt source file 2025-09-07T08:19:22.3565398Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0 2025-09-07T08:19:22.3568047Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0, line 314 <- wrt source file 2025-09-07T08:19:22.3574780Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0 2025-09-07T08:19:22.3577509Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 343 <- wrt source file 2025-09-07T08:19:22.3579970Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0 2025-09-07T08:19:22.3582137Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 523 <- wrt source file 2025-09-07T08:19:22.3584702Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0 2025-09-07T08:19:22.3587153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 653 <- wrt source file 2025-09-07T08:19:22.3590267Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0 2025-09-07T08:19:22.3593216Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 808 <- wrt source file 2025-09-07T08:19:22.3595749Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0 2025-09-07T08:19:22.3598403Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2025-09-07T08:19:22.3601359Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2025-09-07T08:19:22.3603909Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 81 <- wrt source file 2025-09-07T08:19:22.3606971Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2025-09-07T08:19:22.3609745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2025-09-07T08:19:22.3612192Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0 2025-09-07T08:19:22.3614830Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 108 <- wrt source file 2025-09-07T08:19:22.3617446Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2025-09-07T08:19:22.3619903Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 163 <- wrt source file 2025-09-07T08:19:22.3639511Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2025-09-07T08:19:22.3641950Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 211 <- wrt source file 2025-09-07T08:19:22.3711734Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2025-09-07T08:19:22.3714611Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 257 <- wrt source file 2025-09-07T08:19:22.3717323Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2025-09-07T08:19:22.3719925Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 309 <- wrt source file 2025-09-07T08:19:22.3788748Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2025-09-07T08:19:22.3791870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2025-09-07T08:19:22.3795095Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0 2025-09-07T08:19:22.3797404Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 224 <- wrt source file 2025-09-07T08:19:22.3801321Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0 2025-09-07T08:19:22.3803570Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 395 <- wrt source file 2025-09-07T08:19:22.3817135Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0 2025-09-07T08:19:22.3819963Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2025-09-07T08:19:22.3831354Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2025-09-07T08:19:22.3834038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2025-09-07T08:19:22.4025641Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2025-09-07T08:19:22.4028419Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2025-09-07T08:19:22.6523557Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2025-09-07T08:19:22.6689063Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file 2025-09-07T08:19:22.6692280Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2025-09-07T08:19:22.6694422Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2025-09-07T08:19:22.6700714Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0 2025-09-07T08:19:22.6703428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 83 <- wrt source file 2025-09-07T08:19:22.6711239Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0 2025-09-07T08:19:22.6713760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 191 <- wrt source file 2025-09-07T08:19:22.6733330Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0 2025-09-07T08:19:22.6735645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2025-09-07T08:19:22.6742021Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0 2025-09-07T08:19:22.6744293Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 215 <- wrt source file 2025-09-07T08:19:22.6770021Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2025-09-07T08:19:22.6772747Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 329 <- wrt source file 2025-09-07T08:19:22.6778516Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2025-09-07T08:19:22.6781149Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 418 <- wrt source file 2025-09-07T08:19:22.6795505Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2025-09-07T08:19:22.6798019Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 535 <- wrt source file 2025-09-07T08:19:22.6805779Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2025-09-07T08:19:22.6808531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 617 <- wrt source file 2025-09-07T08:19:22.6814185Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0 2025-09-07T08:19:22.6816466Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 703 <- wrt source file 2025-09-07T08:19:22.6821398Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0 2025-09-07T08:19:22.6823810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 778 <- wrt source file 2025-09-07T08:19:22.6833861Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2025-09-07T08:19:22.6836388Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1, line 826 <- wrt source file 2025-09-07T08:19:22.6840470Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1 2025-09-07T08:19:22.6843472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 974 <- wrt source file 2025-09-07T08:19:22.6849508Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2025-09-07T08:19:22.6852074Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1306 <- wrt source file 2025-09-07T08:19:22.6858417Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2025-09-07T08:19:22.6861413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1, line 1333 <- wrt source file 2025-09-07T08:19:22.6864165Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1 2025-09-07T08:19:22.6866719Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1495 <- wrt source file 2025-09-07T08:19:22.6870109Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2025-09-07T08:19:22.6872664Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1562 <- wrt source file 2025-09-07T08:19:22.6878727Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2025-09-07T08:19:22.6881240Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1643 <- wrt source file 2025-09-07T08:19:22.6887216Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2025-09-07T08:19:22.6889849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1745 <- wrt source file 2025-09-07T08:19:22.6900000Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2025-09-07T08:19:22.6902930Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 551 <- wrt source file 2025-09-07T08:19:22.6905334Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2025-09-07T08:19:22.6907604Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1039 <- wrt source file 2025-09-07T08:19:22.6918077Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0 2025-09-07T08:19:22.6920497Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1290 <- wrt source file 2025-09-07T08:19:22.6925825Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0 2025-09-07T08:19:22.6928821Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2229 <- wrt source file 2025-09-07T08:19:22.6931436Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2025-09-07T08:19:22.6934018Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2670 <- wrt source file 2025-09-07T08:19:22.6936617Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0 2025-09-07T08:19:22.6939252Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2698 <- wrt source file 2025-09-07T08:19:22.6942000Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2025-09-07T08:19:22.6944599Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2725 <- wrt source file 2025-09-07T08:19:22.6947242Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0 2025-09-07T08:19:22.6949855Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2752 <- wrt source file 2025-09-07T08:19:22.6952507Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2025-09-07T08:19:22.6955140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2783 <- wrt source file 2025-09-07T08:19:22.6957833Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0 2025-09-07T08:19:22.6960136Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2807 <- wrt source file 2025-09-07T08:19:22.6962644Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0 2025-09-07T08:19:22.6965988Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2845 <- wrt source file 2025-09-07T08:19:22.6969440Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2025-09-07T08:19:22.6972908Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2025-09-07T08:19:22.6982801Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2025-09-07T08:19:22.6985992Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 163 <- wrt source file 2025-09-07T08:19:22.6994708Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2025-09-07T08:19:22.6997639Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 274 <- wrt source file 2025-09-07T08:19:22.7005455Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2025-09-07T08:19:22.7008064Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 367 <- wrt source file 2025-09-07T08:19:22.7012839Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2025-09-07T08:19:22.7015381Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 70 <- wrt source file 2025-09-07T08:19:22.7019031Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2025-09-07T08:19:22.7021557Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 122 <- wrt source file 2025-09-07T08:19:22.7040929Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2025-09-07T08:19:22.7043430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 187 <- wrt source file 2025-09-07T08:19:23.3155909Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2025-09-07T08:19:23.3446141Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 241 <- wrt source file 2025-09-07T08:19:23.3457282Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2025-09-07T08:19:23.3459646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 294 <- wrt source file 2025-09-07T08:19:23.3464959Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2025-09-07T08:19:23.3467251Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 350 <- wrt source file 2025-09-07T08:19:23.3490322Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2025-09-07T08:19:23.3492521Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 395 <- wrt source file 2025-09-07T08:19:23.3498848Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2025-09-07T08:19:23.3501110Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 439 <- wrt source file 2025-09-07T08:19:23.3506628Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2025-09-07T08:19:23.3508907Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 497 <- wrt source file 2025-09-07T08:19:23.3512192Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2025-09-07T08:19:23.3514407Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 556 <- wrt source file 2025-09-07T08:19:23.3520037Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2025-09-07T08:19:23.3522519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 600 <- wrt source file 2025-09-07T08:19:23.3528233Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2025-09-07T08:19:23.3530576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 658 <- wrt source file 2025-09-07T08:19:23.8688382Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2025-09-07T08:19:23.8976552Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 692 <- wrt source file 2025-09-07T08:19:23.8987692Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2025-09-07T08:19:23.8989882Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 750 <- wrt source file 2025-09-07T08:19:23.8995625Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2025-09-07T08:19:23.8997813Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 812 <- wrt source file 2025-09-07T08:19:23.9020495Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2025-09-07T08:19:23.9022744Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2025-09-07T08:19:23.9029049Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2025-09-07T08:19:23.9031796Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 99 <- wrt source file 2025-09-07T08:19:23.9036151Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2025-09-07T08:19:23.9038926Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 129 <- wrt source file 2025-09-07T08:19:23.9042568Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2025-09-07T08:19:23.9045035Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 207 <- wrt source file 2025-09-07T08:19:23.9094753Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2025-09-07T08:19:23.9097135Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 291 <- wrt source file 2025-09-07T08:19:24.1278287Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2025-09-07T08:19:24.1337314Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 366 <- wrt source file 2025-09-07T08:19:24.1349852Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2025-09-07T08:19:24.1352079Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 550 <- wrt source file 2025-09-07T08:19:24.2112415Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2025-09-07T08:19:24.2114588Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 642 <- wrt source file 2025-09-07T08:19:24.2124329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2025-09-07T08:19:24.2126623Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 738 <- wrt source file 2025-09-07T08:19:24.2164444Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2025-09-07T08:19:24.2166643Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 855 <- wrt source file 2025-09-07T08:19:24.3724696Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2025-09-07T08:19:24.3783485Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 946 <- wrt source file 2025-09-07T08:19:24.3832384Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2025-09-07T08:19:24.3834777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1033 <- wrt source file 2025-09-07T08:19:24.4566425Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2025-09-07T08:19:24.4568620Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1152 <- wrt source file 2025-09-07T08:19:24.4577344Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2025-09-07T08:19:24.4580505Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1204 <- wrt source file 2025-09-07T08:19:24.4628711Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2025-09-07T08:19:24.4631635Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1264 <- wrt source file 2025-09-07T08:19:24.6838655Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2025-09-07T08:19:24.6896694Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1320 <- wrt source file 2025-09-07T08:19:24.6904526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2025-09-07T08:19:24.6907353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1355 <- wrt source file 2025-09-07T08:19:24.6916079Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2025-09-07T08:19:24.6919323Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1399 <- wrt source file 2025-09-07T08:19:24.7000641Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2025-09-07T08:19:24.7003058Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1447 <- wrt source file 2025-09-07T08:19:24.7006675Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2025-09-07T08:19:24.7009661Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1481 <- wrt source file 2025-09-07T08:19:24.7017625Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2025-09-07T08:19:24.7020683Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1521 <- wrt source file 2025-09-07T08:19:24.7038769Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2025-09-07T08:19:24.7041308Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 595 <- wrt source file 2025-09-07T08:19:24.7052525Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0 2025-09-07T08:19:24.7054747Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 953 <- wrt source file 2025-09-07T08:19:24.7400851Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0 2025-09-07T08:19:24.7403241Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1288 <- wrt source file 2025-09-07T08:19:24.7419212Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0 2025-09-07T08:19:24.7421472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1537 <- wrt source file 2025-09-07T08:19:24.7432543Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2025-09-07T08:19:24.7435315Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1659 <- wrt source file 2025-09-07T08:19:24.7444827Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2025-09-07T08:19:24.7447145Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1773 <- wrt source file 2025-09-07T08:19:24.7459023Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2025-09-07T08:19:24.7461351Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 71 <- wrt source file 2025-09-07T08:19:24.7473595Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0 2025-09-07T08:19:24.7476167Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 243 <- wrt source file 2025-09-07T08:19:24.7479593Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2025-09-07T08:19:24.7482385Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 521 <- wrt source file 2025-09-07T08:19:24.7486411Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2025-09-07T08:19:24.7489106Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2025-09-07T08:19:25.5836208Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0 2025-09-07T08:19:25.5853977Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 336 <- wrt source file 2025-09-07T08:19:25.6991622Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2025-09-07T08:19:25.6997292Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 562 <- wrt source file 2025-09-07T08:19:25.9471588Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2025-09-07T08:19:25.9481090Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 686 <- wrt source file 2025-09-07T08:19:25.9768966Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2025-09-07T08:19:25.9805295Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 995 <- wrt source file 2025-09-07T08:19:26.0349619Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2025-09-07T08:19:26.0352211Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2025-09-07T08:19:26.0376896Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2025-09-07T08:19:26.0379209Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 229 <- wrt source file 2025-09-07T08:19:26.0391303Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2025-09-07T08:19:26.0394379Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 279 <- wrt source file 2025-09-07T08:19:26.0399488Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2025-09-07T08:19:26.0402286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 127 <- wrt source file 2025-09-07T08:19:26.0405228Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2025-09-07T08:19:26.0407788Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 642 <- wrt source file 2025-09-07T08:19:26.0410510Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2025-09-07T08:19:26.0413392Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1446 <- wrt source file 2025-09-07T08:19:26.0416414Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2025-09-07T08:19:26.0419712Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1999 <- wrt source file 2025-09-07T08:19:26.0423083Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2025-09-07T08:19:26.0426381Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 2009 <- wrt source file 2025-09-07T08:19:26.0429637Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2025-09-07T08:19:26.0433147Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2044 <- wrt source file 2025-09-07T08:19:26.0436721Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2025-09-07T08:19:26.0439853Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2025-09-07T08:19:26.0442649Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2025-09-07T08:19:26.0444835Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2025-09-07T08:19:26.0446059Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0 2025-09-07T08:19:26.0447303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2025-09-07T08:19:26.0448649Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2025-09-07T08:19:26.0449970Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2025-09-07T08:19:26.0451317Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2025-09-07T08:19:26.0453294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2025-09-07T08:19:26.0455628Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2025-09-07T08:19:26.0457870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 849 <- wrt source file 2025-09-07T08:19:26.0459936Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0 2025-09-07T08:19:26.0461917Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 885 <- wrt source file 2025-09-07T08:19:26.0464093Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2025-09-07T08:19:26.0466440Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 928 <- wrt source file 2025-09-07T08:19:26.0468625Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2025-09-07T08:19:26.0470937Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0, line 968 <- wrt source file 2025-09-07T08:19:26.0473097Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0 2025-09-07T08:19:26.0475429Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1197 <- wrt source file 2025-09-07T08:19:26.0477646Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0 2025-09-07T08:19:26.0479769Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1225 <- wrt source file 2025-09-07T08:19:26.0481727Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0 2025-09-07T08:19:26.0483912Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 439 <- wrt source file 2025-09-07T08:19:26.0486298Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2025-09-07T08:19:26.0488409Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 500 <- wrt source file 2025-09-07T08:19:26.0499369Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2025-09-07T08:19:26.0501745Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 556 <- wrt source file 2025-09-07T08:19:26.0507417Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2025-09-07T08:19:26.0509809Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 584 <- wrt source file 2025-09-07T08:19:26.0524245Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2025-09-07T08:19:26.0526746Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 314 <- wrt source file 2025-09-07T08:19:26.0531550Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2025-09-07T08:19:26.0534326Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 346 <- wrt source file 2025-09-07T08:19:26.0539174Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2025-09-07T08:19:26.0541993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 196 <- wrt source file 2025-09-07T08:19:26.0544577Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0 2025-09-07T08:19:26.0547006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 134 <- wrt source file 2025-09-07T08:19:26.0550100Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2025-09-07T08:19:26.0552632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 156 <- wrt source file 2025-09-07T08:19:26.0556414Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2025-09-07T08:19:26.0559108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 315 <- wrt source file 2025-09-07T08:19:26.0561993Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2025-09-07T08:19:26.0565153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file 2025-09-07T08:19:26.0568674Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2025-09-07T08:19:26.0571575Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 283 <- wrt source file 2025-09-07T08:19:26.0574179Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2025-09-07T08:19:26.0576637Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 391 <- wrt source file 2025-09-07T08:19:26.0579220Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2025-09-07T08:19:26.0581626Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 494 <- wrt source file 2025-09-07T08:19:26.0583963Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2025-09-07T08:19:26.0586337Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 550 <- wrt source file 2025-09-07T08:19:26.0588806Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2025-09-07T08:19:26.0591199Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 611 <- wrt source file 2025-09-07T08:19:26.0593630Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2025-09-07T08:19:26.0595983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 686 <- wrt source file 2025-09-07T08:19:26.0598356Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2025-09-07T08:19:26.0600825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0, line 776 <- wrt source file 2025-09-07T08:19:26.0603434Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0 2025-09-07T08:19:26.0605958Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 974 <- wrt source file 2025-09-07T08:19:26.0608446Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2025-09-07T08:19:26.0610986Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0, line 1065 <- wrt source file 2025-09-07T08:19:26.0613615Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0 2025-09-07T08:19:26.0616199Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1137 <- wrt source file 2025-09-07T08:19:26.0618817Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2025-09-07T08:19:26.0621646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1806 <- wrt source file 2025-09-07T08:19:26.0624630Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2025-09-07T08:19:26.0627556Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1822 <- wrt source file 2025-09-07T08:19:26.0630544Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2025-09-07T08:19:26.0633130Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0, line 337 <- wrt source file 2025-09-07T08:19:26.0635518Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0 2025-09-07T08:19:26.0637872Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file 2025-09-07T08:19:26.0640290Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0 2025-09-07T08:19:26.0643038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 295 <- wrt source file 2025-09-07T08:19:26.0646195Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0 2025-09-07T08:19:26.0648872Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0, line 616 <- wrt source file 2025-09-07T08:19:26.0651253Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0 2025-09-07T08:19:26.0653804Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 339 <- wrt source file 2025-09-07T08:19:26.0656641Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2025-09-07T08:19:26.0659213Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file 2025-09-07T08:19:26.0661665Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0 2025-09-07T08:19:26.0664201Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 615 <- wrt source file 2025-09-07T08:19:26.0666994Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2025-09-07T08:19:26.0669709Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0, line 736 <- wrt source file 2025-09-07T08:19:26.0672483Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0 2025-09-07T08:19:26.0675304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 825 <- wrt source file 2025-09-07T08:19:26.0677999Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2025-09-07T08:19:26.0680833Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4734 <- wrt source file 2025-09-07T08:19:26.0683959Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2025-09-07T08:19:26.0687024Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4748 <- wrt source file 2025-09-07T08:19:26.0690090Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2025-09-07T08:19:26.0693074Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4778 <- wrt source file 2025-09-07T08:19:26.0696083Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2025-09-07T08:19:26.0699031Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 194 <- wrt source file 2025-09-07T08:19:26.0701818Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2025-09-07T08:19:26.0704645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0, line 220 <- wrt source file 2025-09-07T08:19:26.0707611Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0 2025-09-07T08:19:26.0710742Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 331 <- wrt source file 2025-09-07T08:19:26.0714162Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0 2025-09-07T08:19:26.0717520Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2025-09-07T08:19:26.0721005Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2025-09-07T08:19:26.0723830Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 277 <- wrt source file 2025-09-07T08:19:26.0726333Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2025-09-07T08:19:26.0728769Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 320 <- wrt source file 2025-09-07T08:19:26.0731200Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2025-09-07T08:19:26.0733608Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 357 <- wrt source file 2025-09-07T08:19:26.0736082Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2025-09-07T08:19:26.0738443Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 387 <- wrt source file 2025-09-07T08:19:26.0740805Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2025-09-07T08:19:26.0743145Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 422 <- wrt source file 2025-09-07T08:19:26.0745645Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2025-09-07T08:19:26.0748354Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 457 <- wrt source file 2025-09-07T08:19:26.0750992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2025-09-07T08:19:26.0753482Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 494 <- wrt source file 2025-09-07T08:19:26.0756072Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2025-09-07T08:19:26.0758632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 893 <- wrt source file 2025-09-07T08:19:26.0761335Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2025-09-07T08:19:26.0764013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 303 <- wrt source file 2025-09-07T08:19:26.0766732Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0 2025-09-07T08:19:26.0769324Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0, line 419 <- wrt source file 2025-09-07T08:19:26.0772064Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0 2025-09-07T08:19:26.0774630Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0, line 1026 <- wrt source file 2025-09-07T08:19:26.0777120Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0 2025-09-07T08:19:26.0779551Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 1345 <- wrt source file 2025-09-07T08:19:26.0781948Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0 2025-09-07T08:19:26.0795817Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2025-09-07T08:19:26.0798983Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2025-09-07T08:19:26.0802140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 375 <- wrt source file 2025-09-07T08:19:26.0805478Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2025-09-07T08:19:26.0808443Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 410 <- wrt source file 2025-09-07T08:19:26.0811395Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0 2025-09-07T08:19:26.0829642Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 724 <- wrt source file 2025-09-07T08:19:26.1158904Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2025-09-07T08:19:26.1161765Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file 2025-09-07T08:19:26.1164414Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2025-09-07T08:19:26.1166921Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 219 <- wrt source file 2025-09-07T08:19:26.1169428Z * SKIPPED: 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2025-09-07T08:19:26.1219480Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2025-09-07T08:19:26.1222622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 38 <- wrt source file 2025-09-07T08:19:26.1225879Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2025-09-07T08:19:26.1228933Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 88 <- wrt source file 2025-09-07T08:19:26.1232050Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2025-09-07T08:19:26.1235036Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 304 <- wrt source file 2025-09-07T08:19:26.1238040Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2025-09-07T08:19:26.1241116Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file 2025-09-07T08:19:26.1244437Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2025-09-07T08:19:26.1247533Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 53 <- wrt source file 2025-09-07T08:19:26.1250624Z * SKIPPED: 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25 <- wrt source file 2025-09-07T08:19:26.1269677Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2025-09-07T08:19:26.1272877Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 29 <- wrt source file 2025-09-07T08:19:26.1276247Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2025-09-07T08:19:26.1279386Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file 2025-09-07T08:19:26.1282422Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2025-09-07T08:19:26.1285534Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 34 <- wrt source file 2025-09-07T08:19:26.1288797Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2025-09-07T08:19:26.1291947Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 29 <- wrt source file 2025-09-07T08:19:26.1295072Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2025-09-07T08:19:26.1298096Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 73 <- wrt source file 2025-09-07T08:19:26.1301173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2025-09-07T08:19:26.1304172Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 29 <- wrt source file 2025-09-07T08:19:26.1307238Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2025-09-07T08:19:26.1310311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 29 <- wrt source file 2025-09-07T08:19:26.1313473Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2025-09-07T08:19:26.1316494Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 37 <- wrt source file 2025-09-07T08:19:26.1319516Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2025-09-07T08:19:26.1322498Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2025-09-07T08:19:26.1325526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2025-09-07T08:19:26.1328322Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 439 <- wrt source file 2025-09-07T08:19:26.3398884Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2025-09-07T08:19:26.3401164Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 535 <- wrt source file 2025-09-07T08:19:26.3403158Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2025-09-07T08:19:26.3405019Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2025-09-07T08:19:26.3407292Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2025-09-07T08:19:26.3409375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2025-09-07T08:19:26.3411456Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2025-09-07T08:19:26.3413677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2025-09-07T08:19:26.3415447Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2025-09-07T08:19:26.3417214Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2025-09-07T08:19:26.3418975Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2025-09-07T08:19:26.3420445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2025-09-07T08:19:26.3421957Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2025-09-07T08:19:26.3423451Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2025-09-07T08:19:26.3425115Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2025-09-07T08:19:26.3426625Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2025-09-07T08:19:26.3428173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2025-09-07T08:19:26.3429648Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2025-09-07T08:19:26.3431108Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2025-09-07T08:19:26.3432687Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2025-09-07T08:19:26.3434203Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2025-09-07T08:19:26.3436334Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2025-09-07T08:19:26.3438790Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2025-09-07T08:19:26.3441356Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2025-09-07T08:19:26.3444040Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2025-09-07T08:19:26.3446570Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2025-09-07T08:19:26.3449218Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2025-09-07T08:19:26.3451870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2025-09-07T08:19:26.3455006Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2025-09-07T08:19:26.3457631Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2025-09-07T08:19:26.3460611Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2025-09-07T08:19:26.3463273Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2025-09-07T08:19:26.3466141Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2025-09-07T08:19:26.3468681Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2025-09-07T08:19:26.3471258Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2025-09-07T08:19:26.3472689Z ============ 2025-09-07T08:19:26.3473140Z Finished doctests 2025-09-07T08:19:26.3473687Z 338 / 732 passed 2025-09-07T08:19:26.3473993Z  2025-09-07T08:19:26.3474380Z === Found 146 parse-time warnings === 2025-09-07T08:19:26.3475046Z --- Parse Warning: 1 / 146 --- 2025-09-07T08:19:26.3477572Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1493. 2025-09-07T08:19:26.3479660Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3480478Z 2025-09-07T08:19:26.3480791Z dim_order(ambiguity_check=False) -> tuple 2025-09-07T08:19:26.3481275Z 2025-09-07T08:19:26.3481847Z Returns the uniquely determined tuple of int describing the dim order or 2025-09-07T08:19:26.3482681Z physical layout of :attr:`self`. 2025-09-07T08:19:26.3483372Z 2025-09-07T08:19:26.3484061Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-09-07T08:19:26.3486001Z starting from the outermost to the innermost dimension. 2025-09-07T08:19:26.3486705Z 2025-09-07T08:19:26.3487215Z Note that the dim order may not always be uniquely determined. 2025-09-07T08:19:26.3488491Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-09-07T08:19:26.3490205Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-09-07T08:19:26.3491635Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-09-07T08:19:26.3492955Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-09-07T08:19:26.3493966Z Otherwise, it will raise TypeError. 2025-09-07T08:19:26.3494509Z 2025-09-07T08:19:26.3494823Z Args: 2025-09-07T08:19:26.3495629Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-09-07T08:19:26.3496566Z 2025-09-07T08:19:26.3496918Z Examples:: 2025-09-07T08:19:26.3497301Z 2025-09-07T08:19:26.3497843Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-09-07T08:19:26.3498392Z (0, 1, 2, 3) 2025-09-07T08:19:26.3498923Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-09-07T08:19:26.3499578Z (0, 2, 1, 3) 2025-09-07T08:19:26.3500232Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-09-07T08:19:26.3500980Z (0, 2, 3, 1) 2025-09-07T08:19:26.3501419Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-09-07T08:19:26.3501986Z (0, 1, 2, 3) 2025-09-07T08:19:26.3502377Z >>> try: 2025-09-07T08:19:26.3502942Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-09-07T08:19:26.3503670Z ... except RuntimeError as e: 2025-09-07T08:19:26.3504185Z ... print(e) 2025-09-07T08:19:26.3505039Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-09-07T08:19:26.3506138Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-09-07T08:19:26.3506899Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-09-07T08:19:26.3507690Z ... ) # It can be mapped to contiguous format 2025-09-07T08:19:26.3508288Z (0, 1, 2, 3) 2025-09-07T08:19:26.3508695Z >>> try: 2025-09-07T08:19:26.3509251Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-09-07T08:19:26.3509984Z ... except TypeError as e: 2025-09-07T08:19:26.3510466Z ... print(e) 2025-09-07T08:19:26.3511157Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-09-07T08:19:26.3511952Z 2025-09-07T08:19:26.3512307Z .. warning:: 2025-09-07T08:19:26.3512918Z The dim_order tensor API is experimental and subject to change. 2025-09-07T08:19:26.3513602Z 2025-09-07T08:19:26.3514245Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3515072Z 2025-09-07T08:19:26.3515415Z warnings.warn(msg) 2025-09-07T08:19:26.3515843Z 2025-09-07T08:19:26.3516443Z --- Parse Warning: 2 / 146 --- 2025-09-07T08:19:26.3518461Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=397. 2025-09-07T08:19:26.3520657Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3521759Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-09-07T08:19:26.3522595Z 2025-09-07T08:19:26.3523120Z This is helpful when you want to visualize data over some 2025-09-07T08:19:26.3523957Z range of inputs. See below for a plotting example. 2025-09-07T08:19:26.3524661Z 2025-09-07T08:19:26.3525152Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-09-07T08:19:26.3525972Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-09-07T08:19:26.3526849Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-09-07T08:19:26.3527711Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-09-07T08:19:26.3528479Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-09-07T08:19:26.3529245Z to the result shape. 2025-09-07T08:19:26.3529703Z 2025-09-07T08:19:26.3530006Z .. note:: 2025-09-07T08:19:26.3530519Z 0D inputs are treated equivalently to 1D inputs of a 2025-09-07T08:19:26.3531217Z single element. 2025-09-07T08:19:26.3531684Z 2025-09-07T08:19:26.3532007Z .. warning:: 2025-09-07T08:19:26.3532633Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-09-07T08:19:26.3533480Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-09-07T08:19:26.3534135Z 2025-09-07T08:19:26.3534605Z In the future `torch.meshgrid` will transition to 2025-09-07T08:19:26.3535326Z `indexing='xy'` as the default. 2025-09-07T08:19:26.3535908Z 2025-09-07T08:19:26.3536466Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-09-07T08:19:26.3537278Z this issue with the goal of migrating to NumPy's behavior. 2025-09-07T08:19:26.3537916Z 2025-09-07T08:19:26.3538288Z .. seealso:: 2025-09-07T08:19:26.3538739Z 2025-09-07T08:19:26.3539234Z :func:`torch.cartesian_prod` has the same effect but it 2025-09-07T08:19:26.3539956Z collects the data in a tensor of vectors. 2025-09-07T08:19:26.3540546Z 2025-09-07T08:19:26.3540910Z Args: 2025-09-07T08:19:26.3541609Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-09-07T08:19:26.3542620Z treated as tensors of size :math:`(1,)` automatically 2025-09-07T08:19:26.3543260Z 2025-09-07T08:19:26.3543752Z indexing: (str, optional): the indexing mode, either "xy" 2025-09-07T08:19:26.3544575Z or "ij", defaults to "ij". See warning for future changes. 2025-09-07T08:19:26.3545251Z 2025-09-07T08:19:26.3545709Z If "xy" is selected, the first dimension corresponds 2025-09-07T08:19:26.3546483Z to the cardinality of the second input and the second 2025-09-07T08:19:26.3547300Z dimension corresponds to the cardinality of the first 2025-09-07T08:19:26.3547981Z input. 2025-09-07T08:19:26.3548406Z 2025-09-07T08:19:26.3548881Z If "ij" is selected, the dimensions are in the same 2025-09-07T08:19:26.3549624Z order as the cardinality of the inputs. 2025-09-07T08:19:26.3550224Z 2025-09-07T08:19:26.3550572Z Returns: 2025-09-07T08:19:26.3551137Z seq (sequence of Tensors): If the input has :math:`N` 2025-09-07T08:19:26.3551938Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-09-07T08:19:26.3552758Z output will also have :math:`N` tensors, where each tensor 2025-09-07T08:19:26.3553500Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-09-07T08:19:26.3554070Z 2025-09-07T08:19:26.3554409Z Example:: 2025-09-07T08:19:26.3554794Z 2025-09-07T08:19:26.3555168Z >>> x = torch.tensor([1, 2, 3]) 2025-09-07T08:19:26.3555783Z >>> y = torch.tensor([4, 5, 6]) 2025-09-07T08:19:26.3556319Z 2025-09-07T08:19:26.3556869Z Observe the element-wise pairings across the grid, (1, 4), 2025-09-07T08:19:26.3557720Z (1, 5), ..., (3, 6). This is the same thing as the 2025-09-07T08:19:26.3558375Z cartesian product. 2025-09-07T08:19:26.3559037Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-09-07T08:19:26.3559715Z >>> grid_x 2025-09-07T08:19:26.3560159Z tensor([[1, 1, 1], 2025-09-07T08:19:26.3560649Z [2, 2, 2], 2025-09-07T08:19:26.3561113Z [3, 3, 3]]) 2025-09-07T08:19:26.3561622Z >>> grid_y 2025-09-07T08:19:26.3562094Z tensor([[4, 5, 6], 2025-09-07T08:19:26.3562582Z [4, 5, 6], 2025-09-07T08:19:26.3563064Z [4, 5, 6]]) 2025-09-07T08:19:26.3563522Z 2025-09-07T08:19:26.3564003Z This correspondence can be seen when these grids are 2025-09-07T08:19:26.3564772Z stacked properly. 2025-09-07T08:19:26.3565487Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-09-07T08:19:26.3566280Z ... torch.cartesian_prod(x, y)) 2025-09-07T08:19:26.3566871Z True 2025-09-07T08:19:26.3567265Z 2025-09-07T08:19:26.3567799Z `torch.meshgrid` is commonly used to produce a grid for 2025-09-07T08:19:26.3568528Z plotting. 2025-09-07T08:19:26.3569042Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-09-07T08:19:26.3569705Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-09-07T08:19:26.3570390Z >>> import matplotlib.pyplot as plt 2025-09-07T08:19:26.3571040Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-09-07T08:19:26.3571688Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-09-07T08:19:26.3572359Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-09-07T08:19:26.3573051Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-09-07T08:19:26.3573844Z >>> ax = plt.axes(projection='3d') 2025-09-07T08:19:26.3574552Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-09-07T08:19:26.3575199Z >>> plt.show() 2025-09-07T08:19:26.3575647Z 2025-09-07T08:19:26.3576169Z .. image:: ../_static/img/meshgrid.png 2025-09-07T08:19:26.3576769Z :width: 512 2025-09-07T08:19:26.3577223Z 2025-09-07T08:19:26.3577546Z 2025-09-07T08:19:26.3578233Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3579068Z 2025-09-07T08:19:26.3579410Z warnings.warn(msg) 2025-09-07T08:19:26.3579842Z 2025-09-07T08:19:26.3580423Z --- Parse Warning: 3 / 146 --- 2025-09-07T08:19:26.3582440Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=793. 2025-09-07T08:19:26.3584813Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3586211Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-09-07T08:19:26.3587304Z 2025-09-07T08:19:26.3587780Z Returns the unique elements of the input tensor. 2025-09-07T08:19:26.3588404Z 2025-09-07T08:19:26.3589128Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-09-07T08:19:26.3590287Z this function also eliminates non-consecutive duplicate values. 2025-09-07T08:19:26.3591057Z 2025-09-07T08:19:26.3591657Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-09-07T08:19:26.3592833Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-09-07T08:19:26.3594099Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-09-07T08:19:26.3595295Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-09-07T08:19:26.3595890Z 2025-09-07T08:19:26.3596089Z Args: 2025-09-07T08:19:26.3596324Z input (Tensor): the input tensor 2025-09-07T08:19:26.3596768Z sorted (bool): Whether to sort the unique elements in ascending order 2025-09-07T08:19:26.3597229Z before returning as output. 2025-09-07T08:19:26.3597667Z return_inverse (bool): Whether to also return the indices for where 2025-09-07T08:19:26.3598233Z elements in the original input ended up in the returned unique list. 2025-09-07T08:19:26.3598787Z return_counts (bool): Whether to also return the counts for each unique 2025-09-07T08:19:26.3599229Z element. 2025-09-07T08:19:26.3599602Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-09-07T08:19:26.3600139Z unique of the flattened input is returned. Otherwise, each of the 2025-09-07T08:19:26.3600662Z tensors indexed by the given dimension is treated as one of the 2025-09-07T08:19:26.3601203Z elements to apply the unique operation upon. See examples for more 2025-09-07T08:19:26.3601651Z details. Default: ``None`` 2025-09-07T08:19:26.3602001Z 2025-09-07T08:19:26.3602188Z Returns: 2025-09-07T08:19:26.3602618Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-09-07T08:19:26.3603107Z 2025-09-07T08:19:26.3603425Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-09-07T08:19:26.3603896Z - **inverse_indices** (*Tensor*): (optional) if 2025-09-07T08:19:26.3604410Z :attr:`return_inverse` is True, there will be an additional 2025-09-07T08:19:26.3604929Z returned tensor (same shape as input) representing the indices 2025-09-07T08:19:26.3605464Z for where elements in the original input map to in the output; 2025-09-07T08:19:26.3605976Z otherwise, this function will only return a single tensor. 2025-09-07T08:19:26.3606407Z - **counts** (*Tensor*): (optional) if 2025-09-07T08:19:26.3606864Z :attr:`return_counts` is True, there will be an additional 2025-09-07T08:19:26.3607354Z returned tensor (same shape as output or output.size(dim), 2025-09-07T08:19:26.3607854Z if dim was specified) representing the number of occurrences 2025-09-07T08:19:26.3608283Z for each unique value or tensor. 2025-09-07T08:19:26.3608584Z 2025-09-07T08:19:26.3608790Z Example:: 2025-09-07T08:19:26.3609016Z 2025-09-07T08:19:26.3609332Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-09-07T08:19:26.3609757Z >>> output 2025-09-07T08:19:26.3610013Z tensor([1, 2, 3]) 2025-09-07T08:19:26.3610277Z 2025-09-07T08:19:26.3610514Z >>> output, inverse_indices = torch.unique( 2025-09-07T08:19:26.3610995Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T08:19:26.3611442Z >>> output 2025-09-07T08:19:26.3611693Z tensor([1, 2, 3]) 2025-09-07T08:19:26.3611973Z >>> inverse_indices 2025-09-07T08:19:26.3612241Z tensor([0, 2, 1, 2]) 2025-09-07T08:19:26.3612516Z 2025-09-07T08:19:26.3612759Z >>> output, inverse_indices = torch.unique( 2025-09-07T08:19:26.3613246Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T08:19:26.3613678Z >>> output 2025-09-07T08:19:26.3613927Z tensor([1, 2, 3]) 2025-09-07T08:19:26.3614205Z >>> inverse_indices 2025-09-07T08:19:26.3614481Z tensor([[0, 2], 2025-09-07T08:19:26.3614732Z [1, 2]]) 2025-09-07T08:19:26.3614984Z 2025-09-07T08:19:26.3615226Z >>> a = torch.tensor([ 2025-09-07T08:19:26.3615511Z ... [ 2025-09-07T08:19:26.3615795Z ... [1, 1, 0, 0], 2025-09-07T08:19:26.3616091Z ... [1, 1, 0, 0], 2025-09-07T08:19:26.3616385Z ... [0, 0, 1, 1], 2025-09-07T08:19:26.3616670Z ... ], 2025-09-07T08:19:26.3616897Z ... [ 2025-09-07T08:19:26.3617143Z ... [0, 0, 1, 1], 2025-09-07T08:19:26.3617432Z ... [0, 0, 1, 1], 2025-09-07T08:19:26.3617717Z ... [1, 1, 1, 1], 2025-09-07T08:19:26.3617985Z ... ], 2025-09-07T08:19:26.3618224Z ... [ 2025-09-07T08:19:26.3618464Z ... [1, 1, 0, 0], 2025-09-07T08:19:26.3618748Z ... [1, 1, 0, 0], 2025-09-07T08:19:26.3619022Z ... [0, 0, 1, 1], 2025-09-07T08:19:26.3619303Z ... ], 2025-09-07T08:19:26.3619545Z ... ]) 2025-09-07T08:19:26.3619756Z 2025-09-07T08:19:26.3620092Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-09-07T08:19:26.3620647Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-09-07T08:19:26.3621119Z >>> # each other, so one of them will be removed. 2025-09-07T08:19:26.3621510Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-09-07T08:19:26.3621816Z tensor(True) 2025-09-07T08:19:26.3622115Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-09-07T08:19:26.3622455Z >>> a_unique_dim0 2025-09-07T08:19:26.3622738Z tensor([[[0, 0, 1, 1], 2025-09-07T08:19:26.3623016Z [0, 0, 1, 1], 2025-09-07T08:19:26.3623307Z [1, 1, 1, 1]], 2025-09-07T08:19:26.3623596Z [[1, 1, 0, 0], 2025-09-07T08:19:26.3623877Z [1, 1, 0, 0], 2025-09-07T08:19:26.3624146Z [0, 0, 1, 1]]]) 2025-09-07T08:19:26.3624427Z 2025-09-07T08:19:26.3624756Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-09-07T08:19:26.3625196Z >>> # `a_unique_dim0`: 2025-09-07T08:19:26.3625532Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-09-07T08:19:26.3625871Z tensor(True) 2025-09-07T08:19:26.3626157Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-09-07T08:19:26.3626486Z tensor(True) 2025-09-07T08:19:26.3626717Z 2025-09-07T08:19:26.3627034Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-09-07T08:19:26.3627545Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-09-07T08:19:26.3627957Z >>> # them will be removed. 2025-09-07T08:19:26.3628261Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-09-07T08:19:26.3628570Z tensor(True) 2025-09-07T08:19:26.3628841Z >>> torch.unique(a, dim=1) 2025-09-07T08:19:26.3629150Z tensor([[[0, 0, 1, 1], 2025-09-07T08:19:26.3629427Z [1, 1, 0, 0]], 2025-09-07T08:19:26.3629711Z [[1, 1, 1, 1], 2025-09-07T08:19:26.3629998Z [0, 0, 1, 1]], 2025-09-07T08:19:26.3630287Z [[0, 0, 1, 1], 2025-09-07T08:19:26.3630557Z [1, 1, 0, 0]]]) 2025-09-07T08:19:26.3630841Z 2025-09-07T08:19:26.3631164Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-09-07T08:19:26.3631656Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-09-07T08:19:26.3632106Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-09-07T08:19:26.3632506Z >>> # sub-tensors will be removed. 2025-09-07T08:19:26.3632839Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-09-07T08:19:26.3633211Z tensor(True) 2025-09-07T08:19:26.3633740Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-09-07T08:19:26.3634234Z tensor(True) 2025-09-07T08:19:26.3634540Z >>> torch.unique(a, dim=2) 2025-09-07T08:19:26.3634850Z tensor([[[0, 1], 2025-09-07T08:19:26.3635122Z [0, 1], 2025-09-07T08:19:26.3635376Z [1, 0]], 2025-09-07T08:19:26.3635644Z [[1, 0], 2025-09-07T08:19:26.3635913Z [1, 0], 2025-09-07T08:19:26.3636177Z [1, 1]], 2025-09-07T08:19:26.3636425Z [[0, 1], 2025-09-07T08:19:26.3636684Z [0, 1], 2025-09-07T08:19:26.3636944Z [1, 0]]]) 2025-09-07T08:19:26.3637204Z 2025-09-07T08:19:26.3637571Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3638029Z 2025-09-07T08:19:26.3638240Z warnings.warn(msg) 2025-09-07T08:19:26.3638494Z 2025-09-07T08:19:26.3638825Z --- Parse Warning: 4 / 146 --- 2025-09-07T08:19:26.3639859Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=565. 2025-09-07T08:19:26.3641039Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3641542Z 2025-09-07T08:19:26.3641806Z Load a model from a github repo or a local directory. 2025-09-07T08:19:26.3642160Z 2025-09-07T08:19:26.3642512Z Note: Loading a model is the typical use case, but this can also be used to 2025-09-07T08:19:26.3643085Z for loading other objects such as tokenizers, loss functions, etc. 2025-09-07T08:19:26.3643486Z 2025-09-07T08:19:26.3643766Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-09-07T08:19:26.3644302Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-09-07T08:19:26.3644692Z ref (a tag or a branch). 2025-09-07T08:19:26.3644943Z 2025-09-07T08:19:26.3645229Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-09-07T08:19:26.3645625Z path to a local directory. 2025-09-07T08:19:26.3645896Z 2025-09-07T08:19:26.3646083Z Args: 2025-09-07T08:19:26.3646373Z repo_or_dir (str): If ``source`` is 'github', 2025-09-07T08:19:26.3646915Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-09-07T08:19:26.3647630Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-09-07T08:19:26.3648320Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-09-07T08:19:26.3648902Z If ``source`` is 'local' then it should be a path to a local directory. 2025-09-07T08:19:26.3649426Z model (str): the name of a callable (entrypoint) defined in the 2025-09-07T08:19:26.3649855Z repo/dir's ``hubconf.py``. 2025-09-07T08:19:26.3650273Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T08:19:26.3650762Z source (str, optional): 'github' or 'local'. Specifies how 2025-09-07T08:19:26.3651233Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-09-07T08:19:26.3651741Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-09-07T08:19:26.3652308Z This parameter was introduced in v1.12 and helps ensuring that users 2025-09-07T08:19:26.3652792Z only run code from repos that they trust. 2025-09-07T08:19:26.3653108Z 2025-09-07T08:19:26.3653415Z - If ``False``, a prompt will ask the user whether the repo should 2025-09-07T08:19:26.3653831Z be trusted. 2025-09-07T08:19:26.3654195Z - If ``True``, the repo will be added to the trusted list and loaded 2025-09-07T08:19:26.3654637Z without requiring explicit confirmation. 2025-09-07T08:19:26.3655091Z - If ``"check"``, the repo will be checked against the list of 2025-09-07T08:19:26.3655596Z trusted repos in the cache. If it is not present in that list, the 2025-09-07T08:19:26.3656154Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-09-07T08:19:26.3656664Z - If ``None``: this will raise a warning, inviting the user to set 2025-09-07T08:19:26.3657155Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-09-07T08:19:26.3657681Z is only present for backward compatibility and will be removed in 2025-09-07T08:19:26.3658101Z v2.0. 2025-09-07T08:19:26.3658331Z 2025-09-07T08:19:26.3658646Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-09-07T08:19:26.3659190Z force_reload (bool, optional): whether to force a fresh download of 2025-09-07T08:19:26.3659720Z the github repo unconditionally. Does not have any effect if 2025-09-07T08:19:26.3660169Z ``source = 'local'``. Default is ``False``. 2025-09-07T08:19:26.3660607Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-09-07T08:19:26.3661144Z local caches. Note that the message about first download cannot be 2025-09-07T08:19:26.3661677Z muted. Does not have any effect if ``source = 'local'``. 2025-09-07T08:19:26.3662074Z Default is ``True``. 2025-09-07T08:19:26.3662566Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-09-07T08:19:26.3663255Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-09-07T08:19:26.3663931Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-09-07T08:19:26.3664507Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-09-07T08:19:26.3665026Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T08:19:26.3665431Z 2025-09-07T08:19:26.3665631Z Returns: 2025-09-07T08:19:26.3665954Z The output of the ``model`` callable when called with the given 2025-09-07T08:19:26.3666361Z ``*args`` and ``**kwargs``. 2025-09-07T08:19:26.3666659Z 2025-09-07T08:19:26.3666862Z Example: 2025-09-07T08:19:26.3667130Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T08:19:26.3667480Z >>> # from a github repo 2025-09-07T08:19:26.3667762Z >>> repo = "pytorch/vision" 2025-09-07T08:19:26.3668077Z >>> model = torch.hub.load( 2025-09-07T08:19:26.3668468Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-09-07T08:19:26.3668854Z ... ) 2025-09-07T08:19:26.3669080Z >>> # from a local directory 2025-09-07T08:19:26.3669421Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T08:19:26.3669766Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.3670193Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-09-07T08:19:26.3670630Z 2025-09-07T08:19:26.3671001Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3671458Z 2025-09-07T08:19:26.3671669Z warnings.warn(msg) 2025-09-07T08:19:26.3671909Z 2025-09-07T08:19:26.3672235Z --- Parse Warning: 5 / 146 --- 2025-09-07T08:19:26.3673498Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=657. 2025-09-07T08:19:26.3674726Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3675202Z 2025-09-07T08:19:26.3675483Z Load a model from a local directory with a ``hubconf.py``. 2025-09-07T08:19:26.3675862Z 2025-09-07T08:19:26.3676060Z Args: 2025-09-07T08:19:26.3676370Z hubconf_dir (str): path to a local directory that contains a 2025-09-07T08:19:26.3676833Z ``hubconf.py``. 2025-09-07T08:19:26.3677247Z model (str): name of an entrypoint defined in the directory's 2025-09-07T08:19:26.3677652Z ``hubconf.py``. 2025-09-07T08:19:26.3678036Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T08:19:26.3678560Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T08:19:26.3678980Z 2025-09-07T08:19:26.3679178Z Returns: 2025-09-07T08:19:26.3679479Z a single model with corresponding pretrained weights. 2025-09-07T08:19:26.3679832Z 2025-09-07T08:19:26.3680029Z Example: 2025-09-07T08:19:26.3680281Z >>> # xdoctest: +SKIP("stub local path") 2025-09-07T08:19:26.3680649Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T08:19:26.3680982Z >>> model = _load_local( 2025-09-07T08:19:26.3681258Z ... path, 2025-09-07T08:19:26.3681503Z ... "resnet50", 2025-09-07T08:19:26.3681822Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-09-07T08:19:26.3682155Z ... ) 2025-09-07T08:19:26.3682363Z 2025-09-07T08:19:26.3682732Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3683186Z 2025-09-07T08:19:26.3683417Z warnings.warn(msg) 2025-09-07T08:19:26.3683670Z 2025-09-07T08:19:26.3683989Z --- Parse Warning: 6 / 146 --- 2025-09-07T08:19:26.3685189Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=696. 2025-09-07T08:19:26.3686413Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3686938Z Download object at the given URL to a local path. 2025-09-07T08:19:26.3687286Z 2025-09-07T08:19:26.3687490Z Args: 2025-09-07T08:19:26.3687758Z url (str): URL of the object to download 2025-09-07T08:19:26.3688222Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-09-07T08:19:26.3688964Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-09-07T08:19:26.3689522Z Default: None 2025-09-07T08:19:26.3689955Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-09-07T08:19:26.3690414Z Default: True 2025-09-07T08:19:26.3690678Z 2025-09-07T08:19:26.3690878Z Example: 2025-09-07T08:19:26.3691161Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T08:19:26.3691520Z >>> # xdoctest: +REQUIRES(POSIX) 2025-09-07T08:19:26.3691871Z >>> torch.hub.download_url_to_file( 2025-09-07T08:19:26.3692343Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-09-07T08:19:26.3692800Z ... "/tmp/temporary_file", 2025-09-07T08:19:26.3693099Z ... ) 2025-09-07T08:19:26.3693328Z 2025-09-07T08:19:26.3693529Z 2025-09-07T08:19:26.3693907Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3694355Z 2025-09-07T08:19:26.3694575Z warnings.warn(msg) 2025-09-07T08:19:26.3694833Z 2025-09-07T08:19:26.3695149Z --- Parse Warning: 7 / 146 --- 2025-09-07T08:19:26.3696246Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=821. 2025-09-07T08:19:26.3697506Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3698054Z Loads the Torch serialized object at the given URL. 2025-09-07T08:19:26.3698407Z 2025-09-07T08:19:26.3698711Z If downloaded file is a zip file, it will be automatically 2025-09-07T08:19:26.3699138Z decompressed. 2025-09-07T08:19:26.3699381Z 2025-09-07T08:19:26.3699743Z If the object is already present in `model_dir`, it's deserialized and 2025-09-07T08:19:26.3700171Z returned. 2025-09-07T08:19:26.3700522Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-09-07T08:19:26.3701054Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-09-07T08:19:26.3701451Z 2025-09-07T08:19:26.3701651Z Args: 2025-09-07T08:19:26.3701899Z url (str): URL of the object to download 2025-09-07T08:19:26.3702338Z model_dir (str, optional): directory in which to save the object 2025-09-07T08:19:26.3703013Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-09-07T08:19:26.3703740Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-09-07T08:19:26.3704217Z Default: True 2025-09-07T08:19:26.3704714Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-09-07T08:19:26.3705385Z ``filename-.ext`` where ```` is the first eight or more 2025-09-07T08:19:26.3705983Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-09-07T08:19:26.3706531Z ensure unique names and to verify the contents of the file. 2025-09-07T08:19:26.3706932Z Default: False 2025-09-07T08:19:26.3707456Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-09-07T08:19:26.3708238Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-09-07T08:19:26.3708946Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-09-07T08:19:26.3709412Z 2025-09-07T08:19:26.3709606Z Example: 2025-09-07T08:19:26.3709889Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T08:19:26.3710296Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-09-07T08:19:26.3710808Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-09-07T08:19:26.3711221Z ... ) 2025-09-07T08:19:26.3711449Z 2025-09-07T08:19:26.3711641Z 2025-09-07T08:19:26.3712015Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3712462Z 2025-09-07T08:19:26.3712671Z warnings.warn(msg) 2025-09-07T08:19:26.3712924Z 2025-09-07T08:19:26.3713233Z --- Parse Warning: 8 / 146 --- 2025-09-07T08:19:26.3714323Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=375. 2025-09-07T08:19:26.3715547Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.3716167Z Registers the function implementation as the fallback for the given key. 2025-09-07T08:19:26.3716618Z 2025-09-07T08:19:26.3716953Z This function only works for a library with global namespace ("_"). 2025-09-07T08:19:26.3717364Z 2025-09-07T08:19:26.3717564Z Args: 2025-09-07T08:19:26.3717988Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-09-07T08:19:26.3718507Z to register a fallthrough. 2025-09-07T08:19:26.3719049Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-09-07T08:19:26.3719660Z the dispatch key that the library was created with. 2025-09-07T08:19:26.3720323Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-09-07T08:19:26.3721169Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-09-07T08:19:26.3721725Z 2025-09-07T08:19:26.3721927Z Example:: 2025-09-07T08:19:26.3722171Z 2025-09-07T08:19:26.3722398Z >>> my_lib = Library("_", "IMPL") 2025-09-07T08:19:26.3722763Z >>> def fallback_kernel(op, *args, **kwargs): 2025-09-07T08:19:26.3723137Z >>> # Handle all autocast ops generically 2025-09-07T08:19:26.3723477Z >>> # ... 2025-09-07T08:19:26.3723803Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T08:19:26.3724226Z 2025-09-07T08:19:26.3724988Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-09-07T08:19:26.3725816Z 2025-09-07T08:19:26.3726066Z my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T08:19:26.3726398Z ^ 2025-09-07T08:19:26.3726603Z warnings.warn(msg) 2025-09-07T08:19:26.3726853Z 2025-09-07T08:19:26.3727163Z --- Parse Warning: 9 / 146 --- 2025-09-07T08:19:26.3728244Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=948. 2025-09-07T08:19:26.3729512Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.3730107Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-09-07T08:19:26.3730539Z 2025-09-07T08:19:26.3730838Z Also sometimes known as a "meta kernel", "abstract impl". 2025-09-07T08:19:26.3731216Z 2025-09-07T08:19:26.3731565Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-09-07T08:19:26.3732164Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-09-07T08:19:26.3732754Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-09-07T08:19:26.3733259Z what the properties of the output Tensors are. 2025-09-07T08:19:26.3733612Z 2025-09-07T08:19:26.3733962Z The FakeTensor implementation has the same signature as the operator. 2025-09-07T08:19:26.3734524Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-09-07T08:19:26.3735075Z implementation, assume that all Tensor inputs to the operator are 2025-09-07T08:19:26.3735618Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-09-07T08:19:26.3736152Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-09-07T08:19:26.3736717Z The FakeTensor implementation must consist of only PyTorch operations 2025-09-07T08:19:26.3737267Z (and may not directly access the storage or data of any input or 2025-09-07T08:19:26.3737692Z intermediate Tensors). 2025-09-07T08:19:26.3737957Z 2025-09-07T08:19:26.3738234Z This API may be used as a decorator (see examples). 2025-09-07T08:19:26.3738594Z 2025-09-07T08:19:26.3738859Z For a detailed guide on custom ops, please see 2025-09-07T08:19:26.3739346Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-09-07T08:19:26.3739787Z 2025-09-07T08:19:26.3739994Z Args: 2025-09-07T08:19:26.3740357Z op_name: Operator name (along with the overload) or OpOverload object. 2025-09-07T08:19:26.3740818Z func: Fake tensor implementation. 2025-09-07T08:19:26.3741257Z lib (Optional[Library]): Library to register the fake tensor to. 2025-09-07T08:19:26.3741766Z allow_override: Flag controlling if we want to override an 2025-09-07T08:19:26.3742255Z existing registered fake impl. This is by default off, 2025-09-07T08:19:26.3742747Z and will error you're trying to register a fake impl to 2025-09-07T08:19:26.3743249Z an operator that already has a fake impl. This also only 2025-09-07T08:19:26.3743718Z applies if the custom operator was not created via 2025-09-07T08:19:26.3744199Z torch.library.custom_op, as overriding and existing fake 2025-09-07T08:19:26.3744637Z impl is already allowed. 2025-09-07T08:19:26.3744942Z 2025-09-07T08:19:26.3745150Z Examples: 2025-09-07T08:19:26.3745391Z >>> import torch 2025-09-07T08:19:26.3745673Z >>> import numpy as np 2025-09-07T08:19:26.3745969Z >>> from torch import Tensor 2025-09-07T08:19:26.3746268Z >>> 2025-09-07T08:19:26.3746595Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T08:19:26.3747124Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-09-07T08:19:26.3747666Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T08:19:26.3748194Z >>> raise NotImplementedError("Implementation goes here") 2025-09-07T08:19:26.3748589Z >>> 2025-09-07T08:19:26.3748928Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-09-07T08:19:26.3749320Z >>> def _(x, weight, bias): 2025-09-07T08:19:26.3749625Z >>> assert x.dim() == 2 2025-09-07T08:19:26.3749953Z >>> assert weight.dim() == 2 2025-09-07T08:19:26.3750285Z >>> assert bias.dim() == 1 2025-09-07T08:19:26.3750633Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T08:19:26.3750993Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T08:19:26.3751369Z >>> assert x.device == weight.device 2025-09-07T08:19:26.3751691Z >>> 2025-09-07T08:19:26.3751947Z >>> return (x @ weight.t()) + bias 2025-09-07T08:19:26.3752254Z >>> 2025-09-07T08:19:26.3752561Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-09-07T08:19:26.3752961Z >>> x = torch.randn(2, 3) 2025-09-07T08:19:26.3753317Z >>> w = torch.randn(3, 3) 2025-09-07T08:19:26.3753627Z >>> b = torch.randn(3) 2025-09-07T08:19:26.3753974Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-09-07T08:19:26.3754315Z >>> 2025-09-07T08:19:26.3754557Z >>> assert y.shape == (2, 3) 2025-09-07T08:19:26.3754843Z >>> 2025-09-07T08:19:26.3755158Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T08:19:26.3755679Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-09-07T08:19:26.3756153Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-09-07T08:19:26.3756500Z >>> x_np = x.numpy(force=True) 2025-09-07T08:19:26.3756864Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T08:19:26.3757252Z >>> return torch.tensor(res, device=x.device) 2025-09-07T08:19:26.3757593Z >>> 2025-09-07T08:19:26.3757904Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-09-07T08:19:26.3758273Z >>> def _(x): 2025-09-07T08:19:26.3758590Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T08:19:26.3759013Z >>> # Since we cannot peek at the data in an fake impl, 2025-09-07T08:19:26.3759445Z >>> # we use the ctx object to construct a new symint that 2025-09-07T08:19:26.3759841Z >>> # represents the data-dependent size. 2025-09-07T08:19:26.3760205Z >>> ctx = torch.library.get_ctx() 2025-09-07T08:19:26.3760556Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T08:19:26.3760893Z >>> shape = [nnz, x.dim()] 2025-09-07T08:19:26.3761249Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T08:19:26.3761642Z >>> return result 2025-09-07T08:19:26.3761945Z >>> 2025-09-07T08:19:26.3762265Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T08:19:26.3762640Z >>> 2025-09-07T08:19:26.3762896Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-09-07T08:19:26.3763376Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-09-07T08:19:26.3763855Z >>> trace.print_readable() 2025-09-07T08:19:26.3764222Z >>> 2025-09-07T08:19:26.3764587Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-09-07T08:19:26.3765020Z 2025-09-07T08:19:26.3765221Z 2025-09-07T08:19:26.3765866Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-09-07T08:19:26.3766600Z 2025-09-07T08:19:26.3766807Z _._ = None 2025-09-07T08:19:26.3767030Z ^ 2025-09-07T08:19:26.3767236Z warnings.warn(msg) 2025-09-07T08:19:26.3767491Z 2025-09-07T08:19:26.3767816Z --- Parse Warning: 10 / 146 --- 2025-09-07T08:19:26.3768936Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1083. 2025-09-07T08:19:26.3770222Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3770743Z Register a backward formula for this custom op. 2025-09-07T08:19:26.3771088Z 2025-09-07T08:19:26.3771419Z In order for an operator to work with autograd, you need to register 2025-09-07T08:19:26.3771859Z a backward formula: 2025-09-07T08:19:26.3772237Z 1. You must tell us how to compute gradients during the backward pass 2025-09-07T08:19:26.3772692Z by providing us a "backward" function. 2025-09-07T08:19:26.3773146Z 2. If you need any values from the forward to compute gradients, you can 2025-09-07T08:19:26.3773844Z use `setup_context` to save values for backward. 2025-09-07T08:19:26.3774195Z 2025-09-07T08:19:26.3774604Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-09-07T08:19:26.3775153Z - ``grads`` is one or more gradients. The number of gradients matches 2025-09-07T08:19:26.3775601Z the number of outputs of the operator. 2025-09-07T08:19:26.3776059Z The ``ctx`` object is `the same ctx object `_ used by 2025-09-07T08:19:26.3776635Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-09-07T08:19:26.3813160Z same as :meth:`torch.autograd.Function.backward`. 2025-09-07T08:19:26.3813774Z 2025-09-07T08:19:26.3814331Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-09-07T08:19:26.3815328Z Please save quantities needed for backward onto the ``ctx`` object via 2025-09-07T08:19:26.3816314Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-09-07T08:19:26.3817289Z or assigning them as attributes of ``ctx``. If your custom op has 2025-09-07T08:19:26.3818225Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-09-07T08:19:26.3819146Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-09-07T08:19:26.3819838Z 2025-09-07T08:19:26.3820386Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-09-07T08:19:26.3821374Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-09-07T08:19:26.3822201Z not depend on or mutate global state. If you need a non-traceable backward, 2025-09-07T08:19:26.3822788Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-09-07T08:19:26.3823203Z 2025-09-07T08:19:26.3823670Z If you need different autograd behavior on different devices, then we 2025-09-07T08:19:26.3824344Z recommend creating two different custom operators, one for each device 2025-09-07T08:19:26.3825025Z that needs different behavior, and switching between them at runtime. 2025-09-07T08:19:26.3825445Z 2025-09-07T08:19:26.3825658Z Examples: 2025-09-07T08:19:26.3825905Z >>> import torch 2025-09-07T08:19:26.3826192Z >>> import numpy as np 2025-09-07T08:19:26.3826494Z >>> from torch import Tensor 2025-09-07T08:19:26.3826796Z >>> 2025-09-07T08:19:26.3827141Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-09-07T08:19:26.3827592Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-09-07T08:19:26.3827926Z >>> x_np = x.cpu().numpy() 2025-09-07T08:19:26.3828246Z >>> y_np = np.sin(x_np) 2025-09-07T08:19:26.3828612Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T08:19:26.3828972Z >>> 2025-09-07T08:19:26.3829263Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-09-07T08:19:26.3829616Z >>> x, = inputs 2025-09-07T08:19:26.3829916Z >>> ctx.save_for_backward(x) 2025-09-07T08:19:26.3830271Z >>> 2025-09-07T08:19:26.3830520Z >>> def backward(ctx, grad): 2025-09-07T08:19:26.3830832Z >>> x, = ctx.saved_tensors 2025-09-07T08:19:26.3831147Z >>> return grad * x.cos() 2025-09-07T08:19:26.3831450Z >>> 2025-09-07T08:19:26.3831713Z >>> torch.library.register_autograd( 2025-09-07T08:19:26.3832126Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-09-07T08:19:26.3832514Z ... ) 2025-09-07T08:19:26.3832743Z >>> 2025-09-07T08:19:26.3833010Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T08:19:26.3833344Z >>> y = numpy_sin(x) 2025-09-07T08:19:26.3833711Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T08:19:26.3834135Z >>> assert torch.allclose(grad_x, x.cos()) 2025-09-07T08:19:26.3834461Z >>> 2025-09-07T08:19:26.3834740Z >>> # Example with a keyword-only arg 2025-09-07T08:19:26.3835185Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T08:19:26.3835663Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-09-07T08:19:26.3836042Z >>> x_np = x.cpu().numpy() 2025-09-07T08:19:26.3836347Z >>> y_np = x_np * val 2025-09-07T08:19:26.3836712Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T08:19:26.3837079Z >>> 2025-09-07T08:19:26.3837448Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-09-07T08:19:26.3837931Z >>> ctx.val = keyword_only_inputs["val"] 2025-09-07T08:19:26.3838243Z >>> 2025-09-07T08:19:26.3838488Z >>> def backward(ctx, grad): 2025-09-07T08:19:26.3838813Z >>> return grad * ctx.val 2025-09-07T08:19:26.3839110Z >>> 2025-09-07T08:19:26.3839361Z >>> torch.library.register_autograd( 2025-09-07T08:19:26.3839787Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-09-07T08:19:26.3840173Z ... ) 2025-09-07T08:19:26.3840400Z >>> 2025-09-07T08:19:26.3840651Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T08:19:26.3841002Z >>> y = numpy_mul(x, val=3.14) 2025-09-07T08:19:26.3841392Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T08:19:26.3841862Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-09-07T08:19:26.3842229Z 2025-09-07T08:19:26.3842430Z 2025-09-07T08:19:26.3842802Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3843287Z 2025-09-07T08:19:26.3843489Z warnings.warn(msg) 2025-09-07T08:19:26.3843770Z 2025-09-07T08:19:26.3844225Z --- Parse Warning: 11 / 146 --- 2025-09-07T08:19:26.3845310Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1482. 2025-09-07T08:19:26.3846485Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.3847067Z Returns the computed kernel for a given operator and dispatch key. 2025-09-07T08:19:26.3847481Z 2025-09-07T08:19:26.3847818Z This function retrieves the kernel that would be executed for a given 2025-09-07T08:19:26.3848389Z operator and dispatch key combination. The returned SafeKernelFunction 2025-09-07T08:19:26.3848929Z can be used to call the kernel in a boxed fashion. The intended use 2025-09-07T08:19:26.3849463Z case for this function is to retrieve the original kernel for a given 2025-09-07T08:19:26.3850026Z dispatch key and then register another kernel to the same dispatch key 2025-09-07T08:19:26.3850541Z that calls into the original kernel for certain cases. 2025-09-07T08:19:26.3850929Z 2025-09-07T08:19:26.3851130Z Args: 2025-09-07T08:19:26.3851457Z op: Operator name (along with the overload) or OpOverload object 2025-09-07T08:19:26.3852008Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-09-07T08:19:26.3852614Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-09-07T08:19:26.3853170Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-09-07T08:19:26.3853561Z 2025-09-07T08:19:26.3853761Z Returns: 2025-09-07T08:19:26.3854134Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-09-07T08:19:26.3854585Z call the kernel. 2025-09-07T08:19:26.3854859Z 2025-09-07T08:19:26.3855048Z Raises: 2025-09-07T08:19:26.3855329Z RuntimeError: If the operator does not exist. 2025-09-07T08:19:26.3855673Z 2025-09-07T08:19:26.3855850Z Example: 2025-09-07T08:19:26.3856105Z >>> # Get the CPU kernel for torch.add 2025-09-07T08:19:26.3856521Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-09-07T08:19:26.3856895Z >>> 2025-09-07T08:19:26.3857129Z >>> # You can also use DispatchKey enum 2025-09-07T08:19:26.3857616Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-09-07T08:19:26.3858072Z >>> 2025-09-07T08:19:26.3858295Z >>> # Or use an OpOverload directly 2025-09-07T08:19:26.3858735Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-09-07T08:19:26.3859151Z >>> 2025-09-07T08:19:26.3859489Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-09-07T08:19:26.3859945Z >>> # Get the original kernel for torch.sin 2025-09-07T08:19:26.3860389Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-09-07T08:19:26.3860798Z >>> 2025-09-07T08:19:26.3861144Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-09-07T08:19:26.3861624Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-09-07T08:19:26.3861953Z >>> if (x < 0).any(): 2025-09-07T08:19:26.3862330Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-09-07T08:19:26.3862700Z >>> else: 2025-09-07T08:19:26.3862971Z >>> return torch.zeros_like(x) 2025-09-07T08:19:26.3863258Z >>> 2025-09-07T08:19:26.3863516Z >>> lib = torch.library.Library("aten", "IMPL") 2025-09-07T08:19:26.3864035Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-09-07T08:19:26.3864621Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T08:19:26.3865097Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-09-07T08:19:26.3865475Z >>> 2025-09-07T08:19:26.3865704Z >>> # Test the conditional behavior 2025-09-07T08:19:26.3866038Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-09-07T08:19:26.3866375Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-09-07T08:19:26.3866699Z >>> torch.sin(x_positive) 2025-09-07T08:19:26.3866985Z tensor([0., 0.]) 2025-09-07T08:19:26.3867242Z >>> torch.sin(x_mixed) 2025-09-07T08:19:26.3867529Z tensor([-0.8415, 0.9093]) 2025-09-07T08:19:26.3867790Z 2025-09-07T08:19:26.3868383Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-09-07T08:19:26.3869046Z 2025-09-07T08:19:26.3869320Z which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T08:19:26.3869691Z ^ 2025-09-07T08:19:26.3869897Z warnings.warn(msg) 2025-09-07T08:19:26.3870159Z 2025-09-07T08:19:26.3870475Z --- Parse Warning: 12 / 146 --- 2025-09-07T08:19:26.3871538Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1571. 2025-09-07T08:19:26.3872729Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3873529Z Given an operator and some sample arguments, tests if the operator is 2025-09-07T08:19:26.3873964Z registered correctly. 2025-09-07T08:19:26.3874230Z 2025-09-07T08:19:26.3874558Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-09-07T08:19:26.3875131Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-09-07T08:19:26.3875711Z and these APIs require that the functions you pass them satisfy certain 2025-09-07T08:19:26.3876355Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-09-07T08:19:26.3876846Z ``opcheck`` tests these metadata and properties. 2025-09-07T08:19:26.3877171Z 2025-09-07T08:19:26.3877378Z Concretely, we test the following: 2025-09-07T08:19:26.3877676Z 2025-09-07T08:19:26.3877957Z - test_schema: If the schema matches the implementation of 2025-09-07T08:19:26.3878466Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-09-07T08:19:26.3878667Z then we check the implementation mutates the Tensor. If the schema 2025-09-07T08:19:26.3878862Z specifies that we return a new Tensor, then we check that the 2025-09-07T08:19:26.3879078Z implementation returns a new Tensor (instead of an existing one or 2025-09-07T08:19:26.3879188Z a view of an existing one). 2025-09-07T08:19:26.3879402Z - test_autograd_registration: If the operator supports training 2025-09-07T08:19:26.3879596Z (autograd): we check that its autograd formula is registered via 2025-09-07T08:19:26.3879809Z torch.library.register_autograd or a manual registration to one 2025-09-07T08:19:26.3880011Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-09-07T08:19:26.3880154Z registrations may lead to undefined behavior. 2025-09-07T08:19:26.3880341Z - test_faketensor: If the operator has a FakeTensor kernel 2025-09-07T08:19:26.3880513Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-09-07T08:19:26.3880743Z but not sufficient) for the operator to work with PyTorch compilation 2025-09-07T08:19:26.3880987Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-09-07T08:19:26.3881176Z (also sometimes known as a meta kernel) was registered for the 2025-09-07T08:19:26.3881398Z operator and that it is correct. This test takes the result of 2025-09-07T08:19:26.3881583Z running the operator on real tensors and the result of running 2025-09-07T08:19:26.3881775Z the operator on FakeTensors and checks that they have the same 2025-09-07T08:19:26.3881919Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-09-07T08:19:26.3882120Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-09-07T08:19:26.3882296Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-09-07T08:19:26.3882496Z This checks that the outputs (and gradients, if applicable) are the 2025-09-07T08:19:26.3882640Z same under eager-mode PyTorch and torch.compile. 2025-09-07T08:19:26.3882829Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-09-07T08:19:26.3883004Z other things it tests are that the operator supports 2025-09-07T08:19:26.3883218Z functionalization and that the backward pass (if it exists) also 2025-09-07T08:19:26.3883354Z supports FakeTensor and functionalization. 2025-09-07T08:19:26.3883491Z 2025-09-07T08:19:26.3883684Z For best results, please call ``opcheck`` multiple times with a 2025-09-07T08:19:26.3883868Z representative set of inputs. If your operator supports 2025-09-07T08:19:26.3884175Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-09-07T08:19:26.3884399Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-09-07T08:19:26.3884567Z use ``opcheck`` with inputs on all supported devices. 2025-09-07T08:19:26.3884638Z 2025-09-07T08:19:26.3884723Z Args: 2025-09-07T08:19:26.3884889Z op: The operator. Must either be a function decorated with 2025-09-07T08:19:26.3885103Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-09-07T08:19:26.3885325Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-09-07T08:19:26.3885463Z args: The args to the operator 2025-09-07T08:19:26.3885590Z kwargs: The kwargs to the operator 2025-09-07T08:19:26.3885768Z test_utils: Tests that we should run. Default: all of them. 2025-09-07T08:19:26.3885904Z Example: ("test_schema", "test_faketensor") 2025-09-07T08:19:26.3886099Z raise_exception: If we should raise an exception on the first 2025-09-07T08:19:26.3886255Z error. If False, we will return a dict with information 2025-09-07T08:19:26.3886368Z on if each test passed or not. 2025-09-07T08:19:26.3886599Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-09-07T08:19:26.3886736Z If specified ``atol`` must also be specified. 2025-09-07T08:19:26.3886939Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T08:19:26.3887103Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T08:19:26.3887341Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-09-07T08:19:26.3887477Z If specified ``rtol`` must also be specified. 2025-09-07T08:19:26.3887654Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T08:19:26.3887826Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T08:19:26.3887900Z 2025-09-07T08:19:26.3887987Z .. warning:: 2025-09-07T08:19:26.3888064Z 2025-09-07T08:19:26.3888276Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-09-07T08:19:26.3888491Z opcheck tests if your usage of torch.library APIs is correct while 2025-09-07T08:19:26.3888717Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-09-07T08:19:26.3888937Z mathematically correct. Use both to test custom ops that support 2025-09-07T08:19:26.3889060Z gradient computation. 2025-09-07T08:19:26.3889132Z 2025-09-07T08:19:26.3889218Z Example: 2025-09-07T08:19:26.3889296Z 2025-09-07T08:19:26.3889438Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:26.3889630Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T08:19:26.3889772Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-09-07T08:19:26.3889880Z >>> x_np = x.numpy(force=True) 2025-09-07T08:19:26.3889975Z >>> z_np = x_np * y 2025-09-07T08:19:26.3890118Z >>> return torch.from_numpy(z_np).to(x.device) 2025-09-07T08:19:26.3890192Z >>> 2025-09-07T08:19:26.3890294Z >>> @numpy_mul.register_fake 2025-09-07T08:19:26.3890395Z >>> def _(x, y): 2025-09-07T08:19:26.3890501Z >>> return torch.empty_like(x) 2025-09-07T08:19:26.3890595Z >>> 2025-09-07T08:19:26.3890719Z >>> def setup_context(ctx, inputs, output): 2025-09-07T08:19:26.3890809Z >>> y, = inputs 2025-09-07T08:19:26.3890924Z >>> ctx.y = y 2025-09-07T08:19:26.3890999Z >>> 2025-09-07T08:19:26.3891103Z >>> def backward(ctx, grad): 2025-09-07T08:19:26.3891218Z >>> return grad * ctx.y, None 2025-09-07T08:19:26.3891291Z >>> 2025-09-07T08:19:26.3891520Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-09-07T08:19:26.3891595Z >>> 2025-09-07T08:19:26.3891691Z >>> sample_inputs = [ 2025-09-07T08:19:26.3891802Z >>> (torch.randn(3), 3.14), 2025-09-07T08:19:26.3891927Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-09-07T08:19:26.3892082Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-09-07T08:19:26.3892263Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-09-07T08:19:26.3892346Z >>> ] 2025-09-07T08:19:26.3892428Z >>> 2025-09-07T08:19:26.3892558Z >>> for args in sample_inputs: 2025-09-07T08:19:26.3892704Z >>> torch.library.opcheck(numpy_mul, args) 2025-09-07T08:19:26.3892779Z 2025-09-07T08:19:26.3892856Z 2025-09-07T08:19:26.3893118Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3893197Z 2025-09-07T08:19:26.3893289Z warnings.warn(msg) 2025-09-07T08:19:26.3893360Z 2025-09-07T08:19:26.3893583Z --- Parse Warning: 13 / 146 --- 2025-09-07T08:19:26.3894734Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1285. 2025-09-07T08:19:26.3896279Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3897007Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-09-07T08:19:26.3897567Z 2025-09-07T08:19:26.3897952Z Loads an object saved with :func:`torch.save` from a file. 2025-09-07T08:19:26.3898361Z 2025-09-07T08:19:26.3898694Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-09-07T08:19:26.3899274Z which underlie tensors, specially. They are first deserialized on the 2025-09-07T08:19:26.3899842Z CPU and are then moved to the device they were saved from. If this fails 2025-09-07T08:19:26.3900407Z (e.g. because the run time system doesn't have certain devices), an exception 2025-09-07T08:19:26.3901002Z is raised. However, storages can be dynamically remapped to an alternative 2025-09-07T08:19:26.3901572Z set of devices using the :attr:`map_location` argument. 2025-09-07T08:19:26.3901937Z 2025-09-07T08:19:26.3902353Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-09-07T08:19:26.3902972Z storage with two arguments: storage and location. The storage argument 2025-09-07T08:19:26.3903544Z will be the initial deserialization of the storage, residing on the CPU. 2025-09-07T08:19:26.3904102Z Each serialized storage has a location tag associated with it which 2025-09-07T08:19:26.3904632Z identifies the device it was saved from, and this tag is the second 2025-09-07T08:19:26.3905205Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-09-07T08:19:26.3905794Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-09-07T08:19:26.3906328Z :attr:`map_location` should return either ``None`` or a storage. If 2025-09-07T08:19:26.3906893Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-09-07T08:19:26.3907504Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-09-07T08:19:26.3908126Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-09-07T08:19:26.3908595Z 2025-09-07T08:19:26.3908938Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-09-07T08:19:26.3909516Z a device tag, it indicates the location where all tensors should be loaded. 2025-09-07T08:19:26.3909935Z 2025-09-07T08:19:26.3910300Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-09-07T08:19:26.3910908Z appearing in the file (keys), to ones that specify where to put the 2025-09-07T08:19:26.3911324Z storages (values). 2025-09-07T08:19:26.3911597Z 2025-09-07T08:19:26.3911929Z User extensions can register their own location tags and tagging and 2025-09-07T08:19:26.3912564Z deserialization methods using :func:`torch.serialization.register_package`. 2025-09-07T08:19:26.3913039Z 2025-09-07T08:19:26.3913386Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-09-07T08:19:26.3913854Z 2025-09-07T08:19:26.3914041Z Args: 2025-09-07T08:19:26.3914493Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-09-07T08:19:26.3915085Z or a string or os.PathLike object containing a file name 2025-09-07T08:19:26.3915695Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-09-07T08:19:26.3916229Z locations 2025-09-07T08:19:26.3916618Z pickle_module: module used for unpickling metadata and objects (has to 2025-09-07T08:19:26.3917137Z match the :attr:`pickle_module` used to serialize file) 2025-09-07T08:19:26.3917632Z weights_only: Indicates whether unpickler should be restricted to 2025-09-07T08:19:26.3918122Z loading only tensors, primitive types, dictionaries 2025-09-07T08:19:26.3918614Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-09-07T08:19:26.3919088Z See :ref:`weights-only` for more details. 2025-09-07T08:19:26.3919659Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-09-07T08:19:26.3920436Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-09-07T08:19:26.3921232Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-09-07T08:19:26.3922021Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-09-07T08:19:26.3922821Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-09-07T08:19:26.3923434Z will be lazily loaded when their data is accessed. 2025-09-07T08:19:26.3923932Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-09-07T08:19:26.3924604Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-09-07T08:19:26.3925079Z :attr:`errors=...`. 2025-09-07T08:19:26.3925357Z 2025-09-07T08:19:26.3925549Z .. warning:: 2025-09-07T08:19:26.3925914Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-09-07T08:19:26.3926473Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-09-07T08:19:26.3927088Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-09-07T08:19:26.3927704Z during unpickling. Never load data that could have come from an untrusted 2025-09-07T08:19:26.3928350Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-09-07T08:19:26.3928842Z 2025-09-07T08:19:26.3929031Z .. note:: 2025-09-07T08:19:26.3929426Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-09-07T08:19:26.3930088Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-09-07T08:19:26.3930726Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-09-07T08:19:26.3931195Z 2025-09-07T08:19:26.3931389Z .. note:: 2025-09-07T08:19:26.3931759Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-09-07T08:19:26.3932337Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-09-07T08:19:26.3932876Z when loading files saved by Python 2 in Python 3. If this default 2025-09-07T08:19:26.3933455Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-09-07T08:19:26.3934067Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-09-07T08:19:26.3934682Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-09-07T08:19:26.3935268Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-09-07T08:19:26.3935692Z 2025-09-07T08:19:26.3935885Z Example: 2025-09-07T08:19:26.3936147Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-09-07T08:19:26.3936525Z >>> torch.load("tensors.pt", weights_only=True) 2025-09-07T08:19:26.3936883Z # Load all tensors onto the CPU 2025-09-07T08:19:26.3937195Z >>> torch.load( 2025-09-07T08:19:26.3937450Z ... "tensors.pt", 2025-09-07T08:19:26.3937765Z ... map_location=torch.device("cpu"), 2025-09-07T08:19:26.3938108Z ... weights_only=True, 2025-09-07T08:19:26.3938390Z ... ) 2025-09-07T08:19:26.3938660Z # Load all tensors onto the CPU, using a function 2025-09-07T08:19:26.3938999Z >>> torch.load( 2025-09-07T08:19:26.3939267Z ... "tensors.pt", 2025-09-07T08:19:26.3939593Z ... map_location=lambda storage, loc: storage, 2025-09-07T08:19:26.3939931Z ... weights_only=True, 2025-09-07T08:19:26.3940216Z ... ) 2025-09-07T08:19:26.3940458Z # Load all tensors onto GPU 1 2025-09-07T08:19:26.3940755Z >>> torch.load( 2025-09-07T08:19:26.3941018Z ... "tensors.pt", 2025-09-07T08:19:26.3941355Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-09-07T08:19:26.3941741Z ... weights_only=True, 2025-09-07T08:19:26.3942051Z ... ) # type: ignore[attr-defined] 2025-09-07T08:19:26.3942383Z # Map tensors from GPU 1 to GPU 0 2025-09-07T08:19:26.3942717Z >>> torch.load( 2025-09-07T08:19:26.3942985Z ... "tensors.pt", 2025-09-07T08:19:26.3943343Z ... map_location={"cuda:1": "cuda:0"}, 2025-09-07T08:19:26.3943664Z ... weights_only=True, 2025-09-07T08:19:26.3943937Z ... ) 2025-09-07T08:19:26.3944198Z # Load tensor from io.BytesIO object 2025-09-07T08:19:26.3944673Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-09-07T08:19:26.3945148Z >>> with open("tensor.pt", "rb") as f: 2025-09-07T08:19:26.3945487Z ... buffer = io.BytesIO(f.read()) 2025-09-07T08:19:26.3945846Z >>> torch.load(buffer, weights_only=False) 2025-09-07T08:19:26.3946240Z # Load a module with 'ascii' encoding for unpickling 2025-09-07T08:19:26.3946737Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-09-07T08:19:26.3947290Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-09-07T08:19:26.3947682Z 2025-09-07T08:19:26.3948050Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3948490Z 2025-09-07T08:19:26.3948702Z warnings.warn(msg) 2025-09-07T08:19:26.3948980Z 2025-09-07T08:19:26.3949327Z --- Parse Warning: 14 / 146 --- 2025-09-07T08:19:26.3950554Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py line=1877. 2025-09-07T08:19:26.3951921Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.3952520Z Computes the minimum storage size to hold the given tensor geometry. 2025-09-07T08:19:26.3952928Z 2025-09-07T08:19:26.3953123Z Example 2025-09-07T08:19:26.3953326Z ======= 2025-09-07T08:19:26.3953533Z 2025-09-07T08:19:26.3953867Z This is the size of a newly allocated tensor's storage, in units of elements 2025-09-07T08:19:26.3954294Z 2025-09-07T08:19:26.3954492Z >>> t = torch.empty((10, 20)) 2025-09-07T08:19:26.3954965Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-09-07T08:19:26.3955415Z 200 2025-09-07T08:19:26.3955600Z 2025-09-07T08:19:26.3955797Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:26.3956136Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-09-07T08:19:26.3956496Z >>> size = compute_required_storage_length( 2025-09-07T08:19:26.3956875Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-09-07T08:19:26.3957188Z ... ) 2025-09-07T08:19:26.3957405Z >>> size == t.storage().size() 2025-09-07T08:19:26.3957695Z True 2025-09-07T08:19:26.3957889Z 2025-09-07T08:19:26.3958177Z A valid tensor may have a larger storage size, but never smaller 2025-09-07T08:19:26.3958568Z 2025-09-07T08:19:26.3958798Z >>> slice = torch.empty(100)[20:40] 2025-09-07T08:19:26.3959124Z >>> slice.storage().size() 2025-09-07T08:19:26.3959397Z 100 2025-09-07T08:19:26.3959593Z 2025-09-07T08:19:26.3959823Z >>> compute_required_storage_length( 2025-09-07T08:19:26.3960222Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-09-07T08:19:26.3960585Z ... ) 2025-09-07T08:19:26.3960800Z 40 2025-09-07T08:19:26.3961001Z 2025-09-07T08:19:26.3961185Z 2025-09-07T08:19:26.3961559Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.3962018Z 2025-09-07T08:19:26.3962225Z warnings.warn(msg) 2025-09-07T08:19:26.3962464Z 2025-09-07T08:19:26.3962773Z --- Parse Warning: 15 / 146 --- 2025-09-07T08:19:26.3963948Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=66. 2025-09-07T08:19:26.3965350Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.3965983Z Check if the current accelerator is available at runtime: it was build, all the 2025-09-07T08:19:26.3966573Z required drivers are available and at least one device is visible. 2025-09-07T08:19:26.3967062Z See :ref:`accelerator` for details. 2025-09-07T08:19:26.3967400Z 2025-09-07T08:19:26.3967597Z Returns: 2025-09-07T08:19:26.3968000Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-09-07T08:19:26.3968484Z 2025-09-07T08:19:26.3968840Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-09-07T08:19:26.3969469Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-09-07T08:19:26.3970104Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-09-07T08:19:26.3970616Z :ref:`multiprocessing-poison-fork-note`. 2025-09-07T08:19:26.3970947Z 2025-09-07T08:19:26.3971144Z Example:: 2025-09-07T08:19:26.3971440Z 2025-09-07T08:19:26.3971808Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T08:19:26.3972282Z 2025-09-07T08:19:26.3972967Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-09-07T08:19:26.3973934Z 2025-09-07T08:19:26.3974302Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T08:19:26.3974784Z ^ 2025-09-07T08:19:26.3975092Z warnings.warn(msg) 2025-09-07T08:19:26.3975337Z 2025-09-07T08:19:26.3975660Z --- Parse Warning: 16 / 146 --- 2025-09-07T08:19:26.3976870Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=212. 2025-09-07T08:19:26.3978138Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.3978716Z Wait for all kernels in all streams on the given device to complete. 2025-09-07T08:19:26.3979123Z 2025-09-07T08:19:26.3979309Z Args: 2025-09-07T08:19:26.3979766Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-09-07T08:19:26.3980430Z the current :ref:`accelerator` device type. If not given, 2025-09-07T08:19:26.3980975Z use :func:`torch.accelerator.current_device_index` by default. 2025-09-07T08:19:26.3981357Z 2025-09-07T08:19:26.3981784Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-09-07T08:19:26.3982296Z 2025-09-07T08:19:26.3982493Z Example:: 2025-09-07T08:19:26.3982706Z 2025-09-07T08:19:26.3982957Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:26.3983480Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T08:19:26.3984011Z >>> start_event = torch.Event(enable_timing=True) 2025-09-07T08:19:26.3984406Z >>> end_event = torch.Event(enable_timing=True) 2025-09-07T08:19:26.3984746Z >>> start_event.record() 2025-09-07T08:19:26.3985186Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-09-07T08:19:26.3985653Z >>> sum = torch.sum(tensor) 2025-09-07T08:19:26.3985963Z >>> end_event.record() 2025-09-07T08:19:26.3986273Z >>> torch.accelerator.synchronize() 2025-09-07T08:19:26.3986716Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-09-07T08:19:26.3987115Z 2025-09-07T08:19:26.3987808Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-09-07T08:19:26.3988553Z 2025-09-07T08:19:26.3988923Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T08:19:26.3989401Z ^ 2025-09-07T08:19:26.3989709Z warnings.warn(msg) 2025-09-07T08:19:26.3989952Z 2025-09-07T08:19:26.3990243Z --- Parse Warning: 17 / 146 --- 2025-09-07T08:19:26.3991315Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=434. 2025-09-07T08:19:26.3992499Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.3992994Z Retrieves the CUDA runtime API module. 2025-09-07T08:19:26.3993295Z 2025-09-07T08:19:26.3993481Z 2025-09-07T08:19:26.3993844Z This function initializes the CUDA runtime environment if it is not already 2025-09-07T08:19:26.3994485Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-09-07T08:19:26.3995053Z runtime API module provides access to various CUDA runtime functions. 2025-09-07T08:19:26.3995479Z 2025-09-07T08:19:26.3995677Z Args: 2025-09-07T08:19:26.3995896Z ``None`` 2025-09-07T08:19:26.3996114Z 2025-09-07T08:19:26.3996305Z Returns: 2025-09-07T08:19:26.3996585Z module: The CUDA runtime API module (_cudart). 2025-09-07T08:19:26.3996923Z 2025-09-07T08:19:26.3997103Z Raises: 2025-09-07T08:19:26.3997466Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-09-07T08:19:26.3998177Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-09-07T08:19:26.3998739Z 2025-09-07T08:19:26.3998976Z Example of CUDA operations with profiling: 2025-09-07T08:19:26.3999345Z >>> import torch 2025-09-07T08:19:26.3999665Z >>> from torch.cuda import cudart, check_error 2025-09-07T08:19:26.4000009Z >>> import os 2025-09-07T08:19:26.4000248Z >>> 2025-09-07T08:19:26.4000498Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-09-07T08:19:26.4000812Z >>> 2025-09-07T08:19:26.4001089Z >>> def perform_cuda_operations_with_streams(): 2025-09-07T08:19:26.4001459Z >>> stream = torch.cuda.Stream() 2025-09-07T08:19:26.4001821Z >>> with torch.cuda.stream(stream): 2025-09-07T08:19:26.4002188Z >>> x = torch.randn(100, 100, device='cuda') 2025-09-07T08:19:26.4002563Z >>> y = torch.randn(100, 100, device='cuda') 2025-09-07T08:19:26.4002916Z >>> z = torch.mul(x, y) 2025-09-07T08:19:26.4003216Z >>> return z 2025-09-07T08:19:26.4003478Z >>> 2025-09-07T08:19:26.4003733Z >>> torch.cuda.synchronize() 2025-09-07T08:19:26.4004183Z >>> print("====== Start nsys profiling ======") 2025-09-07T08:19:26.4004563Z >>> check_error(cudart().cudaProfilerStart()) 2025-09-07T08:19:26.4004957Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-09-07T08:19:26.4005363Z >>> result = perform_cuda_operations_with_streams() 2025-09-07T08:19:26.4005766Z >>> print("CUDA operations completed.") 2025-09-07T08:19:26.4006161Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-09-07T08:19:26.4006567Z >>> print("====== End nsys profiling ======") 2025-09-07T08:19:26.4006882Z 2025-09-07T08:19:26.4007193Z To run this example and save the profiling information, execute: 2025-09-07T08:19:26.4007898Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T08:19:26.4008474Z 2025-09-07T08:19:26.4008836Z This command profiles the CUDA operations in the provided script and saves 2025-09-07T08:19:26.4009401Z the profiling information to a file named `trace_name.prof`. 2025-09-07T08:19:26.4009945Z The `--profile-from-start off` option ensures that profiling starts only 2025-09-07T08:19:26.4010440Z after the `cudaProfilerStart` call in the script. 2025-09-07T08:19:26.4010925Z The `--csv` and `--print-summary` options format the profiling output as a 2025-09-07T08:19:26.4011393Z CSV file and print a summary, respectively. 2025-09-07T08:19:26.4011882Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-09-07T08:19:26.4012393Z overwrite of the output file if it already exists. 2025-09-07T08:19:26.4012741Z 2025-09-07T08:19:26.4013518Z 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)) 2025-09-07T08:19:26.4014354Z 2025-09-07T08:19:26.4014816Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T08:19:26.4015390Z ^ 2025-09-07T08:19:26.4015607Z warnings.warn(msg) 2025-09-07T08:19:26.4015854Z 2025-09-07T08:19:26.4016171Z --- Parse Warning: 18 / 146 --- 2025-09-07T08:19:26.4017269Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2025-09-07T08:19:26.4018517Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4018983Z 2025-09-07T08:19:26.4019326Z Append the given callback function to this ``Future``, which will be run 2025-09-07T08:19:26.4019884Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-09-07T08:19:26.4020459Z the same ``Future``, but the order in which they will be executed cannot 2025-09-07T08:19:26.4020980Z be guaranteed (to enforce a certain order consider chaining: 2025-09-07T08:19:26.4021483Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-09-07T08:19:26.4022014Z is the reference to this ``Future``. The callback function can use the 2025-09-07T08:19:26.4022537Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-09-07T08:19:26.4023097Z already completed, the given callback will be run immediately inline. 2025-09-07T08:19:26.4023526Z 2025-09-07T08:19:26.4023833Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-09-07T08:19:26.4024357Z callback might be invoked while the async kernels that are populating 2025-09-07T08:19:26.4024924Z those tensors haven't yet finished executing on the device. However, the 2025-09-07T08:19:26.4025487Z callback will be invoked with some dedicated streams set as current 2025-09-07T08:19:26.4026022Z (fetched from a global pool) which will be synchronized with those 2025-09-07T08:19:26.4026570Z kernels. Hence any operation performed by the callback on these tensors 2025-09-07T08:19:26.4027114Z will be scheduled on the device after the kernels complete. In other 2025-09-07T08:19:26.4027639Z words, as long as the callback doesn't switch streams, it can safely 2025-09-07T08:19:26.4028189Z manipulate the result without any additional synchronization. This is 2025-09-07T08:19:26.4028701Z similar to the non-blocking behavior of :meth:`wait`. 2025-09-07T08:19:26.4029038Z 2025-09-07T08:19:26.4029364Z Similarly, if the callback returns a value that contains tensors that 2025-09-07T08:19:26.4029897Z reside on a GPU, it can do so even if the kernels that are producing 2025-09-07T08:19:26.4030480Z these tensors are still running on the device, as long as the callback 2025-09-07T08:19:26.4031229Z didn't change streams during its execution. If one wants to change 2025-09-07T08:19:26.4031758Z streams, one must be careful to re-synchronize them with the original 2025-09-07T08:19:26.4032309Z streams, that is, those that were current when the callback was invoked. 2025-09-07T08:19:26.4032730Z 2025-09-07T08:19:26.4032924Z Args: 2025-09-07T08:19:26.4033234Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-09-07T08:19:26.4033669Z the only argument. 2025-09-07T08:19:26.4033972Z 2025-09-07T08:19:26.4034164Z Returns: 2025-09-07T08:19:26.4034445Z A new ``Future`` object that holds the return value of the 2025-09-07T08:19:26.4034907Z ``callback`` and will be marked as completed when the given 2025-09-07T08:19:26.4035298Z ``callback`` finishes. 2025-09-07T08:19:26.4035566Z 2025-09-07T08:19:26.4035841Z .. note:: Note that if the callback function throws, either 2025-09-07T08:19:26.4036343Z through the original future being completed with an exception and 2025-09-07T08:19:26.4036872Z calling ``fut.wait()``, or through other code in the callback, the 2025-09-07T08:19:26.4037437Z future returned by ``then`` will be marked appropriately with the 2025-09-07T08:19:26.4037954Z encountered error. However, if this callback later completes 2025-09-07T08:19:26.4038490Z additional futures, those futures are not marked as completed with 2025-09-07T08:19:26.4039031Z an error and the user is responsible for handling completion/waiting 2025-09-07T08:19:26.4039469Z on those futures independently. 2025-09-07T08:19:26.4039766Z 2025-09-07T08:19:26.4039949Z Example:: 2025-09-07T08:19:26.4040157Z 2025-09-07T08:19:26.4040413Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T08:19:26.4040790Z >>> def callback(fut): 2025-09-07T08:19:26.4041106Z ... print(f"RPC return value is {fut.wait()}.") 2025-09-07T08:19:26.4041484Z >>> fut = torch.futures.Future() 2025-09-07T08:19:26.4041904Z >>> # The inserted callback will print the return value when 2025-09-07T08:19:26.4042307Z >>> # receiving the response from "worker1" 2025-09-07T08:19:26.4042638Z >>> cb_fut = fut.then(callback) 2025-09-07T08:19:26.4042955Z >>> chain_cb_fut = cb_fut.then( 2025-09-07T08:19:26.4043308Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-09-07T08:19:26.4043660Z ... ) 2025-09-07T08:19:26.4043885Z >>> fut.set_result(5) 2025-09-07T08:19:26.4044238Z RPC return value is 5. 2025-09-07T08:19:26.4044522Z Chained cb done. None 2025-09-07T08:19:26.4044777Z 2025-09-07T08:19:26.4045133Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4045589Z 2025-09-07T08:19:26.4045809Z warnings.warn(msg) 2025-09-07T08:19:26.4046057Z 2025-09-07T08:19:26.4046385Z --- Parse Warning: 19 / 146 --- 2025-09-07T08:19:26.4047556Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=211. 2025-09-07T08:19:26.4048838Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4049314Z 2025-09-07T08:19:26.4049620Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-09-07T08:19:26.4050167Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-09-07T08:19:26.4050617Z cannot be marked completed twice. 2025-09-07T08:19:26.4050907Z 2025-09-07T08:19:26.4051235Z If the result contains tensors that reside on GPUs, this method can be 2025-09-07T08:19:26.4051808Z called even if the asynchronous kernels that are populating those 2025-09-07T08:19:26.4052356Z tensors haven't yet completed running on the device, provided that the 2025-09-07T08:19:26.4052954Z streams on which those kernels were enqueued are set as the current ones 2025-09-07T08:19:26.4053518Z when this method is called. Put simply, it's safe to call this method 2025-09-07T08:19:26.4054057Z immediately after launching those kernels, without any additional 2025-09-07T08:19:26.4054621Z synchronization, as long as one doesn't change streams in between. This 2025-09-07T08:19:26.4055198Z method will record events on all the relevant current streams and will 2025-09-07T08:19:26.4055747Z use them to ensure proper scheduling for all the consumers of this 2025-09-07T08:19:26.4056160Z ``Future``. 2025-09-07T08:19:26.4056366Z 2025-09-07T08:19:26.4056565Z Args: 2025-09-07T08:19:26.4056853Z result (object): the result object of this ``Future``. 2025-09-07T08:19:26.4057201Z 2025-09-07T08:19:26.4057402Z Example:: 2025-09-07T08:19:26.4057612Z 2025-09-07T08:19:26.4057874Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T08:19:26.4058227Z >>> import threading 2025-09-07T08:19:26.4058503Z >>> import time 2025-09-07T08:19:26.4058804Z >>> def slow_set_future(fut, value): 2025-09-07T08:19:26.4059123Z ... time.sleep(0.5) 2025-09-07T08:19:26.4059402Z ... fut.set_result(value) 2025-09-07T08:19:26.4059720Z >>> fut = torch.futures.Future() 2025-09-07T08:19:26.4060043Z >>> t = threading.Thread( 2025-09-07T08:19:26.4060344Z ... target=slow_set_future, 2025-09-07T08:19:26.4060658Z ... args=(fut, torch.ones(2) * 3) 2025-09-07T08:19:26.4060967Z ... ) 2025-09-07T08:19:26.4061187Z >>> t.start() 2025-09-07T08:19:26.4061440Z >>> print(fut.wait()) 2025-09-07T08:19:26.4061704Z tensor([3., 3.]) 2025-09-07T08:19:26.4061953Z >>> t.join() 2025-09-07T08:19:26.4062177Z 2025-09-07T08:19:26.4062551Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4063000Z 2025-09-07T08:19:26.4063204Z warnings.warn(msg) 2025-09-07T08:19:26.4063447Z 2025-09-07T08:19:26.4063785Z --- Parse Warning: 20 / 146 --- 2025-09-07T08:19:26.4064903Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=145. 2025-09-07T08:19:26.4066163Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4066772Z Compiles compute shader from source and allows one to invoke kernels 2025-09-07T08:19:26.4067268Z defined there from the comfort of Python runtime 2025-09-07T08:19:26.4067624Z Example:: 2025-09-07T08:19:26.4067832Z 2025-09-07T08:19:26.4068080Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-09-07T08:19:26.4068456Z >>> lib = torch.mps.compile_shader( 2025-09-07T08:19:26.4069089Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-09-07T08:19:26.4069671Z ... ) 2025-09-07T08:19:26.4069932Z >>> x = torch.zeros(16, device="mps") 2025-09-07T08:19:26.4070262Z >>> lib.full(x, 3.14) 2025-09-07T08:19:26.4070537Z 2025-09-07T08:19:26.4070898Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4071355Z 2025-09-07T08:19:26.4071568Z warnings.warn(msg) 2025-09-07T08:19:26.4071819Z 2025-09-07T08:19:26.4072109Z --- Parse Warning: 21 / 146 --- 2025-09-07T08:19:26.4073185Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-09-07T08:19:26.4074966Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4075567Z Return the sum of each row of the given sparse tensor. 2025-09-07T08:19:26.4075928Z 2025-09-07T08:19:26.4076260Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-09-07T08:19:26.4076811Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-09-07T08:19:26.4077341Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-09-07T08:19:26.4077827Z returns a dense tensor instead of a sparse tensor. 2025-09-07T08:19:26.4078159Z 2025-09-07T08:19:26.4078526Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-09-07T08:19:26.4079105Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-09-07T08:19:26.4079497Z 2025-09-07T08:19:26.4079818Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-09-07T08:19:26.4080403Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-09-07T08:19:26.4080852Z 2025-09-07T08:19:26.4081051Z Args: 2025-09-07T08:19:26.4081300Z input (Tensor): the input sparse tensor 2025-09-07T08:19:26.4081879Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-09-07T08:19:26.4082375Z over all dims. 2025-09-07T08:19:26.4082817Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-09-07T08:19:26.4083325Z Default: dtype of :attr:`input`. 2025-09-07T08:19:26.4083627Z 2025-09-07T08:19:26.4083839Z Example:: 2025-09-07T08:19:26.4084067Z 2025-09-07T08:19:26.4084338Z >>> nnz = 3 2025-09-07T08:19:26.4084581Z >>> dims = [5, 5, 2, 3] 2025-09-07T08:19:26.4084940Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-09-07T08:19:26.4085413Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-09-07T08:19:26.4085854Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-09-07T08:19:26.4086229Z >>> size = torch.Size(dims) 2025-09-07T08:19:26.4086587Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:26.4086978Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-09-07T08:19:26.4087307Z >>> S 2025-09-07T08:19:26.4087551Z tensor(indices=tensor([[2, 0, 3], 2025-09-07T08:19:26.4087884Z [2, 4, 1]]), 2025-09-07T08:19:26.4088237Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-09-07T08:19:26.4088597Z [ 0.3411, 0.0918, -0.2312]], 2025-09-07T08:19:26.4088892Z 2025-09-07T08:19:26.4089113Z [[ 0.5348, 0.0634, -2.0494], 2025-09-07T08:19:26.4089456Z [-0.7125, -1.0646, 2.1844]], 2025-09-07T08:19:26.4089764Z 2025-09-07T08:19:26.4089974Z [[ 0.1276, 0.1874, -0.6334], 2025-09-07T08:19:26.4090318Z [-1.9682, -0.5340, 0.7483]]]), 2025-09-07T08:19:26.4090700Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-09-07T08:19:26.4091039Z 2025-09-07T08:19:26.4091344Z # when sum over only part of sparse_dims, return a sparse tensor 2025-09-07T08:19:26.4091766Z >>> torch.sparse.sum(S, [1, 3]) 2025-09-07T08:19:26.4092105Z tensor(indices=tensor([[0, 2, 3]]), 2025-09-07T08:19:26.4092458Z values=tensor([[-1.4512, 0.4073], 2025-09-07T08:19:26.4092795Z [-0.8901, 0.2017], 2025-09-07T08:19:26.4093108Z [-0.3183, -1.7539]]), 2025-09-07T08:19:26.4093465Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-09-07T08:19:26.4093798Z 2025-09-07T08:19:26.4094127Z # when sum over all sparse dim, return a dense tensor 2025-09-07T08:19:26.4094519Z # with summed dims squeezed 2025-09-07T08:19:26.4094847Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-09-07T08:19:26.4095174Z tensor([-2.6596, -1.1450]) 2025-09-07T08:19:26.4095463Z 2025-09-07T08:19:26.4095831Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4096289Z 2025-09-07T08:19:26.4096503Z warnings.warn(msg) 2025-09-07T08:19:26.4096754Z 2025-09-07T08:19:26.4097091Z --- Parse Warning: 22 / 146 --- 2025-09-07T08:19:26.4098235Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=as_sparse_gradcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=550. 2025-09-07T08:19:26.4099513Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4100091Z Decorate function, to extend gradcheck for sparse tensors. 2025-09-07T08:19:26.4100482Z 2025-09-07T08:19:26.4100800Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-09-07T08:19:26.4101354Z variants that extends the gradcheck function with support to input 2025-09-07T08:19:26.4101898Z functions that operate on or/and return sparse tensors. 2025-09-07T08:19:26.4102264Z 2025-09-07T08:19:26.4102572Z The specified gradcheck function itself is guaranteed to operate 2025-09-07T08:19:26.4103003Z on strided tensors only. 2025-09-07T08:19:26.4103280Z 2025-09-07T08:19:26.4103481Z For example: 2025-09-07T08:19:26.4103708Z 2025-09-07T08:19:26.4104057Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-09-07T08:19:26.4104497Z >>> x = ( 2025-09-07T08:19:26.4104791Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-09-07T08:19:26.4105149Z ... .to_sparse_coo() 2025-09-07T08:19:26.4105436Z ... .requires_grad_(True) 2025-09-07T08:19:26.4105725Z ... ) 2025-09-07T08:19:26.4105987Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-09-07T08:19:26.4106324Z True 2025-09-07T08:19:26.4106537Z 2025-09-07T08:19:26.4106910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4107358Z 2025-09-07T08:19:26.4107555Z warnings.warn(msg) 2025-09-07T08:19:26.4107801Z 2025-09-07T08:19:26.4108102Z --- Parse Warning: 23 / 146 --- 2025-09-07T08:19:26.4109175Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-09-07T08:19:26.4110368Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4110838Z 2025-09-07T08:19:26.4111173Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-09-07T08:19:26.4111718Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-09-07T08:19:26.4111935Z pushes the map into PyTorch operations called by ``func``, effectively 2025-09-07T08:19:26.4112059Z vectorizing those operations. 2025-09-07T08:19:26.4112140Z 2025-09-07T08:19:26.4112348Z vmap is useful for handling batch dimensions: one can write a function 2025-09-07T08:19:26.4112559Z ``func`` that runs on examples and then lift it to a function that can 2025-09-07T08:19:26.4112770Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-09-07T08:19:26.4112950Z compute batched gradients when composed with autograd. 2025-09-07T08:19:26.4113028Z 2025-09-07T08:19:26.4113113Z .. note:: 2025-09-07T08:19:26.4113304Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-09-07T08:19:26.4113463Z convenience. Use whichever one you'd like. 2025-09-07T08:19:26.4113549Z 2025-09-07T08:19:26.4113659Z Args: 2025-09-07T08:19:26.4113867Z func (function): A Python function that takes one or more arguments. 2025-09-07T08:19:26.4113994Z Must return one or more Tensors. 2025-09-07T08:19:26.4114195Z in_dims (int or nested structure): Specifies which dimension of the 2025-09-07T08:19:26.4114376Z inputs should be mapped over. ``in_dims`` should have a 2025-09-07T08:19:26.4114568Z structure like the inputs. If the ``in_dim`` for a particular 2025-09-07T08:19:26.4114754Z input is None, then that indicates there is no map dimension. 2025-09-07T08:19:26.4114852Z Default: 0. 2025-09-07T08:19:26.4115054Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-09-07T08:19:26.4115248Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-09-07T08:19:26.4115400Z it should have one element per output. Default: 0. 2025-09-07T08:19:26.4115587Z randomness (str): Specifies whether the randomness in this 2025-09-07T08:19:26.4115810Z vmap should be the same or different across batches. If 'different', 2025-09-07T08:19:26.4116011Z the randomness for each batch will be different. If 'same', the 2025-09-07T08:19:26.4116262Z randomness will be the same across batches. If 'error', any calls to 2025-09-07T08:19:26.4116472Z random functions will error. Default: 'error'. WARNING: this flag 2025-09-07T08:19:26.4116674Z only applies to random PyTorch operations and does not apply to 2025-09-07T08:19:26.4116820Z Python's random module or numpy randomness. 2025-09-07T08:19:26.4117046Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-09-07T08:19:26.4117273Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-09-07T08:19:26.4117530Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-09-07T08:19:26.4117796Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-09-07T08:19:26.4117891Z 2025-09-07T08:19:26.4118002Z Returns: 2025-09-07T08:19:26.4118201Z Returns a new "batched" function. It takes the same inputs as 2025-09-07T08:19:26.4118385Z ``func``, except each input has an extra dimension at the index 2025-09-07T08:19:26.4118572Z specified by ``in_dims``. It takes returns the same outputs as 2025-09-07T08:19:26.4118767Z ``func``, except each output has an extra dimension at the index 2025-09-07T08:19:26.4118871Z specified by ``out_dims``. 2025-09-07T08:19:26.4118965Z 2025-09-07T08:19:26.4119047Z .. warning: 2025-09-07T08:19:26.4119245Z :func:`vmap` works best with functional-style code. Please do not 2025-09-07T08:19:26.4119445Z perform any side-effects in ``func``, with the exception of 2025-09-07T08:19:26.4119682Z in-place PyTorch operations. Examples of side-effects include mutating 2025-09-07T08:19:26.4119921Z Python data structures and assigning values to variables not captured 2025-09-07T08:19:26.4120007Z in ``func``. 2025-09-07T08:19:26.4120088Z 2025-09-07T08:19:26.4120328Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-09-07T08:19:26.4120548Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-09-07T08:19:26.4120777Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-09-07T08:19:26.4120857Z 2025-09-07T08:19:26.4120959Z >>> torch.dot # [D], [D] -> [] 2025-09-07T08:19:26.4121167Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-09-07T08:19:26.4121289Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T08:19:26.4121394Z >>> batched_dot(x, y) 2025-09-07T08:19:26.4121472Z 2025-09-07T08:19:26.4121734Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-09-07T08:19:26.4121877Z model authoring experience. 2025-09-07T08:19:26.4121951Z 2025-09-07T08:19:26.4122077Z >>> batch_size, feature_size = 3, 5 2025-09-07T08:19:26.4122246Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T08:19:26.4122327Z >>> 2025-09-07T08:19:26.4122443Z >>> def model(feature_vec): 2025-09-07T08:19:26.4122570Z >>> # Very simple linear model with activation 2025-09-07T08:19:26.4122704Z >>> return feature_vec.dot(weights).relu() 2025-09-07T08:19:26.4122784Z >>> 2025-09-07T08:19:26.4122930Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T08:19:26.4123065Z >>> result = torch.vmap(model)(examples) 2025-09-07T08:19:26.4123140Z 2025-09-07T08:19:26.4123388Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-09-07T08:19:26.4123631Z or impossible to batch. One example is higher-order gradient computation. 2025-09-07T08:19:26.4123859Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-09-07T08:19:26.4124184Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-09-07T08:19:26.4124467Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-09-07T08:19:26.4124713Z we can vectorize the whole computation, computing the Jacobian in a single 2025-09-07T08:19:26.4124814Z call to ``autograd.grad``. 2025-09-07T08:19:26.4124887Z 2025-09-07T08:19:26.4124982Z >>> # Setup 2025-09-07T08:19:26.4125062Z >>> N = 5 2025-09-07T08:19:26.4125152Z >>> f = lambda x: x**2 2025-09-07T08:19:26.4125288Z >>> x = torch.randn(N, requires_grad=True) 2025-09-07T08:19:26.4125369Z >>> y = f(x) 2025-09-07T08:19:26.4125477Z >>> I_N = torch.eye(N) 2025-09-07T08:19:26.4125558Z >>> 2025-09-07T08:19:26.4125657Z >>> # Sequential approach 2025-09-07T08:19:26.4125885Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-09-07T08:19:26.4125997Z >>> for v in I_N.unbind()] 2025-09-07T08:19:26.4126153Z >>> jacobian = torch.stack(jacobian_rows) 2025-09-07T08:19:26.4126236Z >>> 2025-09-07T08:19:26.4126355Z >>> # vectorized gradient computation 2025-09-07T08:19:26.4126458Z >>> def get_vjp(v): 2025-09-07T08:19:26.4126578Z >>> return torch.autograd.grad(y, x, v) 2025-09-07T08:19:26.4126708Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-09-07T08:19:26.4126785Z 2025-09-07T08:19:26.4127044Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-09-07T08:19:26.4127133Z 2025-09-07T08:19:26.4127235Z >>> torch.dot # [D], [D] -> [] 2025-09-07T08:19:26.4127349Z >>> batched_dot = torch.vmap( 2025-09-07T08:19:26.4127449Z ... torch.vmap(torch.dot) 2025-09-07T08:19:26.4127564Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-09-07T08:19:26.4127712Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-09-07T08:19:26.4127837Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-09-07T08:19:26.4127924Z 2025-09-07T08:19:26.4128165Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-09-07T08:19:26.4128315Z the dimension that each inputs are batched along as 2025-09-07T08:19:26.4128401Z 2025-09-07T08:19:26.4128500Z >>> torch.dot # [N], [N] -> [] 2025-09-07T08:19:26.4128720Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-09-07T08:19:26.4128853Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T08:19:26.4128944Z >>> batched_dot( 2025-09-07T08:19:26.4129036Z ... x, y 2025-09-07T08:19:26.4129227Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-09-07T08:19:26.4129304Z 2025-09-07T08:19:26.4129591Z If there are multiple inputs each of which is batched along different dimensions, 2025-09-07T08:19:26.4129811Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-09-07T08:19:26.4129901Z 2025-09-07T08:19:26.4130002Z >>> torch.dot # [D], [D] -> [] 2025-09-07T08:19:26.4130233Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-09-07T08:19:26.4130367Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T08:19:26.4130458Z >>> batched_dot( 2025-09-07T08:19:26.4130549Z ... x, y 2025-09-07T08:19:26.4130741Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-09-07T08:19:26.4130819Z 2025-09-07T08:19:26.4131061Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-09-07T08:19:26.4131168Z matching the shape of the input: 2025-09-07T08:19:26.4131257Z 2025-09-07T08:19:26.4131398Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-09-07T08:19:26.4131518Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T08:19:26.4131626Z >>> input = {"x": x, "y": y} 2025-09-07T08:19:26.4131803Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-09-07T08:19:26.4131936Z >>> batched_dot(input) 2025-09-07T08:19:26.4132011Z 2025-09-07T08:19:26.4132285Z By default, the output is batched along the first dimension. However, it can be batched 2025-09-07T08:19:26.4132417Z along any dimension by using ``out_dims`` 2025-09-07T08:19:26.4132494Z 2025-09-07T08:19:26.4132597Z >>> f = lambda x: x**2 2025-09-07T08:19:26.4132693Z >>> x = torch.randn(2, 5) 2025-09-07T08:19:26.4132816Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-09-07T08:19:26.4132921Z >>> batched_pow(x) # [5, 2] 2025-09-07T08:19:26.4132998Z 2025-09-07T08:19:26.4133285Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-09-07T08:19:26.4133387Z accept kwargs 2025-09-07T08:19:26.4133465Z 2025-09-07T08:19:26.4133574Z >>> x = torch.randn([2, 5]) 2025-09-07T08:19:26.4133668Z >>> def fn(x, scale=4.): 2025-09-07T08:19:26.4133800Z >>> return x * scale 2025-09-07T08:19:26.4133891Z >>> 2025-09-07T08:19:26.4133998Z >>> batched_pow = torch.vmap(fn) 2025-09-07T08:19:26.4134146Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-09-07T08:19:26.4134365Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-09-07T08:19:26.4134442Z 2025-09-07T08:19:26.4134537Z .. note:: 2025-09-07T08:19:26.4134757Z vmap does not provide general autobatching or handle variable-length 2025-09-07T08:19:26.4134871Z sequences out of the box. 2025-09-07T08:19:26.4134945Z 2025-09-07T08:19:26.4135196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4135286Z 2025-09-07T08:19:26.4135380Z warnings.warn(msg) 2025-09-07T08:19:26.4135457Z 2025-09-07T08:19:26.4135680Z --- Parse Warning: 24 / 146 --- 2025-09-07T08:19:26.4136522Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-09-07T08:19:26.4136798Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4137029Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-09-07T08:19:26.4137235Z input(s) specified by ``argnums``. This operator can be nested to 2025-09-07T08:19:26.4137344Z compute higher-order gradients. 2025-09-07T08:19:26.4137420Z 2025-09-07T08:19:26.4137513Z Args: 2025-09-07T08:19:26.4137719Z func (Callable): A Python function that takes one or more arguments. 2025-09-07T08:19:26.4138024Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-09-07T08:19:26.4138314Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-09-07T08:19:26.4138418Z ``(output, aux)``. 2025-09-07T08:19:26.4138707Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-09-07T08:19:26.4138912Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-09-07T08:19:26.4139141Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-09-07T08:19:26.4139303Z auxiliary objects: ``(output, aux)``. Default: False. 2025-09-07T08:19:26.4139379Z 2025-09-07T08:19:26.4139479Z Returns: 2025-09-07T08:19:26.4139757Z Function to compute gradients with respect to its inputs. By default, the output of 2025-09-07T08:19:26.4140005Z the function is the gradient tensor(s) with respect to the first argument. 2025-09-07T08:19:26.4140279Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-09-07T08:19:26.4140527Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-09-07T08:19:26.4140709Z respect to each ``argnums`` value is returned. 2025-09-07T08:19:26.4140790Z 2025-09-07T08:19:26.4140902Z Example of using ``grad``: 2025-09-07T08:19:26.4140980Z 2025-09-07T08:19:26.4141079Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4141249Z >>> from torch.func import grad 2025-09-07T08:19:26.4141370Z >>> x = torch.randn([]) 2025-09-07T08:19:26.4141505Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-09-07T08:19:26.4141626Z >>> assert torch.allclose(cos_x, x.cos()) 2025-09-07T08:19:26.4164235Z >>> 2025-09-07T08:19:26.4164395Z >>> # Second-order gradients 2025-09-07T08:19:26.4164583Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-09-07T08:19:26.4164730Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-09-07T08:19:26.4164809Z 2025-09-07T08:19:26.4165173Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-09-07T08:19:26.4165257Z 2025-09-07T08:19:26.4165365Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4165488Z >>> from torch.func import grad, vmap 2025-09-07T08:19:26.4165604Z >>> batch_size, feature_size = 3, 5 2025-09-07T08:19:26.4165694Z >>> 2025-09-07T08:19:26.4165812Z >>> def model(weights, feature_vec): 2025-09-07T08:19:26.4165940Z >>> # Very simple linear model with activation 2025-09-07T08:19:26.4166063Z >>> assert feature_vec.dim() == 1 2025-09-07T08:19:26.4166188Z >>> return feature_vec.dot(weights).relu() 2025-09-07T08:19:26.4166276Z >>> 2025-09-07T08:19:26.4166416Z >>> def compute_loss(weights, example, target): 2025-09-07T08:19:26.4166527Z >>> y = model(weights, example) 2025-09-07T08:19:26.4166672Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-09-07T08:19:26.4166754Z >>> 2025-09-07T08:19:26.4166936Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T08:19:26.4167083Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T08:19:26.4167199Z >>> targets = torch.randn(batch_size) 2025-09-07T08:19:26.4167335Z >>> inputs = (weights, examples, targets) 2025-09-07T08:19:26.4167568Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-09-07T08:19:26.4167669Z ... *inputs 2025-09-07T08:19:26.4167751Z ... ) 2025-09-07T08:19:26.4167827Z 2025-09-07T08:19:26.4168012Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-09-07T08:19:26.4168092Z 2025-09-07T08:19:26.4168227Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4168338Z >>> from torch.func import grad 2025-09-07T08:19:26.4168474Z >>> def my_loss_func(y, y_pred): 2025-09-07T08:19:26.4168613Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-09-07T08:19:26.4168733Z >>> loss = loss_per_sample.mean() 2025-09-07T08:19:26.4168870Z >>> return loss, (y_pred, loss_per_sample) 2025-09-07T08:19:26.4168951Z >>> 2025-09-07T08:19:26.4169101Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-09-07T08:19:26.4169211Z >>> y_true = torch.rand(4) 2025-09-07T08:19:26.4169343Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-09-07T08:19:26.4169454Z >>> out = fn(y_true, y_preds) 2025-09-07T08:19:26.4169695Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-09-07T08:19:26.4169773Z 2025-09-07T08:19:26.4169871Z .. note:: 2025-09-07T08:19:26.4170037Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-09-07T08:19:26.4170124Z 2025-09-07T08:19:26.4170266Z Case 1: Using ``torch.no_grad`` inside a function: 2025-09-07T08:19:26.4170344Z 2025-09-07T08:19:26.4170489Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4170578Z >>> def f(x): 2025-09-07T08:19:26.4170685Z >>> with torch.no_grad(): 2025-09-07T08:19:26.4170785Z >>> c = x ** 2 2025-09-07T08:19:26.4170878Z >>> return x - c 2025-09-07T08:19:26.4170966Z 2025-09-07T08:19:26.4171164Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-09-07T08:19:26.4171242Z 2025-09-07T08:19:26.4171431Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-09-07T08:19:26.4171509Z 2025-09-07T08:19:26.4171617Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4171719Z >>> with torch.no_grad(): 2025-09-07T08:19:26.4171810Z >>> grad(f)(x) 2025-09-07T08:19:26.4171901Z 2025-09-07T08:19:26.4172119Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-09-07T08:19:26.4172387Z outer one. This is because ``grad`` is a "function transform": its result 2025-09-07T08:19:26.4172609Z should not depend on the result of a context manager outside of ``f``. 2025-09-07T08:19:26.4172685Z 2025-09-07T08:19:26.4172776Z 2025-09-07T08:19:26.4173025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4173116Z 2025-09-07T08:19:26.4173207Z warnings.warn(msg) 2025-09-07T08:19:26.4173475Z 2025-09-07T08:19:26.4173736Z --- Parse Warning: 25 / 146 --- 2025-09-07T08:19:26.4174705Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py line=397. 2025-09-07T08:19:26.4174968Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.4175150Z Register a FakeTensor implementation for this custom op. 2025-09-07T08:19:26.4175232Z 2025-09-07T08:19:26.4175492Z This is necessary to get the operator to work efficiently with torch.compile. 2025-09-07T08:19:26.4175569Z 2025-09-07T08:19:26.4175802Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-09-07T08:19:26.4176029Z specifies the behavior of this operator on Tensors that carry no data. 2025-09-07T08:19:26.4176180Z Given some input Tensors with certain properties 2025-09-07T08:19:26.4176448Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-09-07T08:19:26.4176548Z the output Tensors are. 2025-09-07T08:19:26.4176638Z 2025-09-07T08:19:26.4176913Z Please see :func:`torch.library.register_fake` for more details. 2025-09-07T08:19:26.4177025Z 2025-09-07T08:19:26.4177119Z Args: 2025-09-07T08:19:26.4177295Z fn (Callable): The function to register as the FakeTensor 2025-09-07T08:19:26.4177409Z implementation. 2025-09-07T08:19:26.4177489Z 2025-09-07T08:19:26.4177573Z Examples: 2025-09-07T08:19:26.4177683Z >>> import torch 2025-09-07T08:19:26.4177784Z >>> import numpy as np 2025-09-07T08:19:26.4177893Z >>> from torch import Tensor 2025-09-07T08:19:26.4177989Z >>> 2025-09-07T08:19:26.4178177Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T08:19:26.4178378Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-09-07T08:19:26.4178568Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T08:19:26.4178688Z >>> return (x @ weight.t()) + bias 2025-09-07T08:19:26.4178781Z >>> 2025-09-07T08:19:26.4178883Z >>> @linear.register_fake 2025-09-07T08:19:26.4179000Z >>> def _(x, weight, bias): 2025-09-07T08:19:26.4179138Z >>> assert x.dim() == 2 2025-09-07T08:19:26.4179247Z >>> assert weight.dim() == 2 2025-09-07T08:19:26.4179369Z >>> assert bias.dim() == 1 2025-09-07T08:19:26.4179497Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T08:19:26.4179640Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T08:19:26.4179759Z >>> assert x.device == weight.device 2025-09-07T08:19:26.4179901Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-09-07T08:19:26.4179995Z >>> 2025-09-07T08:19:26.4180097Z >>> x = torch.randn(2, 2) 2025-09-07T08:19:26.4180216Z >>> weight = torch.randn(2, 2) 2025-09-07T08:19:26.4180317Z >>> bias = torch.randn(2) 2025-09-07T08:19:26.4180450Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T08:19:26.4180679Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-09-07T08:19:26.4180819Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T08:19:26.4181082Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-09-07T08:19:26.4181162Z >>> 2025-09-07T08:19:26.4181340Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T08:19:26.4181544Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-09-07T08:19:26.4181663Z >>> def nonzero(x: Tensor) -> Tensor: 2025-09-07T08:19:26.4181778Z >>> x_np = x.cpu().numpy() 2025-09-07T08:19:26.4181913Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T08:19:26.4182050Z >>> return torch.tensor(res, device=x.device) 2025-09-07T08:19:26.4182144Z >>> 2025-09-07T08:19:26.4182254Z >>> @nonzero.register_fake 2025-09-07T08:19:26.4182355Z >>> def _(x): 2025-09-07T08:19:26.4182505Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T08:19:26.4182674Z >>> # Since we cannot peek at the data in an abstract impl, 2025-09-07T08:19:26.4182842Z >>> # we use the ctx object to construct a new symint that 2025-09-07T08:19:26.4182970Z >>> # represents the data-dependent size. 2025-09-07T08:19:26.4183101Z >>> ctx = torch.library.get_ctx() 2025-09-07T08:19:26.4183220Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T08:19:26.4183325Z >>> shape = [nnz, x.dim()] 2025-09-07T08:19:26.4183488Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T08:19:26.4183615Z >>> return result 2025-09-07T08:19:26.4183736Z >>> 2025-09-07T08:19:26.4183850Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-09-07T08:19:26.4183987Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T08:19:26.4184145Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-09-07T08:19:26.4184276Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T08:19:26.4184416Z >>> assert torch.allclose(out, x.nonzero()) 2025-09-07T08:19:26.4184492Z 2025-09-07T08:19:26.4184571Z 2025-09-07T08:19:26.4185111Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-09-07T08:19:26.4185188Z 2025-09-07T08:19:26.4185283Z _._ = None 2025-09-07T08:19:26.4185358Z ^ 2025-09-07T08:19:26.4185451Z warnings.warn(msg) 2025-09-07T08:19:26.4185542Z 2025-09-07T08:19:26.4185740Z --- Parse Warning: 26 / 146 --- 2025-09-07T08:19:26.4186725Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_profile.py line=94. 2025-09-07T08:19:26.4187015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4187091Z 2025-09-07T08:19:26.4187332Z Registers a fake kernel based on the given operator profiles. This fake 2025-09-07T08:19:26.4187575Z kernel registration will override any existing fake kernel registrations. 2025-09-07T08:19:26.4187662Z 2025-09-07T08:19:26.4187872Z The input is a dictionary mapping operator names to a set of operator 2025-09-07T08:19:26.4188102Z profiles, which we will use to generate fake kernels. The operator profiles 2025-09-07T08:19:26.4188313Z are a record of the input and output tensor metadata. Based on this 2025-09-07T08:19:26.4188551Z information we will match a given input to the recorded profile, and return 2025-09-07T08:19:26.4188811Z an output with the same metadata as in the recorded profile. If a profile 2025-09-07T08:19:26.4188953Z doesn't exist then an exception will be thrown. 2025-09-07T08:19:26.4189032Z 2025-09-07T08:19:26.4189275Z The fake kernel generation is considered unsafe because it relies on the 2025-09-07T08:19:26.4189498Z rigid, pre-defined operator profiles that do not account for potential 2025-09-07T08:19:26.4189757Z variations in output behavior. Specifically, the generated kernels assume a 2025-09-07T08:19:26.4190006Z fixed relationship between input and output ranks. However, in reality, it's 2025-09-07T08:19:26.4190256Z possible that data-dependent operations may produce outputs of different 2025-09-07T08:19:26.4190475Z ranks even when given inputs of the same rank. The generated fake kernels 2025-09-07T08:19:26.4190686Z are inflexible and unable to accommodate these nuances, making them 2025-09-07T08:19:26.4190792Z potentially unsafe. 2025-09-07T08:19:26.4190869Z 2025-09-07T08:19:26.4190950Z Args: 2025-09-07T08:19:26.4191178Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-09-07T08:19:26.4191373Z name to a set of operator profiles from which we will generate fake 2025-09-07T08:19:26.4191464Z kernels. 2025-09-07T08:19:26.4191541Z 2025-09-07T08:19:26.4191621Z Examples: 2025-09-07T08:19:26.4191708Z 2025-09-07T08:19:26.4191877Z >>> # Example: Registering an op-profile from draft-export 2025-09-07T08:19:26.4191981Z >>> import torch 2025-09-07T08:19:26.4192143Z >>> from torch.export._draft_export import draft_export 2025-09-07T08:19:26.4192222Z >>> 2025-09-07T08:19:26.4192408Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-09-07T08:19:26.4192557Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T08:19:26.4192658Z >>> return x + y 2025-09-07T08:19:26.4192760Z >>> 2025-09-07T08:19:26.4192866Z >>> class M(torch.nn.Module): 2025-09-07T08:19:26.4192979Z >>> def forward(self, a, b): 2025-09-07T08:19:26.4193123Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-09-07T08:19:26.4193224Z >>> return res 2025-09-07T08:19:26.4193305Z >>> 2025-09-07T08:19:26.4193472Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-09-07T08:19:26.4193562Z >>> 2025-09-07T08:19:26.4193856Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-09-07T08:19:26.4193986Z >>> decomp = ep.run_decompositions() 2025-09-07T08:19:26.4194062Z 2025-09-07T08:19:26.4194138Z 2025-09-07T08:19:26.4194395Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4194474Z 2025-09-07T08:19:26.4194568Z warnings.warn(msg) 2025-09-07T08:19:26.4194660Z 2025-09-07T08:19:26.4194846Z --- Parse Warning: 27 / 146 --- 2025-09-07T08:19:26.4195709Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=96. 2025-09-07T08:19:26.4196000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4196261Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-09-07T08:19:26.4196336Z 2025-09-07T08:19:26.4196544Z This is a more structured way of using triton kernels with PyTorch. 2025-09-07T08:19:26.4196809Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-09-07T08:19:26.4197059Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-09-07T08:19:26.4197162Z that is simpler; 2025-09-07T08:19:26.4197414Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-09-07T08:19:26.4197661Z want to create an operator that behaves like PyTorch built-in operators. 2025-09-07T08:19:26.4197883Z For example, you may use a ``torch.library`` wrapper API to define the 2025-09-07T08:19:26.4198096Z behavior of the triton kernel when passed a tensor subclass or under 2025-09-07T08:19:26.4198204Z a TorchDispatchMode. 2025-09-07T08:19:26.4198276Z 2025-09-07T08:19:26.4198524Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-09-07T08:19:26.4198635Z when the implementation 2025-09-07T08:19:26.4198848Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-09-07T08:19:26.4199025Z custom operators as opaque (:func:`torch.compile` and 2025-09-07T08:19:26.4199256Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-09-07T08:19:26.4199476Z makes the implementation visible to these subsystems, allowing them 2025-09-07T08:19:26.4199598Z to optimize the triton kernel(s). 2025-09-07T08:19:26.4199678Z 2025-09-07T08:19:26.4199878Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-09-07T08:19:26.4200100Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-09-07T08:19:26.4200288Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-09-07T08:19:26.4200370Z 2025-09-07T08:19:26.4200449Z Args: 2025-09-07T08:19:26.4200677Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-09-07T08:19:26.4200883Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-09-07T08:19:26.4201046Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-09-07T08:19:26.4201313Z To avoid name collisions, please use your project name as the namespace; 2025-09-07T08:19:26.4201555Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-09-07T08:19:26.4201838Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-09-07T08:19:26.4202069Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-09-07T08:19:26.4202339Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-09-07T08:19:26.4202521Z schema (None | str): A schema string for the operator. If None 2025-09-07T08:19:26.4202722Z (recommended) we'll infer a schema for the operator from its type 2025-09-07T08:19:26.4202935Z annotations. We recommend letting us infer a schema unless you 2025-09-07T08:19:26.4203044Z have a specific reason not to. 2025-09-07T08:19:26.4203200Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-09-07T08:19:26.4203279Z 2025-09-07T08:19:26.4203368Z Example:: 2025-09-07T08:19:26.4203457Z 2025-09-07T08:19:26.4203592Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:26.4203688Z >>> import torch 2025-09-07T08:19:26.4203888Z >>> from torch.library import triton_op, wrap_triton 2025-09-07T08:19:26.4203966Z >>> 2025-09-07T08:19:26.4204069Z >>> import triton 2025-09-07T08:19:26.4204268Z >>> from triton import language as tl 2025-09-07T08:19:26.4204346Z >>> 2025-09-07T08:19:26.4204442Z >>> @triton.jit 2025-09-07T08:19:26.4204533Z >>> def add_kernel( 2025-09-07T08:19:26.4204633Z >>> in_ptr0, 2025-09-07T08:19:26.4204725Z >>> in_ptr1, 2025-09-07T08:19:26.4204811Z >>> out_ptr, 2025-09-07T08:19:26.4204912Z >>> n_elements, 2025-09-07T08:19:26.4205027Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T08:19:26.4205121Z >>> ): 2025-09-07T08:19:26.4205234Z >>> pid = tl.program_id(axis=0) 2025-09-07T08:19:26.4205345Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T08:19:26.4205529Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T08:19:26.4205638Z >>> mask = offsets < n_elements 2025-09-07T08:19:26.4205767Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T08:19:26.4205905Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T08:19:26.4206000Z >>> output = x + y 2025-09-07T08:19:26.4206142Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T08:19:26.4206230Z >>> 2025-09-07T08:19:26.4206356Z >>> @triton_op("mylib::add", mutates_args={}) 2025-09-07T08:19:26.4206545Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-09-07T08:19:26.4206658Z >>> output = torch.empty_like(x) 2025-09-07T08:19:26.4206766Z >>> n_elements = output.numel() 2025-09-07T08:19:26.4206859Z >>> 2025-09-07T08:19:26.4206952Z >>> def grid(meta): 2025-09-07T08:19:26.4207125Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T08:19:26.4207205Z >>> 2025-09-07T08:19:26.4207382Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-09-07T08:19:26.4207577Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-09-07T08:19:26.4207669Z >>> return output 2025-09-07T08:19:26.4207752Z >>> 2025-09-07T08:19:26.4207845Z >>> @torch.compile 2025-09-07T08:19:26.4207933Z >>> def f(x, y): 2025-09-07T08:19:26.4208033Z >>> return add(x, y) 2025-09-07T08:19:26.4208112Z >>> 2025-09-07T08:19:26.4208225Z >>> x = torch.randn(3, device="cuda") 2025-09-07T08:19:26.4208371Z >>> y = torch.randn(3, device="cuda") 2025-09-07T08:19:26.4208472Z >>> 2025-09-07T08:19:26.4208563Z >>> z = f(x, y) 2025-09-07T08:19:26.4208680Z >>> assert torch.allclose(z, x + y) 2025-09-07T08:19:26.4208756Z 2025-09-07T08:19:26.4208840Z 2025-09-07T08:19:26.4209090Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4209172Z 2025-09-07T08:19:26.4209264Z warnings.warn(msg) 2025-09-07T08:19:26.4209338Z 2025-09-07T08:19:26.4209549Z --- Parse Warning: 28 / 146 --- 2025-09-07T08:19:26.4210416Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=296. 2025-09-07T08:19:26.4210687Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4210877Z Allows capture of a triton kernel into a graph via make_fx or 2025-09-07T08:19:26.4210986Z non-strict ``torch.export``. 2025-09-07T08:19:26.4211068Z 2025-09-07T08:19:26.4211253Z These technologies perform Dispatcher-based tracing (via 2025-09-07T08:19:26.4211485Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-09-07T08:19:26.4211680Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-09-07T08:19:26.4211794Z can actually be traced into a graph. 2025-09-07T08:19:26.4211878Z 2025-09-07T08:19:26.4212083Z Please use this API together with :func:`torch.library.triton_op`. 2025-09-07T08:19:26.4212166Z 2025-09-07T08:19:26.4212246Z Examples: 2025-09-07T08:19:26.4212321Z 2025-09-07T08:19:26.4212424Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4212515Z >>> import torch 2025-09-07T08:19:26.4212604Z >>> import triton 2025-09-07T08:19:26.4212731Z >>> from triton import language as tl 2025-09-07T08:19:26.4212909Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T08:19:26.4213046Z >>> from torch.library import wrap_triton 2025-09-07T08:19:26.4213149Z >>> 2025-09-07T08:19:26.4213237Z >>> @triton.jit 2025-09-07T08:19:26.4213330Z >>> def add_kernel( 2025-09-07T08:19:26.4213415Z >>> in_ptr0, 2025-09-07T08:19:26.4213505Z >>> in_ptr1, 2025-09-07T08:19:26.4213587Z >>> out_ptr, 2025-09-07T08:19:26.4213673Z >>> n_elements, 2025-09-07T08:19:26.4213792Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T08:19:26.4213871Z >>> ): 2025-09-07T08:19:26.4213989Z >>> pid = tl.program_id(axis=0) 2025-09-07T08:19:26.4214101Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T08:19:26.4214246Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T08:19:26.4214359Z >>> mask = offsets < n_elements 2025-09-07T08:19:26.4214488Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T08:19:26.4214624Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T08:19:26.4214717Z >>> output = x + y 2025-09-07T08:19:26.4214861Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T08:19:26.4214945Z >>> 2025-09-07T08:19:26.4215034Z >>> def add(x, y): 2025-09-07T08:19:26.4215157Z >>> output = torch.empty_like(x) 2025-09-07T08:19:26.4215265Z >>> n_elements = output.numel() 2025-09-07T08:19:26.4215343Z >>> 2025-09-07T08:19:26.4215450Z >>> def grid_fn(meta): 2025-09-07T08:19:26.4215614Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T08:19:26.4215701Z >>> 2025-09-07T08:19:26.4215892Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-09-07T08:19:26.4216007Z >>> return output 2025-09-07T08:19:26.4216117Z >>> 2025-09-07T08:19:26.4216227Z >>> x = torch.randn(3, device="cuda") 2025-09-07T08:19:26.4216338Z >>> y = torch.randn(3, device="cuda") 2025-09-07T08:19:26.4216445Z >>> gm = make_fx(add)(x, y) 2025-09-07T08:19:26.4216536Z >>> print(gm.code) 2025-09-07T08:19:26.4216656Z >>> # def forward(self, x_1, y_1): 2025-09-07T08:19:26.4216887Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-09-07T08:19:26.4217126Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-09-07T08:19:26.4217260Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-09-07T08:19:26.4217365Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-09-07T08:19:26.4217526Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-09-07T08:19:26.4217647Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-09-07T08:19:26.4217746Z >>> # }) 2025-09-07T08:19:26.4217844Z >>> # return empty_like 2025-09-07T08:19:26.4217924Z 2025-09-07T08:19:26.4218016Z 2025-09-07T08:19:26.4218294Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4218368Z 2025-09-07T08:19:26.4218480Z warnings.warn(msg) 2025-09-07T08:19:26.4218552Z 2025-09-07T08:19:26.4218738Z --- Parse Warning: 29 / 146 --- 2025-09-07T08:19:26.4219667Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=286. 2025-09-07T08:19:26.4219925Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4220011Z 2025-09-07T08:19:26.4220230Z Test if two objects are equal, and print an error message if test fails. 2025-09-07T08:19:26.4220312Z 2025-09-07T08:19:26.4220455Z The test is performed with ``actual == desired``. 2025-09-07T08:19:26.4220528Z 2025-09-07T08:19:26.4220646Z Parameters 2025-09-07T08:19:26.4220731Z ---------- 2025-09-07T08:19:26.4220814Z test_string : str 2025-09-07T08:19:26.4220945Z The message supplied to AssertionError. 2025-09-07T08:19:26.4221026Z actual : object 2025-09-07T08:19:26.4221184Z The object to test for equality against `desired`. 2025-09-07T08:19:26.4221273Z desired : object 2025-09-07T08:19:26.4221362Z The expected result. 2025-09-07T08:19:26.4221448Z 2025-09-07T08:19:26.4221526Z Examples 2025-09-07T08:19:26.4221615Z -------- 2025-09-07T08:19:26.4221729Z >>> np.testing.print_assert_equal( 2025-09-07T08:19:26.4221842Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-09-07T08:19:26.4221939Z ... ) # doctest: +SKIP 2025-09-07T08:19:26.4222049Z >>> np.testing.print_assert_equal( 2025-09-07T08:19:26.4222158Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-09-07T08:19:26.4222260Z ... ) # doctest: +SKIP 2025-09-07T08:19:26.4222370Z Traceback (most recent call last): 2025-09-07T08:19:26.4222455Z ... 2025-09-07T08:19:26.4222581Z AssertionError: Test XYZ of func xyz failed 2025-09-07T08:19:26.4222661Z ACTUAL: 2025-09-07T08:19:26.4222749Z [0, 1] 2025-09-07T08:19:26.4222827Z DESIRED: 2025-09-07T08:19:26.4222909Z [0, 2] 2025-09-07T08:19:26.4222986Z 2025-09-07T08:19:26.4223059Z 2025-09-07T08:19:26.4223315Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4223391Z 2025-09-07T08:19:26.4223478Z warnings.warn(msg) 2025-09-07T08:19:26.4223560Z 2025-09-07T08:19:26.4223737Z --- Parse Warning: 30 / 146 --- 2025-09-07T08:19:26.4224697Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=331. 2025-09-07T08:19:26.4224990Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4225072Z 2025-09-07T08:19:26.4225276Z Raises an AssertionError if two items are not equal up to desired 2025-09-07T08:19:26.4225357Z precision. 2025-09-07T08:19:26.4225440Z 2025-09-07T08:19:26.4225611Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T08:19:26.4225794Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T08:19:26.4225976Z instead of this function for more consistent floating point 2025-09-07T08:19:26.4226066Z comparisons. 2025-09-07T08:19:26.4226150Z 2025-09-07T08:19:26.4226361Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-09-07T08:19:26.4226443Z 2025-09-07T08:19:26.4226608Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-09-07T08:19:26.4226684Z 2025-09-07T08:19:26.4226915Z That is a looser test than originally documented, but agrees with what the 2025-09-07T08:19:26.4227144Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-09-07T08:19:26.4227402Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-09-07T08:19:26.4227517Z delegates to assert_array_almost_equal 2025-09-07T08:19:26.4227592Z 2025-09-07T08:19:26.4227680Z Parameters 2025-09-07T08:19:26.4227762Z ---------- 2025-09-07T08:19:26.4227851Z actual : array_like 2025-09-07T08:19:26.4227949Z The object to check. 2025-09-07T08:19:26.4228038Z desired : array_like 2025-09-07T08:19:26.4228139Z The expected object. 2025-09-07T08:19:26.4228231Z decimal : int, optional 2025-09-07T08:19:26.4228341Z Desired precision, default is 7. 2025-09-07T08:19:26.4228445Z err_msg : str, optional 2025-09-07T08:19:26.4228597Z The error message to be printed in case of failure. 2025-09-07T08:19:26.4228704Z verbose : bool, optional 2025-09-07T08:19:26.4228927Z If True, the conflicting values are appended to the error message. 2025-09-07T08:19:26.4229007Z 2025-09-07T08:19:26.4229094Z Raises 2025-09-07T08:19:26.4229171Z ------ 2025-09-07T08:19:26.4229261Z AssertionError 2025-09-07T08:19:26.4229459Z If actual and desired are not equal up to specified precision. 2025-09-07T08:19:26.4229534Z 2025-09-07T08:19:26.4229619Z See Also 2025-09-07T08:19:26.4229694Z -------- 2025-09-07T08:19:26.4229929Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T08:19:26.4230064Z relative and/or absolute precision. 2025-09-07T08:19:26.4230269Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T08:19:26.4230353Z 2025-09-07T08:19:26.4230432Z Examples 2025-09-07T08:19:26.4230511Z -------- 2025-09-07T08:19:26.4230678Z >>> from torch._numpy.testing import assert_almost_equal 2025-09-07T08:19:26.4230807Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-09-07T08:19:26.4230986Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-09-07T08:19:26.4231095Z Traceback (most recent call last): 2025-09-07T08:19:26.4231171Z ... 2025-09-07T08:19:26.4231264Z AssertionError: 2025-09-07T08:19:26.4231381Z Arrays are not almost equal to 10 decimals 2025-09-07T08:19:26.4231471Z ACTUAL: 2.3333333333333 2025-09-07T08:19:26.4231565Z DESIRED: 2.33333334 2025-09-07T08:19:26.4231641Z 2025-09-07T08:19:26.4231744Z >>> assert_almost_equal( 2025-09-07T08:19:26.4231938Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-09-07T08:19:26.4232015Z ... ) 2025-09-07T08:19:26.4232129Z Traceback (most recent call last): 2025-09-07T08:19:26.4232205Z ... 2025-09-07T08:19:26.4232327Z AssertionError: 2025-09-07T08:19:26.4232445Z Arrays are not almost equal to 9 decimals 2025-09-07T08:19:26.4232567Z 2025-09-07T08:19:26.4232680Z Mismatched elements: 1 / 2 (50%) 2025-09-07T08:19:26.4232808Z Max absolute difference: 6.666699636781459e-09 2025-09-07T08:19:26.4232949Z Max relative difference: 2.8571569790287484e-09 2025-09-07T08:19:26.4233080Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T08:19:26.4233210Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T08:19:26.4233297Z 2025-09-07T08:19:26.4233369Z 2025-09-07T08:19:26.4233616Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4233702Z 2025-09-07T08:19:26.4233792Z warnings.warn(msg) 2025-09-07T08:19:26.4233882Z 2025-09-07T08:19:26.4234065Z --- Parse Warning: 31 / 146 --- 2025-09-07T08:19:26.4234988Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=457. 2025-09-07T08:19:26.4235258Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4235396Z 2025-09-07T08:19:26.4235623Z Raises an AssertionError if two items are not equal up to significant 2025-09-07T08:19:26.4235705Z digits. 2025-09-07T08:19:26.4235777Z 2025-09-07T08:19:26.4235955Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T08:19:26.4236128Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T08:19:26.4236318Z instead of this function for more consistent floating point 2025-09-07T08:19:26.4236408Z comparisons. 2025-09-07T08:19:26.4236484Z 2025-09-07T08:19:26.4236678Z Given two numbers, check that they are approximately equal. 2025-09-07T08:19:26.4236892Z Approximately equal is defined as the number of significant digits 2025-09-07T08:19:26.4236985Z that agree. 2025-09-07T08:19:26.4237063Z 2025-09-07T08:19:26.4237142Z Parameters 2025-09-07T08:19:26.4237235Z ---------- 2025-09-07T08:19:26.4237344Z actual : scalar 2025-09-07T08:19:26.4237450Z The object to check. 2025-09-07T08:19:26.4237538Z desired : scalar 2025-09-07T08:19:26.4237629Z The expected object. 2025-09-07T08:19:26.4237739Z significant : int, optional 2025-09-07T08:19:26.4237851Z Desired precision, default is 7. 2025-09-07T08:19:26.4237940Z err_msg : str, optional 2025-09-07T08:19:26.4238102Z The error message to be printed in case of failure. 2025-09-07T08:19:26.4238195Z verbose : bool, optional 2025-09-07T08:19:26.4238401Z If True, the conflicting values are appended to the error message. 2025-09-07T08:19:26.4238477Z 2025-09-07T08:19:26.4238551Z Raises 2025-09-07T08:19:26.4238641Z ------ 2025-09-07T08:19:26.4238728Z AssertionError 2025-09-07T08:19:26.4238927Z If actual and desired are not equal up to specified precision. 2025-09-07T08:19:26.4239007Z 2025-09-07T08:19:26.4239083Z See Also 2025-09-07T08:19:26.4239173Z -------- 2025-09-07T08:19:26.4239409Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T08:19:26.4239542Z relative and/or absolute precision. 2025-09-07T08:19:26.4239747Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T08:19:26.4239823Z 2025-09-07T08:19:26.4239911Z Examples 2025-09-07T08:19:26.4239990Z -------- 2025-09-07T08:19:26.4240100Z >>> np.testing.assert_approx_equal( 2025-09-07T08:19:26.4240213Z ... 0.12345677777777e-20, 0.1234567e-20 2025-09-07T08:19:26.4240304Z ... ) # doctest: +SKIP 2025-09-07T08:19:26.4240417Z >>> np.testing.assert_approx_equal( 2025-09-07T08:19:26.4240500Z ... 0.12345670e-20, 2025-09-07T08:19:26.4240628Z ... 0.12345671e-20, # doctest: +SKIP 2025-09-07T08:19:26.4240729Z ... significant=8, 2025-09-07T08:19:26.4240831Z ... ) 2025-09-07T08:19:26.4240946Z >>> np.testing.assert_approx_equal( 2025-09-07T08:19:26.4241030Z ... 0.12345670e-20, 2025-09-07T08:19:26.4241134Z ... 0.12345672e-20, # doctest: +SKIP 2025-09-07T08:19:26.4241230Z ... significant=8, 2025-09-07T08:19:26.4241308Z ... ) 2025-09-07T08:19:26.4241416Z Traceback (most recent call last): 2025-09-07T08:19:26.4241492Z ... 2025-09-07T08:19:26.4241578Z AssertionError: 2025-09-07T08:19:26.4241712Z Items are not equal to 8 significant digits: 2025-09-07T08:19:26.4241799Z ACTUAL: 1.234567e-21 2025-09-07T08:19:26.4241889Z DESIRED: 1.2345672e-21 2025-09-07T08:19:26.4241970Z 2025-09-07T08:19:26.4242126Z the evaluated condition that raises the exception is 2025-09-07T08:19:26.4242206Z 2025-09-07T08:19:26.4242392Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-09-07T08:19:26.4242472Z True 2025-09-07T08:19:26.4242553Z 2025-09-07T08:19:26.4242628Z 2025-09-07T08:19:26.4242880Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4242955Z 2025-09-07T08:19:26.4243047Z warnings.warn(msg) 2025-09-07T08:19:26.4243162Z 2025-09-07T08:19:26.4243350Z --- Parse Warning: 32 / 146 --- 2025-09-07T08:19:26.4244370Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=744. 2025-09-07T08:19:26.4244628Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4244703Z 2025-09-07T08:19:26.4244911Z Raises an AssertionError if two array_like objects are not equal. 2025-09-07T08:19:26.4244991Z 2025-09-07T08:19:26.4245205Z Given two array_like objects, check that the shape is equal and all 2025-09-07T08:19:26.4245419Z elements of these objects are equal (but see the Notes for the special 2025-09-07T08:19:26.4245617Z handling of a scalar). An exception is raised at shape mismatch or 2025-09-07T08:19:26.4245879Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-09-07T08:19:26.4246098Z are compared like numbers, no assertion is raised if both objects have 2025-09-07T08:19:26.4246210Z NaNs in the same positions. 2025-09-07T08:19:26.4246289Z 2025-09-07T08:19:26.4246513Z The usual caution for verifying equality with floating point numbers is 2025-09-07T08:19:26.4246601Z advised. 2025-09-07T08:19:26.4246676Z 2025-09-07T08:19:26.4246770Z Parameters 2025-09-07T08:19:26.4246851Z ---------- 2025-09-07T08:19:26.4246936Z x : array_like 2025-09-07T08:19:26.4247047Z The actual object to check. 2025-09-07T08:19:26.4247131Z y : array_like 2025-09-07T08:19:26.4247239Z The desired, expected object. 2025-09-07T08:19:26.4247350Z err_msg : str, optional 2025-09-07T08:19:26.4247506Z The error message to be printed in case of failure. 2025-09-07T08:19:26.4247613Z verbose : bool, optional 2025-09-07T08:19:26.4247814Z If True, the conflicting values are appended to the error message. 2025-09-07T08:19:26.4247910Z strict : bool, optional 2025-09-07T08:19:26.4248114Z If True, raise an AssertionError when either the shape or the data 2025-09-07T08:19:26.4248286Z type of the array_like objects does not match. The special 2025-09-07T08:19:26.4248499Z handling for scalars mentioned in the Notes section is disabled. 2025-09-07T08:19:26.4248573Z 2025-09-07T08:19:26.4248652Z Raises 2025-09-07T08:19:26.4248738Z ------ 2025-09-07T08:19:26.4248826Z AssertionError 2025-09-07T08:19:26.4248971Z If actual and desired objects are not equal. 2025-09-07T08:19:26.4249043Z 2025-09-07T08:19:26.4249122Z See Also 2025-09-07T08:19:26.4249211Z -------- 2025-09-07T08:19:26.4249474Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T08:19:26.4249624Z relative and/or absolute precision. 2025-09-07T08:19:26.4249840Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T08:19:26.4249916Z 2025-09-07T08:19:26.4250004Z Notes 2025-09-07T08:19:26.4250080Z ----- 2025-09-07T08:19:26.4250257Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-09-07T08:19:26.4250483Z function checks that each element of the array_like object is equal to 2025-09-07T08:19:26.4250701Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-09-07T08:19:26.4250787Z 2025-09-07T08:19:26.4250864Z Examples 2025-09-07T08:19:26.4250941Z -------- 2025-09-07T08:19:26.4251082Z The first assert does not raise an exception: 2025-09-07T08:19:26.4251155Z 2025-09-07T08:19:26.4251274Z >>> np.testing.assert_array_equal( 2025-09-07T08:19:26.4251412Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-09-07T08:19:26.4251491Z ... ) 2025-09-07T08:19:26.4251579Z 2025-09-07T08:19:26.4251804Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-09-07T08:19:26.4251950Z functions for these cases instead: 2025-09-07T08:19:26.4252025Z 2025-09-07T08:19:26.4252130Z >>> np.testing.assert_allclose( 2025-09-07T08:19:26.4252334Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-09-07T08:19:26.4252407Z ... ) 2025-09-07T08:19:26.4252482Z 2025-09-07T08:19:26.4252698Z As mentioned in the Notes section, `assert_array_equal` has special 2025-09-07T08:19:26.4252917Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-09-07T08:19:26.4253007Z 2025-09-07T08:19:26.4253109Z >>> x = np.full((2, 5), fill_value=3) 2025-09-07T08:19:26.4253221Z >>> np.testing.assert_array_equal(x, 3) 2025-09-07T08:19:26.4253312Z 2025-09-07T08:19:26.4253520Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-09-07T08:19:26.4253613Z array: 2025-09-07T08:19:26.4253688Z 2025-09-07T08:19:26.4253854Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-09-07T08:19:26.4253974Z Traceback (most recent call last): 2025-09-07T08:19:26.4254049Z ... 2025-09-07T08:19:26.4254145Z AssertionError: 2025-09-07T08:19:26.4254237Z Arrays are not equal 2025-09-07T08:19:26.4254317Z 2025-09-07T08:19:26.4254422Z (shapes (2, 5), () mismatch) 2025-09-07T08:19:26.4254522Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-09-07T08:19:26.4254606Z [3, 3, 3, 3, 3]]) 2025-09-07T08:19:26.4254706Z y: torch.ndarray(3) 2025-09-07T08:19:26.4254778Z 2025-09-07T08:19:26.4254996Z The `strict` parameter also ensures that the array data types match: 2025-09-07T08:19:26.4255070Z 2025-09-07T08:19:26.4255160Z >>> x = np.array([2, 2, 2]) 2025-09-07T08:19:26.4255299Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-09-07T08:19:26.4255442Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-09-07T08:19:26.4255561Z Traceback (most recent call last): 2025-09-07T08:19:26.4255641Z ... 2025-09-07T08:19:26.4255732Z AssertionError: 2025-09-07T08:19:26.4255830Z Arrays are not equal 2025-09-07T08:19:26.4255912Z 2025-09-07T08:19:26.4256053Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-09-07T08:19:26.4256159Z x: torch.ndarray([2, 2, 2]) 2025-09-07T08:19:26.4256254Z y: torch.ndarray([2., 2., 2.]) 2025-09-07T08:19:26.4256338Z 2025-09-07T08:19:26.4256588Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4256661Z 2025-09-07T08:19:26.4256764Z warnings.warn(msg) 2025-09-07T08:19:26.4256837Z 2025-09-07T08:19:26.4257041Z --- Parse Warning: 33 / 146 --- 2025-09-07T08:19:26.4258024Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=851. 2025-09-07T08:19:26.4258313Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4258399Z 2025-09-07T08:19:26.4258608Z Raises an AssertionError if two objects are not equal up to desired 2025-09-07T08:19:26.4258700Z precision. 2025-09-07T08:19:26.4258774Z 2025-09-07T08:19:26.4258944Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T08:19:26.4259134Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T08:19:26.4259318Z instead of this function for more consistent floating point 2025-09-07T08:19:26.4259415Z comparisons. 2025-09-07T08:19:26.4259490Z 2025-09-07T08:19:26.4259723Z The test verifies identical shapes and that the elements of ``actual`` and 2025-09-07T08:19:26.4259820Z ``desired`` satisfy. 2025-09-07T08:19:26.4259897Z 2025-09-07T08:19:26.4260031Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-09-07T08:19:26.4260104Z 2025-09-07T08:19:26.4260328Z That is a looser test than originally documented, but agrees with what the 2025-09-07T08:19:26.4260597Z actual implementation did up to rounding vagaries. An exception is raised 2025-09-07T08:19:26.4260826Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-09-07T08:19:26.4261048Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-09-07T08:19:26.4261167Z objects have NaNs in the same positions. 2025-09-07T08:19:26.4261243Z 2025-09-07T08:19:26.4261334Z Parameters 2025-09-07T08:19:26.4261416Z ---------- 2025-09-07T08:19:26.4261504Z x : array_like 2025-09-07T08:19:26.4261604Z The actual object to check. 2025-09-07T08:19:26.4261683Z y : array_like 2025-09-07T08:19:26.4261791Z The desired, expected object. 2025-09-07T08:19:26.4261887Z decimal : int, optional 2025-09-07T08:19:26.4261996Z Desired precision, default is 6. 2025-09-07T08:19:26.4262120Z err_msg : str, optional 2025-09-07T08:19:26.4262273Z The error message to be printed in case of failure. 2025-09-07T08:19:26.4262380Z verbose : bool, optional 2025-09-07T08:19:26.4262580Z If True, the conflicting values are appended to the error message. 2025-09-07T08:19:26.4262651Z 2025-09-07T08:19:26.4262738Z Raises 2025-09-07T08:19:26.4262818Z ------ 2025-09-07T08:19:26.4262909Z AssertionError 2025-09-07T08:19:26.4263097Z If actual and desired are not equal up to specified precision. 2025-09-07T08:19:26.4263173Z 2025-09-07T08:19:26.4263264Z See Also 2025-09-07T08:19:26.4263342Z -------- 2025-09-07T08:19:26.4263585Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T08:19:26.4263709Z relative and/or absolute precision. 2025-09-07T08:19:26.4263916Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T08:19:26.4263999Z 2025-09-07T08:19:26.4264080Z Examples 2025-09-07T08:19:26.4264157Z -------- 2025-09-07T08:19:26.4264294Z the first assert does not raise an exception 2025-09-07T08:19:26.4264367Z 2025-09-07T08:19:26.4264619Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-09-07T08:19:26.4264693Z 2025-09-07T08:19:26.4264813Z >>> np.testing.assert_array_almost_equal( 2025-09-07T08:19:26.4264966Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-09-07T08:19:26.4265044Z ... ) 2025-09-07T08:19:26.4265163Z Traceback (most recent call last): 2025-09-07T08:19:26.4265241Z ... 2025-09-07T08:19:26.4265328Z AssertionError: 2025-09-07T08:19:26.4265452Z Arrays are not almost equal to 5 decimals 2025-09-07T08:19:26.4265531Z 2025-09-07T08:19:26.4265683Z Mismatched elements: 1 / 3 (33.3%) 2025-09-07T08:19:26.4265830Z Max absolute difference: 5.999999999994898e-05 2025-09-07T08:19:26.4265955Z Max relative difference: 2.5713661239633743e-05 2025-09-07T08:19:26.4266124Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T08:19:26.4266283Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-09-07T08:19:26.4266369Z 2025-09-07T08:19:26.4266485Z >>> np.testing.assert_array_almost_equal( 2025-09-07T08:19:26.4266612Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-09-07T08:19:26.4266694Z ... ) 2025-09-07T08:19:26.4266802Z Traceback (most recent call last): 2025-09-07T08:19:26.4266881Z ... 2025-09-07T08:19:26.4266981Z AssertionError: 2025-09-07T08:19:26.4267098Z Arrays are not almost equal to 5 decimals 2025-09-07T08:19:26.4267188Z 2025-09-07T08:19:26.4267289Z x and y nan location mismatch: 2025-09-07T08:19:26.4267448Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T08:19:26.4267606Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-09-07T08:19:26.4267690Z 2025-09-07T08:19:26.4267776Z 2025-09-07T08:19:26.4268028Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4268134Z 2025-09-07T08:19:26.4268238Z warnings.warn(msg) 2025-09-07T08:19:26.4268315Z 2025-09-07T08:19:26.4268515Z --- Parse Warning: 34 / 146 --- 2025-09-07T08:19:26.4269463Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1848. 2025-09-07T08:19:26.4269732Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4269958Z Context manager that resets warning registry for catching warnings 2025-09-07T08:19:26.4270040Z 2025-09-07T08:19:26.4270297Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-09-07T08:19:26.4270550Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-09-07T08:19:26.4270792Z it impossible to retrigger the warning in this module, whatever you put in 2025-09-07T08:19:26.4271042Z the warnings filters. This context manager accepts a sequence of `modules` 2025-09-07T08:19:26.4271184Z as a keyword argument to its constructor and: 2025-09-07T08:19:26.4271276Z 2025-09-07T08:19:26.4271502Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-09-07T08:19:26.4271588Z on entry; 2025-09-07T08:19:26.4271787Z * resets ``__warningregistry__`` to its previous state on exit. 2025-09-07T08:19:26.4271865Z 2025-09-07T08:19:26.4272101Z This makes it possible to trigger any warning afresh inside the context 2025-09-07T08:19:26.4272285Z manager without disturbing the state of warnings outside. 2025-09-07T08:19:26.4272365Z 2025-09-07T08:19:26.4272611Z For compatibility with Python 3.0, please consider all arguments to be 2025-09-07T08:19:26.4272708Z keyword-only. 2025-09-07T08:19:26.4272802Z 2025-09-07T08:19:26.4272890Z Parameters 2025-09-07T08:19:26.4272973Z ---------- 2025-09-07T08:19:26.4273087Z record : bool, optional 2025-09-07T08:19:26.4273446Z Specifies whether warnings should be captured by a custom 2025-09-07T08:19:26.4273696Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-09-07T08:19:26.4273902Z returned by the context manager. Otherwise None is returned by the 2025-09-07T08:19:26.4274128Z context manager. The objects appended to the list are arguments whose 2025-09-07T08:19:26.4274311Z attributes mirror the arguments to ``showwarning()``. 2025-09-07T08:19:26.4274494Z modules : sequence, optional 2025-09-07T08:19:26.4274729Z Sequence of modules for which to reset warnings registry on entry and 2025-09-07T08:19:26.4274954Z restore on exit. To work correctly, all 'ignore' filters should 2025-09-07T08:19:26.4275064Z filter by one of these modules. 2025-09-07T08:19:26.4275157Z 2025-09-07T08:19:26.4275238Z Examples 2025-09-07T08:19:26.4275335Z -------- 2025-09-07T08:19:26.4275427Z >>> import warnings 2025-09-07T08:19:26.4275606Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-09-07T08:19:26.4275736Z ... modules=[np.core.fromnumeric] 2025-09-07T08:19:26.4275814Z ... ): 2025-09-07T08:19:26.4275933Z ... warnings.simplefilter("always") 2025-09-07T08:19:26.4276166Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-09-07T08:19:26.4276332Z ... # do something that raises a warning but ignore those in 2025-09-07T08:19:26.4276448Z ... # np.core.fromnumeric 2025-09-07T08:19:26.4276529Z 2025-09-07T08:19:26.4276782Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4276867Z 2025-09-07T08:19:26.4276961Z warnings.warn(msg) 2025-09-07T08:19:26.4277104Z 2025-09-07T08:19:26.4277307Z --- Parse Warning: 35 / 146 --- 2025-09-07T08:19:26.4278217Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2025-09-07T08:19:26.4278488Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4278696Z Applies a 1D convolution over a quantized input signal composed of 2025-09-07T08:19:26.4278817Z several quantized input planes. 2025-09-07T08:19:26.4278893Z 2025-09-07T08:19:26.4279098Z For details on input arguments, parameters, and implementation see 2025-09-07T08:19:26.4279216Z :class:`~torch.nn.Conv1d`. 2025-09-07T08:19:26.4279291Z 2025-09-07T08:19:26.4279387Z .. note:: 2025-09-07T08:19:26.4279614Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-09-07T08:19:26.4279691Z 2025-09-07T08:19:26.4279785Z .. note:: 2025-09-07T08:19:26.4279961Z Only `torch.quint8` is supported for the input data type. 2025-09-07T08:19:26.4280047Z 2025-09-07T08:19:26.4280126Z 2025-09-07T08:19:26.4280210Z Attributes: 2025-09-07T08:19:26.4280429Z weight (Tensor): packed tensor derived from the learnable weight 2025-09-07T08:19:26.4280529Z parameter. 2025-09-07T08:19:26.4280679Z scale (Tensor): scalar for the output scale 2025-09-07T08:19:26.4280840Z zero_point (Tensor): scalar for the output zero point 2025-09-07T08:19:26.4280915Z 2025-09-07T08:19:26.4281074Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-09-07T08:19:26.4281154Z 2025-09-07T08:19:26.4281247Z Examples:: 2025-09-07T08:19:26.4281325Z 2025-09-07T08:19:26.4281578Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-09-07T08:19:26.4281720Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-09-07T08:19:26.4281833Z >>> input = torch.randn(20, 16, 100) 2025-09-07T08:19:26.4281938Z >>> # quantize input to quint8 2025-09-07T08:19:26.4282046Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4282254Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-09-07T08:19:26.4282391Z ... dtype=torch.quint8) 2025-09-07T08:19:26.4282490Z >>> output = m(q_input) 2025-09-07T08:19:26.4282569Z 2025-09-07T08:19:26.4282663Z 2025-09-07T08:19:26.4282946Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4283035Z 2025-09-07T08:19:26.4283154Z warnings.warn(msg) 2025-09-07T08:19:26.4283230Z 2025-09-07T08:19:26.4283432Z --- Parse Warning: 36 / 146 --- 2025-09-07T08:19:26.4284419Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=12. 2025-09-07T08:19:26.4284698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4284825Z A quantized long short-term memory (LSTM). 2025-09-07T08:19:26.4284904Z 2025-09-07T08:19:26.4285194Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-09-07T08:19:26.4285274Z 2025-09-07T08:19:26.4285371Z Attributes: 2025-09-07T08:19:26.4285495Z layers : instances of the `_LSTMLayer` 2025-09-07T08:19:26.4285572Z 2025-09-07T08:19:26.4285671Z .. note:: 2025-09-07T08:19:26.4285884Z To access the weights and biases, you need to access them per layer. 2025-09-07T08:19:26.4286068Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-09-07T08:19:26.4286145Z 2025-09-07T08:19:26.4286265Z Examples:: 2025-09-07T08:19:26.4286372Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4286476Z >>> custom_module_config = { 2025-09-07T08:19:26.4286623Z ... 'float_to_observed_custom_module_class': { 2025-09-07T08:19:26.4286744Z ... nn.LSTM: nn.quantizable.LSTM, 2025-09-07T08:19:26.4286825Z ... }, 2025-09-07T08:19:26.4286982Z ... 'observed_to_quantized_custom_module_class': { 2025-09-07T08:19:26.4287122Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-09-07T08:19:26.4287214Z ... } 2025-09-07T08:19:26.4287293Z ... } 2025-09-07T08:19:26.4287511Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-09-07T08:19:26.4287732Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-09-07T08:19:26.4287810Z 2025-09-07T08:19:26.4288094Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4288176Z 2025-09-07T08:19:26.4288270Z warnings.warn(msg) 2025-09-07T08:19:26.4288353Z 2025-09-07T08:19:26.4288544Z --- Parse Warning: 37 / 146 --- 2025-09-07T08:19:26.4289734Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-09-07T08:19:26.4289990Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4290071Z 2025-09-07T08:19:26.4290344Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-09-07T08:19:26.4290566Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-09-07T08:19:26.4290825Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-09-07T08:19:26.4290917Z input by the user. 2025-09-07T08:19:26.4291135Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-09-07T08:19:26.4291301Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-09-07T08:19:26.4291373Z 2025-09-07T08:19:26.4291469Z Note:: 2025-09-07T08:19:26.4291784Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-09-07T08:19:26.4291859Z 2025-09-07T08:19:26.4291951Z Args: 2025-09-07T08:19:26.4292042Z model (nn.Module): 2025-09-07T08:19:26.4292271Z The model whose layers will be sparsified. The layers that needs to be 2025-09-07T08:19:26.4292537Z sparsified should be added separately using the register_layer() function 2025-09-07T08:19:26.4292675Z aggregate_fn (Optional, Callable): 2025-09-07T08:19:26.4292943Z default aggregate_fn that is used if not specified while registering the layer. 2025-09-07T08:19:26.4293106Z specifies how inputs should be aggregated over time. 2025-09-07T08:19:26.4293397Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-09-07T08:19:26.4293479Z Example 2025-09-07T08:19:26.4293651Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-09-07T08:19:26.4293779Z reduce_fn (Optional, Callable): 2025-09-07T08:19:26.4294019Z default reduce_fn that is used if not specified while registering the layer. 2025-09-07T08:19:26.4294283Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-09-07T08:19:26.4294400Z calling agg_fn() on all inputs. 2025-09-07T08:19:26.4294486Z Example 2025-09-07T08:19:26.4294694Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-09-07T08:19:26.4294805Z mask_fn (Optional, Callable): 2025-09-07T08:19:26.4295285Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-09-07T08:19:26.4295536Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-09-07T08:19:26.4295634Z register_layer(). 2025-09-07T08:19:26.4295990Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-09-07T08:19:26.4296083Z arguments. 2025-09-07T08:19:26.4296202Z features (Optional, list): 2025-09-07T08:19:26.4296334Z default selected features to sparsify. 2025-09-07T08:19:26.4296596Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-09-07T08:19:26.4296704Z For example, 2025-09-07T08:19:26.4296994Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-09-07T08:19:26.4297116Z feature_dim (Optional, int): 2025-09-07T08:19:26.4297392Z default dimension of input features. Again, features along this dim will be chosen 2025-09-07T08:19:26.4297505Z for sparsification. 2025-09-07T08:19:26.4297605Z sparse_config (Dict): 2025-09-07T08:19:26.4297823Z Default configuration for the mask_fn. This config will be passed 2025-09-07T08:19:26.4297931Z with the mask_fn() 2025-09-07T08:19:26.4298008Z 2025-09-07T08:19:26.4298090Z Example: 2025-09-07T08:19:26.4298191Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4298285Z >>> model = SomeModel() 2025-09-07T08:19:26.4298538Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-09-07T08:19:26.4298641Z >>> # Initialize aggregate_fn 2025-09-07T08:19:26.4298733Z >>> def agg_fn(x, y): 2025-09-07T08:19:26.4298832Z >>> return x + y 2025-09-07T08:19:26.4298911Z >>> 2025-09-07T08:19:26.4299018Z >>> # Initialize reduce_fn 2025-09-07T08:19:26.4299108Z >>> def reduce_fn(x): 2025-09-07T08:19:26.4299216Z >>> return torch.mean(x, dim=0) 2025-09-07T08:19:26.4299303Z >>> 2025-09-07T08:19:26.4299400Z >>> # Initialize mask_fn 2025-09-07T08:19:26.4299506Z >>> def mask_fn(data): 2025-09-07T08:19:26.4299645Z >>> return torch.eye(data.shape).to(data.device) 2025-09-07T08:19:26.4299726Z >>> 2025-09-07T08:19:26.4299818Z >>> 2025-09-07T08:19:26.4299933Z >>> act_sparsifier.register_layer( 2025-09-07T08:19:26.4300030Z ... model.some_layer, 2025-09-07T08:19:26.4300165Z ... aggregate_fn=agg_fn, 2025-09-07T08:19:26.4300264Z ... reduce_fn=reduce_fn, 2025-09-07T08:19:26.4300395Z ... mask_fn=mask_fn, 2025-09-07T08:19:26.4300474Z ... ) 2025-09-07T08:19:26.4300553Z >>> 2025-09-07T08:19:26.4300665Z >>> # start training process 2025-09-07T08:19:26.4300754Z >>> for _ in [...]: 2025-09-07T08:19:26.4300858Z >>> # epoch starts 2025-09-07T08:19:26.4301029Z >>> # model.forward(), compute_loss() and model.backwards() 2025-09-07T08:19:26.4301118Z >>> # epoch ends 2025-09-07T08:19:26.4301237Z >>> act_sparsifier.step() 2025-09-07T08:19:26.4301335Z >>> # end training process 2025-09-07T08:19:26.4301453Z >>> sparsifier.squash_mask() 2025-09-07T08:19:26.4301527Z 2025-09-07T08:19:26.4301779Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4301873Z 2025-09-07T08:19:26.4301967Z warnings.warn(msg) 2025-09-07T08:19:26.4302046Z 2025-09-07T08:19:26.4302254Z --- Parse Warning: 38 / 146 --- 2025-09-07T08:19:26.4303457Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-09-07T08:19:26.4303761Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4303840Z 2025-09-07T08:19:26.4304057Z Abstract method that needs to be implemented by the child class. 2025-09-07T08:19:26.4304307Z The expected return type should is a dictionary of name to schedule_param value 2025-09-07T08:19:26.4304572Z The returned values will be updated in sparsifier when the scheduler step() function 2025-09-07T08:19:26.4304666Z is called. 2025-09-07T08:19:26.4304742Z 2025-09-07T08:19:26.4304836Z Example: 2025-09-07T08:19:26.4304945Z >>> def get_schedule_param(self): 2025-09-07T08:19:26.4305036Z ... new_param = {} 2025-09-07T08:19:26.4305205Z ... for name in self.sparsifier.data_groups.keys(): 2025-09-07T08:19:26.4305330Z ... new_param[name] = ( 2025-09-07T08:19:26.4305550Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-09-07T08:19:26.4305631Z ... ) 2025-09-07T08:19:26.4305726Z ... return new_param 2025-09-07T08:19:26.4305816Z 2025-09-07T08:19:26.4306147Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-09-07T08:19:26.4306234Z would be halved 2025-09-07T08:19:26.4306324Z 2025-09-07T08:19:26.4306571Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4306660Z 2025-09-07T08:19:26.4306754Z warnings.warn(msg) 2025-09-07T08:19:26.4306829Z 2025-09-07T08:19:26.4307022Z --- Parse Warning: 39 / 146 --- 2025-09-07T08:19:26.4308082Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=229. 2025-09-07T08:19:26.4308353Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4308526Z Squashes the sparse masks into the appropriate tensors. 2025-09-07T08:19:26.4308602Z 2025-09-07T08:19:26.4308817Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-09-07T08:19:26.4308999Z the module will have a `sparse_params` dict attached to it. 2025-09-07T08:19:26.4309091Z 2025-09-07T08:19:26.4309172Z Args: 2025-09-07T08:19:26.4309351Z params_to_keep: List of keys to save in the module or a dict 2025-09-07T08:19:26.4309547Z representing the modules and keys that will have 2025-09-07T08:19:26.4309692Z sparsity parameters saved 2025-09-07T08:19:26.4309917Z params_to_keep_per_layer: Dict to specify the params that should be 2025-09-07T08:19:26.4310072Z saved for specific layers. The keys in the dict 2025-09-07T08:19:26.4310234Z should be the module fqn, while the values should 2025-09-07T08:19:26.4310392Z be a list of strings with the names of the variables 2025-09-07T08:19:26.4310511Z to save in the `sparse_params` 2025-09-07T08:19:26.4310593Z 2025-09-07T08:19:26.4310676Z Examples: 2025-09-07T08:19:26.4310801Z >>> # xdoctest: +SKIP("locals are undefined") 2025-09-07T08:19:26.4310928Z >>> # Don't save any sparse params 2025-09-07T08:19:26.4311042Z >>> sparsifier.squash_mask() 2025-09-07T08:19:26.4311192Z >>> hasattr(model.submodule1, "sparse_params") 2025-09-07T08:19:26.4311279Z False 2025-09-07T08:19:26.4311357Z 2025-09-07T08:19:26.4311482Z >>> # Keep sparse params per layer 2025-09-07T08:19:26.4311594Z >>> sparsifier.squash_mask( 2025-09-07T08:19:26.4311735Z ... params_to_keep_per_layer={ 2025-09-07T08:19:26.4311864Z ... "submodule1.linear1": ("foo", "bar"), 2025-09-07T08:19:26.4311984Z ... "submodule2.linear42": ("baz",), 2025-09-07T08:19:26.4312077Z ... } 2025-09-07T08:19:26.4312158Z ... ) 2025-09-07T08:19:26.4312318Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T08:19:26.4312414Z {'foo': 42, 'bar': 24} 2025-09-07T08:19:26.4312572Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T08:19:26.4312670Z {'baz': 0.1} 2025-09-07T08:19:26.4312749Z 2025-09-07T08:19:26.4312878Z >>> # Keep sparse params for all layers 2025-09-07T08:19:26.4313056Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-09-07T08:19:26.4313232Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T08:19:26.4313339Z {'foo': 42, 'bar': 24} 2025-09-07T08:19:26.4313494Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T08:19:26.4313595Z {'foo': 42, 'bar': 24} 2025-09-07T08:19:26.4313672Z 2025-09-07T08:19:26.4313867Z >>> # Keep some sparse params for all layers, and specific ones for 2025-09-07T08:19:26.4313975Z >>> # some other layers 2025-09-07T08:19:26.4314083Z >>> sparsifier.squash_mask( 2025-09-07T08:19:26.4314211Z ... params_to_keep=("foo", "bar"), 2025-09-07T08:19:26.4314399Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-09-07T08:19:26.4314484Z ... ) 2025-09-07T08:19:26.4314646Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T08:19:26.4314739Z {'foo': 42, 'bar': 24} 2025-09-07T08:19:26.4314905Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T08:19:26.4315014Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-09-07T08:19:26.4315096Z 2025-09-07T08:19:26.4315353Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4315429Z 2025-09-07T08:19:26.4315530Z warnings.warn(msg) 2025-09-07T08:19:26.4315607Z 2025-09-07T08:19:26.4315786Z --- Parse Warning: 40 / 146 --- 2025-09-07T08:19:26.4316839Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2025-09-07T08:19:26.4317127Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4317236Z 2025-09-07T08:19:26.4317487Z Config object that specifies the supported data types passed as arguments to 2025-09-07T08:19:26.4317725Z quantize ops in the reference model spec, for input and output activations, 2025-09-07T08:19:26.4317826Z weights, and biases. 2025-09-07T08:19:26.4317902Z 2025-09-07T08:19:26.4318065Z For example, consider the following reference model: 2025-09-07T08:19:26.4318139Z 2025-09-07T08:19:26.4318295Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-09-07T08:19:26.4318382Z 2025-09-07T08:19:26.4318595Z The pattern in the square brackets refers to the reference pattern of 2025-09-07T08:19:26.4318837Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-09-07T08:19:26.4319061Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-09-07T08:19:26.4319286Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-09-07T08:19:26.4319513Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-09-07T08:19:26.4319626Z the second quantize op (quant2). 2025-09-07T08:19:26.4319737Z 2025-09-07T08:19:26.4319946Z Note that the dtype here does not refer to the interface dtypes of the 2025-09-07T08:19:26.4320151Z op. For example, the "input dtype" here is not the dtype of the input 2025-09-07T08:19:26.4320371Z tensor passed to the quantized linear op. Though it can still be the 2025-09-07T08:19:26.4320566Z same as the interface dtype, this is not always the case, e.g. the 2025-09-07T08:19:26.4320794Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-09-07T08:19:26.4321000Z specified in the DTypeConfig would still be quint8. The semantics of 2025-09-07T08:19:26.4321207Z dtypes here are the same as the semantics of the dtypes specified in 2025-09-07T08:19:26.4321304Z the observers. 2025-09-07T08:19:26.4321379Z 2025-09-07T08:19:26.4321595Z These dtypes are matched against the ones specified in the user's 2025-09-07T08:19:26.4321830Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-09-07T08:19:26.4322050Z specified in the DTypeConfig (if any), then we will quantize the given 2025-09-07T08:19:26.4322284Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-09-07T08:19:26.4322390Z the pattern will not be quantized. 2025-09-07T08:19:26.4322476Z 2025-09-07T08:19:26.4322567Z Example usage:: 2025-09-07T08:19:26.4322640Z 2025-09-07T08:19:26.4322753Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:26.4322858Z >>> dtype_config1 = DTypeConfig( 2025-09-07T08:19:26.4322963Z ... input_dtype=torch.quint8, 2025-09-07T08:19:26.4323081Z ... output_dtype=torch.quint8, 2025-09-07T08:19:26.4323182Z ... weight_dtype=torch.qint8, 2025-09-07T08:19:26.4323299Z ... bias_dtype=torch.float) 2025-09-07T08:19:26.4323376Z 2025-09-07T08:19:26.4323478Z >>> dtype_config2 = DTypeConfig( 2025-09-07T08:19:26.4323617Z ... input_dtype=DTypeWithConstraints( 2025-09-07T08:19:26.4323715Z ... dtype=torch.quint8, 2025-09-07T08:19:26.4323832Z ... quant_min_lower_bound=0, 2025-09-07T08:19:26.4323939Z ... quant_max_upper_bound=255, 2025-09-07T08:19:26.4324017Z ... ), 2025-09-07T08:19:26.4324235Z ... output_dtype=DTypeWithConstraints( 2025-09-07T08:19:26.4324333Z ... dtype=torch.quint8, 2025-09-07T08:19:26.4324449Z ... quant_min_lower_bound=0, 2025-09-07T08:19:26.4324556Z ... quant_max_upper_bound=255, 2025-09-07T08:19:26.4324635Z ... ), 2025-09-07T08:19:26.4324773Z ... weight_dtype=DTypeWithConstraints( 2025-09-07T08:19:26.4324872Z ... dtype=torch.qint8, 2025-09-07T08:19:26.4325031Z ... quant_min_lower_bound=-128, 2025-09-07T08:19:26.4325165Z ... quant_max_upper_bound=127, 2025-09-07T08:19:26.4325243Z ... ), 2025-09-07T08:19:26.4325358Z ... bias_dtype=torch.float) 2025-09-07T08:19:26.4325436Z 2025-09-07T08:19:26.4325537Z >>> dtype_config1.input_dtype 2025-09-07T08:19:26.4325630Z torch.quint8 2025-09-07T08:19:26.4325703Z 2025-09-07T08:19:26.4325813Z >>> dtype_config2.input_dtype 2025-09-07T08:19:26.4325897Z torch.quint8 2025-09-07T08:19:26.4325970Z 2025-09-07T08:19:26.4326115Z >>> dtype_config2.input_dtype_with_constraints 2025-09-07T08:19:26.4326664Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-09-07T08:19:26.4326749Z 2025-09-07T08:19:26.4327008Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4327084Z 2025-09-07T08:19:26.4327185Z warnings.warn(msg) 2025-09-07T08:19:26.4327267Z 2025-09-07T08:19:26.4327469Z --- Parse Warning: 41 / 146 --- 2025-09-07T08:19:26.4328512Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-09-07T08:19:26.4328820Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4328904Z 2025-09-07T08:19:26.4329213Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-09-07T08:19:26.4329518Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-09-07T08:19:26.4329820Z though the requirements on the type of model more-so depends on the specific report the user 2025-09-07T08:19:26.4330134Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-09-07T08:19:26.4330407Z a set of Detector classes, each of which generate reports on quantization configuration 2025-09-07T08:19:26.4330532Z issues a use might have. 2025-09-07T08:19:26.4330623Z 2025-09-07T08:19:26.4330753Z Currently supports generating reports on: 2025-09-07T08:19:26.4330981Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-09-07T08:19:26.4331260Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-09-07T08:19:26.4331553Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-09-07T08:19:26.4331771Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-09-07T08:19:26.4331849Z 2025-09-07T08:19:26.4332262Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-09-07T08:19:26.4332643Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-09-07T08:19:26.4333022Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-09-07T08:19:26.4333240Z has the capability to remove all the observers it inserted as well. 2025-09-07T08:19:26.4333318Z 2025-09-07T08:19:26.4333609Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-09-07T08:19:26.4333687Z 2025-09-07T08:19:26.4334066Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-09-07T08:19:26.4334389Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-09-07T08:19:26.4334468Z 2025-09-07T08:19:26.4334766Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-09-07T08:19:26.4335018Z This set is generated by calling the get_detector_name() of each detector 2025-09-07T08:19:26.4335120Z 2025-09-07T08:19:26.4335468Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-09-07T08:19:26.4335788Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-09-07T08:19:26.4335909Z can be removed at the end if desired 2025-09-07T08:19:26.4335988Z 2025-09-07T08:19:26.4336303Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-09-07T08:19:26.4336541Z This is to ensure we only insert observers once with the ModelReport instance 2025-09-07T08:19:26.4336618Z 2025-09-07T08:19:26.4336874Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-09-07T08:19:26.4337166Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-09-07T08:19:26.4337494Z instance. This also allows the functionality where we can generate the report multiple times 2025-09-07T08:19:26.4337640Z as long as we haven't removed the observers yet. 2025-09-07T08:19:26.4337716Z 2025-09-07T08:19:26.4337827Z Note: 2025-09-07T08:19:26.4338121Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-09-07T08:19:26.4338445Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-09-07T08:19:26.4338740Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-09-07T08:19:26.4338848Z the QuantizationTracer class. 2025-09-07T08:19:26.4338929Z 2025-09-07T08:19:26.4339028Z General Flow for Fx workflow: 2025-09-07T08:19:26.4339429Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-09-07T08:19:26.4339544Z 2.) Prepare your model with prepare_fx 2025-09-07T08:19:26.4339787Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-09-07T08:19:26.4339905Z 4.) Calibrate your model with data 2025-09-07T08:19:26.4340287Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-09-07T08:19:26.4340384Z Optional 2025-09-07T08:19:26.4340651Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-09-07T08:19:26.4340893Z 7.) To help in parsing report information and debugging, view report info as a: 2025-09-07T08:19:26.4340987Z - Table 2025-09-07T08:19:26.4341074Z - Histogram 2025-09-07T08:19:26.4341176Z - Line plot 2025-09-07T08:19:26.4341491Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-09-07T08:19:26.4342764Z 2025-09-07T08:19:26.4342892Z Example (with QuantizationTracer): 2025-09-07T08:19:26.4342987Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4343098Z >>> # get the necessary qconfig 2025-09-07T08:19:26.4343214Z >>> config = PrepareCustomConfig() 2025-09-07T08:19:26.4343368Z >>> skipped_module_names, skipped_module_classes = ( 2025-09-07T08:19:26.4343534Z ... get_skipped_module_name_and_classes(config, False) 2025-09-07T08:19:26.4343613Z ... ) 2025-09-07T08:19:26.4343696Z 2025-09-07T08:19:26.4343827Z >>> # initialize our model and get GraphModule 2025-09-07T08:19:26.4343922Z >>> model = SomeModel() 2025-09-07T08:19:26.4344179Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-09-07T08:19:26.4344346Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-09-07T08:19:26.4344419Z 2025-09-07T08:19:26.4344579Z >>> # get our set of detectors and ModelReport instance 2025-09-07T08:19:26.4344673Z >>> detector_set = set( 2025-09-07T08:19:26.4344787Z ... [ 2025-09-07T08:19:26.4344924Z ... DynamicStaticDetector(tolerance=0.5), 2025-09-07T08:19:26.4345141Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-09-07T08:19:26.4345228Z ... ] 2025-09-07T08:19:26.4345308Z ... ) 2025-09-07T08:19:26.4345523Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-09-07T08:19:26.4345602Z 2025-09-07T08:19:26.4345754Z >>> # now we insert the observers and calibrate the model 2025-09-07T08:19:26.4346020Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-09-07T08:19:26.4346151Z >>> for i in range(num_callibration_batches): 2025-09-07T08:19:26.4346293Z >>> example_input = get_callibration_input() 2025-09-07T08:19:26.4346429Z >>> tracer_model_with_observers(example_input) 2025-09-07T08:19:26.4346506Z 2025-09-07T08:19:26.4346778Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-09-07T08:19:26.4346931Z >>> reports = tracer_reporter.generate_model_report( 2025-09-07T08:19:26.4347053Z ... remove_inserted_observers=True 2025-09-07T08:19:26.4347136Z ... ) 2025-09-07T08:19:26.4347236Z 2025-09-07T08:19:26.4347464Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-09-07T08:19:26.4347621Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-09-07T08:19:26.4347705Z 2025-09-07T08:19:26.4347944Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-09-07T08:19:26.4348116Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-09-07T08:19:26.4348201Z 2025-09-07T08:19:26.4348481Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-09-07T08:19:26.4348695Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-09-07T08:19:26.4348773Z 2025-09-07T08:19:26.4348847Z 2025-09-07T08:19:26.4349110Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4349188Z 2025-09-07T08:19:26.4349311Z warnings.warn(msg) 2025-09-07T08:19:26.4349390Z 2025-09-07T08:19:26.4349588Z --- Parse Warning: 42 / 146 --- 2025-09-07T08:19:26.4350863Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2025-09-07T08:19:26.4351124Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4351209Z 2025-09-07T08:19:26.4351475Z Takes in optional filter values and generates two tables with desired information. 2025-09-07T08:19:26.4351552Z 2025-09-07T08:19:26.4351771Z The generated tables are presented in both a list-of-lists format 2025-09-07T08:19:26.4351850Z 2025-09-07T08:19:26.4352060Z The reason for the two tables are that they handle different things: 2025-09-07T08:19:26.4352220Z 1.) the first table handles all tensor level information 2025-09-07T08:19:26.4352436Z 2.) the second table handles and displays all channel based information 2025-09-07T08:19:26.4352519Z 2025-09-07T08:19:26.4352837Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T08:19:26.4353177Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T08:19:26.4353530Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T08:19:26.4353609Z 2025-09-07T08:19:26.4353710Z Tensor table columns: 2025-09-07T08:19:26.4353927Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T08:19:26.4354088Z ---- --------- --------- --------- --------- --------- 2025-09-07T08:19:26.4354190Z 2025-09-07T08:19:26.4354294Z Per-Channel table columns: 2025-09-07T08:19:26.4354521Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T08:19:26.4354686Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T08:19:26.4354771Z 2025-09-07T08:19:26.4354853Z Args: 2025-09-07T08:19:26.4355114Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T08:19:26.4355229Z contain this filter substring 2025-09-07T08:19:26.4355390Z Default = "", results in all the features being printed 2025-09-07T08:19:26.4355654Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T08:19:26.4355903Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T08:19:26.4355979Z 2025-09-07T08:19:26.4356099Z Returns a dictionary with two keys: 2025-09-07T08:19:26.4356272Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-09-07T08:19:26.4356406Z "tensor_level_info", "channel_level_info" 2025-09-07T08:19:26.4356536Z Each key maps to a tuple with: 2025-09-07T08:19:26.4356651Z A list of the headers of each table 2025-09-07T08:19:26.4356835Z A list of lists containing the table information row by row 2025-09-07T08:19:26.4357003Z The 0th index row will contain the headers of the columns 2025-09-07T08:19:26.4357130Z The rest of the rows will contain data 2025-09-07T08:19:26.4357208Z 2025-09-07T08:19:26.4357296Z Example Use: 2025-09-07T08:19:26.4357432Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4357580Z >>> mod_report_visualizer.generate_filtered_tables( 2025-09-07T08:19:26.4357781Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T08:19:26.4358059Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-09-07T08:19:26.4358162Z 2025-09-07T08:19:26.4358425Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4358503Z 2025-09-07T08:19:26.4358611Z warnings.warn(msg) 2025-09-07T08:19:26.4358690Z 2025-09-07T08:19:26.4358882Z --- Parse Warning: 43 / 146 --- 2025-09-07T08:19:26.4360171Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=399. 2025-09-07T08:19:26.4360431Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4360520Z 2025-09-07T08:19:26.4360791Z Takes in optional filter values and prints out formatted tables of the information. 2025-09-07T08:19:26.4360869Z 2025-09-07T08:19:26.4361224Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-09-07T08:19:26.4361389Z 1.) the first table handles all tensor level information 2025-09-07T08:19:26.4361617Z 2.) the second table handles and displays all channel based information 2025-09-07T08:19:26.4361695Z 2025-09-07T08:19:26.4362017Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T08:19:26.4362357Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T08:19:26.4362714Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T08:19:26.4362805Z 2025-09-07T08:19:26.4362900Z Tensor table columns: 2025-09-07T08:19:26.4363116Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T08:19:26.4363314Z ---- --------- --------- --------- --------- --------- 2025-09-07T08:19:26.4363392Z 2025-09-07T08:19:26.4363514Z Per-Channel table columns: 2025-09-07T08:19:26.4363592Z 2025-09-07T08:19:26.4363810Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T08:19:26.4363985Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T08:19:26.4364063Z 2025-09-07T08:19:26.4364240Z Args: 2025-09-07T08:19:26.4364506Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T08:19:26.4364616Z contain this filter substring 2025-09-07T08:19:26.4364789Z Default = "", results in all the features being printed 2025-09-07T08:19:26.4365053Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T08:19:26.4365314Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T08:19:26.4365395Z 2025-09-07T08:19:26.4365484Z Example Use: 2025-09-07T08:19:26.4365630Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4365828Z >>> mod_report_visualizer.generate_table_visualization( 2025-09-07T08:19:26.4366038Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T08:19:26.4366121Z ... ) 2025-09-07T08:19:26.4366312Z >>> # prints out neatly formatted table with per_channel_min info 2025-09-07T08:19:26.4366452Z >>> # for all modules in block 1 of the model 2025-09-07T08:19:26.4366531Z 2025-09-07T08:19:26.4366798Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4366880Z 2025-09-07T08:19:26.4366978Z warnings.warn(msg) 2025-09-07T08:19:26.4367070Z 2025-09-07T08:19:26.4367266Z --- Parse Warning: 44 / 146 --- 2025-09-07T08:19:26.4368587Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=564. 2025-09-07T08:19:26.4368851Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4368930Z 2025-09-07T08:19:26.4369181Z Takes in a feature and optional module_filter and plots of the desired data. 2025-09-07T08:19:26.4369298Z 2025-09-07T08:19:26.4369781Z For per channel features, it averages the value across the channels and plots a point 2025-09-07T08:19:26.4370131Z per module. The reason for this is that for models with hundreds of channels, it can 2025-09-07T08:19:26.4370441Z be hard to differentiate one channel line from another, and so the point of generating 2025-09-07T08:19:26.4370799Z a single average point per module is to give a sense of general trends that encourage 2025-09-07T08:19:26.4370928Z further deep dives. 2025-09-07T08:19:26.4371020Z 2025-09-07T08:19:26.4371231Z Note: 2025-09-07T08:19:26.4371530Z Only features in the report that have tensor value data are plottable by this class 2025-09-07T08:19:26.4371774Z When the tensor information is plotted, it will plot: 2025-09-07T08:19:26.4371942Z idx as the x val, feature value as the y_val 2025-09-07T08:19:26.4372140Z When the channel information is plotted, it will plot: 2025-09-07T08:19:26.4372492Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-09-07T08:19:26.4372769Z The reason for this is that we want to be able to compare values across the 2025-09-07T08:19:26.4373074Z channels for same layer, and it will be hard if values are staggered by idx 2025-09-07T08:19:26.4373567Z This means each module is represented by only 1 x value 2025-09-07T08:19:26.4373722Z Args: 2025-09-07T08:19:26.4374004Z feature_filter (str): Filters the features presented to only those that 2025-09-07T08:19:26.4374198Z contain this filter substring 2025-09-07T08:19:26.4374563Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T08:19:26.4374841Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T08:19:26.4374954Z 2025-09-07T08:19:26.4375150Z Example Use: 2025-09-07T08:19:26.4375289Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4375575Z >>> mod_report_visualizer.generate_plot_visualization( 2025-09-07T08:19:26.4375814Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T08:19:26.4375925Z ... ) 2025-09-07T08:19:26.4376182Z >>> # outputs line plot of per_channel_min information for all 2025-09-07T08:19:26.4376396Z >>> # modules in block1 of model each channel gets it's own line, 2025-09-07T08:19:26.4376660Z >>> # and it's plotted across the in-order modules on the x-axis 2025-09-07T08:19:26.4376796Z 2025-09-07T08:19:26.4377161Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4377269Z 2025-09-07T08:19:26.4377400Z warnings.warn(msg) 2025-09-07T08:19:26.4377530Z 2025-09-07T08:19:26.4377815Z --- Parse Warning: 45 / 146 --- 2025-09-07T08:19:26.4379229Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=643. 2025-09-07T08:19:26.4379524Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4379637Z 2025-09-07T08:19:26.4379995Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-09-07T08:19:26.4380085Z 2025-09-07T08:19:26.4380300Z Note: 2025-09-07T08:19:26.4380634Z Only features in the report that have tensor value data can be viewed as a histogram 2025-09-07T08:19:26.4380931Z If you want to plot a histogram from all the channel values of a specific feature for 2025-09-07T08:19:26.4381280Z a specific model, make sure to specify both the model and the feature properly 2025-09-07T08:19:26.4381555Z in the filters and you should be able to see a distribution of the channel data 2025-09-07T08:19:26.4381744Z 2025-09-07T08:19:26.4381877Z Args: 2025-09-07T08:19:26.4382170Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T08:19:26.4382352Z contain this filter substring 2025-09-07T08:19:26.4382548Z Default = "", results in all the features being printed 2025-09-07T08:19:26.4382868Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T08:19:26.4383179Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T08:19:26.4383493Z num_bins (int, optional): The number of bins to create the histogram with 2025-09-07T08:19:26.4383709Z Default = 10, the values will be split into 10 equal sized bins 2025-09-07T08:19:26.4383820Z 2025-09-07T08:19:26.4383987Z Example Use: 2025-09-07T08:19:26.4384096Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4384516Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-09-07T08:19:26.4384739Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T08:19:26.4384852Z ... ) 2025-09-07T08:19:26.4385240Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-09-07T08:19:26.4385530Z information is gathered across all channels for all modules in block 1 for the 2025-09-07T08:19:26.4385861Z per_channel_min and is displayed in a histogram of equally sized bins 2025-09-07T08:19:26.4385991Z 2025-09-07T08:19:26.4386273Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4386462Z 2025-09-07T08:19:26.4386590Z warnings.warn(msg) 2025-09-07T08:19:26.4386721Z 2025-09-07T08:19:26.4386984Z --- Parse Warning: 46 / 146 --- 2025-09-07T08:19:26.4387943Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py line=734. 2025-09-07T08:19:26.4388290Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4388712Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-09-07T08:19:26.4388972Z Label will only appear if CPU activity tracing is enabled. 2025-09-07T08:19:26.4389062Z 2025-09-07T08:19:26.4389316Z It is useful when tracing the code profile. 2025-09-07T08:19:26.4389464Z 2025-09-07T08:19:26.4389577Z Args: 2025-09-07T08:19:26.4389792Z name (str): Label assigned to the block of code. 2025-09-07T08:19:26.4390004Z node_id (int): ID of node, for distributed profiling. Unset in 2025-09-07T08:19:26.4390189Z non-distributed cases. 2025-09-07T08:19:26.4390314Z 2025-09-07T08:19:26.4390437Z Example: 2025-09-07T08:19:26.4390691Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-09-07T08:19:26.4390854Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-09-07T08:19:26.4391046Z >>> with torch.autograd.profiler.profile() as prof: 2025-09-07T08:19:26.4391221Z ... y = x**2 2025-09-07T08:19:26.4391437Z ... with torch.autograd.profiler.record_function( 2025-09-07T08:19:26.4391634Z ... "label-z" 2025-09-07T08:19:26.4391794Z ... ): # label the block 2025-09-07T08:19:26.4391918Z ... z = y**3 2025-09-07T08:19:26.4392063Z ... y.backward() 2025-09-07T08:19:26.4392227Z >>> # xdoctest: +IGNORE_WANT 2025-09-07T08:19:26.4392461Z >>> # NOTE: some columns were removed for brevity 2025-09-07T08:19:26.4392695Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-09-07T08:19:26.4392967Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4393181Z Name Self CPU total % CPU time avg Number of Calls 2025-09-07T08:19:26.4393385Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4393650Z pow 60.77% 47.470us 3 2025-09-07T08:19:26.4393812Z mul 21.73% 25.465us 2 2025-09-07T08:19:26.4394058Z PowBackward0 12.03% 121.891us 1 2025-09-07T08:19:26.4394311Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-09-07T08:19:26.4394483Z label-z 2.13% 12.421us 1 2025-09-07T08:19:26.4394783Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-09-07T08:19:26.4395021Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4395207Z Self CPU time total: 234.344us 2025-09-07T08:19:26.4395339Z CUDA time total: 0.000us 2025-09-07T08:19:26.4395495Z 2025-09-07T08:19:26.4395593Z 2025-09-07T08:19:26.4395939Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4396162Z 2025-09-07T08:19:26.4396290Z warnings.warn(msg) 2025-09-07T08:19:26.4396453Z 2025-09-07T08:19:26.4396685Z --- Parse Warning: 47 / 146 --- 2025-09-07T08:19:26.4397668Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=701. 2025-09-07T08:19:26.4398044Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.4398156Z 2025-09-07T08:19:26.4398497Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-09-07T08:19:26.4398802Z The submesh created consists of the dimensions and the communicators indicated by 2025-09-07T08:19:26.4398924Z ``mesh_dim_names`` 2025-09-07T08:19:26.4399099Z 2025-09-07T08:19:26.4399227Z Args: 2025-09-07T08:19:26.4399534Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-09-07T08:19:26.4399760Z mesh dimension of the DeviceMesh to create the submesh for. 2025-09-07T08:19:26.4399906Z Returns: 2025-09-07T08:19:26.4400064Z A :class:`DeviceMesh` object 2025-09-07T08:19:26.4400210Z 2025-09-07T08:19:26.4400585Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-09-07T08:19:26.4400720Z In the first example: 2025-09-07T08:19:26.4401005Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-09-07T08:19:26.4401326Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-09-07T08:19:26.4401594Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-09-07T08:19:26.4401950Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-09-07T08:19:26.4402202Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-09-07T08:19:26.4402540Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-09-07T08:19:26.4402656Z 2025-09-07T08:19:26.4402788Z In the second example: 2025-09-07T08:19:26.4403185Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-09-07T08:19:26.4403499Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-09-07T08:19:26.4403842Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-09-07T08:19:26.4404225Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-09-07T08:19:26.4404339Z 2025-09-07T08:19:26.4404465Z Example:: 2025-09-07T08:19:26.4404666Z 2025-09-07T08:19:26.4404880Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T08:19:26.4405086Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-09-07T08:19:26.4405198Z >>> 2025-09-07T08:19:26.4405479Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-09-07T08:19:26.4405635Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-09-07T08:19:26.4406016Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T08:19:26.4406146Z >>> tp_mesh = mesh_2d["tp"] 2025-09-07T08:19:26.4406275Z >>> dp_mesh = mesh_2d["dp"] 2025-09-07T08:19:26.4406431Z >>> 2025-09-07T08:19:26.4406585Z >>> # Initialize a 3D mesh. 2025-09-07T08:19:26.4406945Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-09-07T08:19:26.4407341Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-09-07T08:19:26.4407487Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-09-07T08:19:26.4407706Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-09-07T08:19:26.4407814Z 2025-09-07T08:19:26.4408563Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2025-09-07T08:19:26.4408705Z 2025-09-07T08:19:26.4409039Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T08:19:26.4409188Z ^ 2025-09-07T08:19:26.4409315Z warnings.warn(msg) 2025-09-07T08:19:26.4409478Z 2025-09-07T08:19:26.4409706Z --- Parse Warning: 48 / 146 --- 2025-09-07T08:19:26.4410812Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2705. 2025-09-07T08:19:26.4411111Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4411250Z 2025-09-07T08:19:26.4411582Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-09-07T08:19:26.4411690Z 2025-09-07T08:19:26.4412027Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-09-07T08:19:26.4412271Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-09-07T08:19:26.4412380Z 2025-09-07T08:19:26.4412558Z Args: 2025-09-07T08:19:26.4412825Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-09-07T08:19:26.4413107Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-09-07T08:19:26.4413386Z matters and it needs to match with corresponding isend/irecv on the 2025-09-07T08:19:26.4413527Z remote end. 2025-09-07T08:19:26.4413685Z 2025-09-07T08:19:26.4413809Z Returns: 2025-09-07T08:19:26.4414157Z A list of distributed request objects returned by calling the corresponding 2025-09-07T08:19:26.4414268Z op in the op_list. 2025-09-07T08:19:26.4414417Z 2025-09-07T08:19:26.4414591Z Examples: 2025-09-07T08:19:26.4414740Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T08:19:26.4415004Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-09-07T08:19:26.4415186Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-09-07T08:19:26.4415410Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-09-07T08:19:26.4415638Z >>> recv_op = dist.P2POp( 2025-09-07T08:19:26.4415859Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-09-07T08:19:26.4416022Z ... ) 2025-09-07T08:19:26.4416194Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-09-07T08:19:26.4416322Z >>> for req in reqs: 2025-09-07T08:19:26.4416516Z >>> req.wait() 2025-09-07T08:19:26.4416651Z >>> recv_tensor 2025-09-07T08:19:26.4416861Z tensor([2, 3]) # Rank 0 2025-09-07T08:19:26.4416988Z tensor([0, 1]) # Rank 1 2025-09-07T08:19:26.4417098Z 2025-09-07T08:19:26.4417399Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-09-07T08:19:26.4417684Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-09-07T08:19:26.4417893Z lead to unexpected hang issues. 2025-09-07T08:19:26.4418002Z 2025-09-07T08:19:26.4418244Z In addition, if this API is the first collective call in the ``group`` 2025-09-07T08:19:26.4418537Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-09-07T08:19:26.4418770Z this API call; otherwise, the behavior is undefined. If this API call is 2025-09-07T08:19:26.4419146Z not the first collective call in the ``group``, batched P2P operations 2025-09-07T08:19:26.4419396Z involving only a subset of ranks of the ``group`` are allowed. 2025-09-07T08:19:26.4419550Z 2025-09-07T08:19:26.4419832Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4419941Z 2025-09-07T08:19:26.4420130Z warnings.warn(msg) 2025-09-07T08:19:26.4420254Z 2025-09-07T08:19:26.4420487Z --- Parse Warning: 49 / 146 --- 2025-09-07T08:19:26.4421510Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2837. 2025-09-07T08:19:26.4421803Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4421936Z 2025-09-07T08:19:26.4422275Z Reduces the tensor data across all machines in a way that all get the final result. 2025-09-07T08:19:26.4422484Z 2025-09-07T08:19:26.4422741Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-09-07T08:19:26.4422850Z 2025-09-07T08:19:26.4423071Z Complex tensors are supported. 2025-09-07T08:19:26.4423160Z 2025-09-07T08:19:26.4423363Z Args: 2025-09-07T08:19:26.4423598Z tensor (Tensor): Input and output of the collective. The function 2025-09-07T08:19:26.4423730Z operates in-place. 2025-09-07T08:19:26.4423922Z op (optional): One of the values from 2025-09-07T08:19:26.4424078Z ``torch.distributed.ReduceOp`` 2025-09-07T08:19:26.4424370Z enum. Specifies an operation used for element-wise reductions. 2025-09-07T08:19:26.4424645Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:26.4424805Z the default process group will be used. 2025-09-07T08:19:26.4425071Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T08:19:26.4425191Z 2025-09-07T08:19:26.4425327Z Returns: 2025-09-07T08:19:26.4425533Z Async work handle, if async_op is set to True. 2025-09-07T08:19:26.4425755Z None, if not async_op or if not part of the group 2025-09-07T08:19:26.4425909Z 2025-09-07T08:19:26.4426033Z Examples: 2025-09-07T08:19:26.4426213Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T08:19:26.4426358Z >>> # All tensors below are of torch.int64 type. 2025-09-07T08:19:26.4426577Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:26.4426787Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T08:19:26.4427042Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T08:19:26.4427211Z >>> tensor 2025-09-07T08:19:26.4427358Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T08:19:26.4427479Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T08:19:26.4427742Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T08:19:26.4427860Z >>> tensor 2025-09-07T08:19:26.4428052Z tensor([4, 6], device='cuda:0') # Rank 0 2025-09-07T08:19:26.4428197Z tensor([4, 6], device='cuda:1') # Rank 1 2025-09-07T08:19:26.4428308Z 2025-09-07T08:19:26.4428543Z >>> # All tensors below are of torch.cfloat type. 2025-09-07T08:19:26.4428705Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:26.4428889Z >>> tensor = torch.tensor( 2025-09-07T08:19:26.4429065Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T08:19:26.4429191Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T08:19:26.4429325Z >>> tensor 2025-09-07T08:19:26.4429524Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T08:19:26.4429757Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T08:19:26.4429919Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T08:19:26.4430082Z >>> tensor 2025-09-07T08:19:26.4430292Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-09-07T08:19:26.4430504Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-09-07T08:19:26.4430715Z 2025-09-07T08:19:26.4430828Z 2025-09-07T08:19:26.4431115Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4431268Z 2025-09-07T08:19:26.4431395Z warnings.warn(msg) 2025-09-07T08:19:26.4431486Z 2025-09-07T08:19:26.4431814Z --- Parse Warning: 50 / 146 --- 2025-09-07T08:19:26.4432809Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3201. 2025-09-07T08:19:26.4433147Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4433256Z 2025-09-07T08:19:26.4433538Z Gathers picklable objects from the whole group in a single process. 2025-09-07T08:19:26.4433687Z 2025-09-07T08:19:26.4433967Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-09-07T08:19:26.4434190Z object must be picklable in order to be gathered. 2025-09-07T08:19:26.4434329Z 2025-09-07T08:19:26.4434483Z Args: 2025-09-07T08:19:26.4434632Z obj (Any): Input object. Must be picklable. 2025-09-07T08:19:26.4434902Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-09-07T08:19:26.4435178Z should be correctly sized as the size of the group for this 2025-09-07T08:19:26.4435423Z collective and will contain the output. Must be ``None`` on non-dst 2025-09-07T08:19:26.4435626Z ranks. (default is ``None``) 2025-09-07T08:19:26.4435989Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-09-07T08:19:26.4436196Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-09-07T08:19:26.4436546Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:26.4436796Z the default process group will be used. Default is ``None``. 2025-09-07T08:19:26.4437221Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-09-07T08:19:26.4437343Z 2025-09-07T08:19:26.4437459Z Returns: 2025-09-07T08:19:26.4437729Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-09-07T08:19:26.4437880Z output of the collective. 2025-09-07T08:19:26.4438032Z 2025-09-07T08:19:26.4438278Z .. note:: Note that this API differs slightly from the gather collective 2025-09-07T08:19:26.4438533Z since it does not provide an async_op handle and thus will be a blocking 2025-09-07T08:19:26.4438668Z call. 2025-09-07T08:19:26.4438818Z 2025-09-07T08:19:26.4439142Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-09-07T08:19:26.4439385Z of objects must be moved to the GPU device before communication takes 2025-09-07T08:19:26.4439615Z place. In this case, the device used is given by 2025-09-07T08:19:26.4439866Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-09-07T08:19:26.4440081Z ensure that this is set so that each rank has an individual GPU, via 2025-09-07T08:19:26.4440310Z ``torch.cuda.set_device()``. 2025-09-07T08:19:26.4440418Z 2025-09-07T08:19:26.4440610Z .. warning:: 2025-09-07T08:19:26.4440879Z Object collectives have a number of serious performance and scalability 2025-09-07T08:19:26.4441085Z limitations. See :ref:`object_collectives` for details. 2025-09-07T08:19:26.4441246Z 2025-09-07T08:19:26.4441378Z .. warning:: 2025-09-07T08:19:26.4441656Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-09-07T08:19:26.4441933Z known to be insecure. It is possible to construct malicious pickle data 2025-09-07T08:19:26.4442206Z which will execute arbitrary code during unpickling. Only call this 2025-09-07T08:19:26.4442367Z function with data you trust. 2025-09-07T08:19:26.4450663Z 2025-09-07T08:19:26.4450832Z .. warning:: 2025-09-07T08:19:26.4451066Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-09-07T08:19:26.4451292Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-09-07T08:19:26.4451478Z pickled. Please consider using :func:`gather` instead. 2025-09-07T08:19:26.4451561Z 2025-09-07T08:19:26.4451658Z Example:: 2025-09-07T08:19:26.4451793Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T08:19:26.4451969Z >>> # Note: Process group initialization omitted on each rank. 2025-09-07T08:19:26.4452097Z >>> import torch.distributed as dist 2025-09-07T08:19:26.4452207Z >>> # Assumes world_size of 3. 2025-09-07T08:19:26.4452392Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-09-07T08:19:26.4452510Z >>> output = [None for _ in gather_objects] 2025-09-07T08:19:26.4452606Z >>> dist.gather_object( 2025-09-07T08:19:26.4452830Z ... gather_objects[dist.get_rank()], 2025-09-07T08:19:26.4452963Z ... output if dist.get_rank() == 0 else None, 2025-09-07T08:19:26.4453046Z ... dst=0 2025-09-07T08:19:26.4453139Z ... ) 2025-09-07T08:19:26.4453220Z >>> # On rank 0 2025-09-07T08:19:26.4453311Z >>> output 2025-09-07T08:19:26.4453398Z ['foo', 12, {1: 2}] 2025-09-07T08:19:26.4453473Z 2025-09-07T08:19:26.4453735Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4453809Z 2025-09-07T08:19:26.4453915Z warnings.warn(msg) 2025-09-07T08:19:26.4453993Z 2025-09-07T08:19:26.4454232Z --- Parse Warning: 51 / 146 --- 2025-09-07T08:19:26.4455217Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3849. 2025-09-07T08:19:26.4455483Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4455570Z 2025-09-07T08:19:26.4455709Z Gathers tensors from the whole group in a list. 2025-09-07T08:19:26.4455782Z 2025-09-07T08:19:26.4455932Z Complex and uneven sized tensors are supported. 2025-09-07T08:19:26.4456006Z 2025-09-07T08:19:26.4456094Z Args: 2025-09-07T08:19:26.4456270Z tensor_list (list[Tensor]): Output list. It should contain 2025-09-07T08:19:26.4456483Z correctly-sized tensors to be used for output of the collective. 2025-09-07T08:19:26.4456617Z Uneven sized tensors are supported. 2025-09-07T08:19:26.4456806Z tensor (Tensor): Tensor to be broadcast from current process. 2025-09-07T08:19:26.4457044Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:26.4457168Z the default process group will be used. 2025-09-07T08:19:26.4457359Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T08:19:26.4457448Z 2025-09-07T08:19:26.4457529Z Returns: 2025-09-07T08:19:26.4457672Z Async work handle, if async_op is set to True. 2025-09-07T08:19:26.4457812Z None, if not async_op or if not part of the group 2025-09-07T08:19:26.4457884Z 2025-09-07T08:19:26.4457979Z Examples: 2025-09-07T08:19:26.4458113Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T08:19:26.4458244Z >>> # All tensors below are of torch.int64 dtype. 2025-09-07T08:19:26.4458371Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:26.4458486Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T08:19:26.4458616Z >>> tensor_list = [ 2025-09-07T08:19:26.4458853Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-09-07T08:19:26.4458927Z ... ] 2025-09-07T08:19:26.4459029Z >>> tensor_list 2025-09-07T08:19:26.4459227Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-09-07T08:19:26.4459431Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-09-07T08:19:26.4459654Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T08:19:26.4459734Z >>> tensor 2025-09-07T08:19:26.4459858Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T08:19:26.4459970Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T08:19:26.4460095Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T08:19:26.4460183Z >>> tensor_list 2025-09-07T08:19:26.4460372Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-09-07T08:19:26.4460572Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-09-07T08:19:26.4460652Z 2025-09-07T08:19:26.4460801Z >>> # All tensors below are of torch.cfloat dtype. 2025-09-07T08:19:26.4460960Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:26.4461049Z >>> tensor_list = [ 2025-09-07T08:19:26.4461267Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-09-07T08:19:26.4461349Z ... ] 2025-09-07T08:19:26.4461442Z >>> tensor_list 2025-09-07T08:19:26.4461686Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-09-07T08:19:26.4461919Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-09-07T08:19:26.4462031Z >>> tensor = torch.tensor( 2025-09-07T08:19:26.4462175Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T08:19:26.4462280Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T08:19:26.4462367Z >>> tensor 2025-09-07T08:19:26.4462503Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T08:19:26.4462674Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T08:19:26.4462793Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T08:19:26.4462886Z >>> tensor_list 2025-09-07T08:19:26.4463125Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-09-07T08:19:26.4463357Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-09-07T08:19:26.4463445Z 2025-09-07T08:19:26.4463521Z 2025-09-07T08:19:26.4463784Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4463862Z 2025-09-07T08:19:26.4463953Z warnings.warn(msg) 2025-09-07T08:19:26.4464041Z 2025-09-07T08:19:26.4464236Z --- Parse Warning: 52 / 146 --- 2025-09-07T08:19:26.4465202Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4555. 2025-09-07T08:19:26.4465468Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4465544Z 2025-09-07T08:19:26.4465808Z Split input tensor and then scatter the split list to all processes in a group. 2025-09-07T08:19:26.4465884Z 2025-09-07T08:19:26.4466151Z Later the received tensors are concatenated from all the processes in the group 2025-09-07T08:19:26.4466272Z and returned as a single output tensor. 2025-09-07T08:19:26.4466344Z 2025-09-07T08:19:26.4466463Z Complex tensors are supported. 2025-09-07T08:19:26.4466541Z 2025-09-07T08:19:26.4466629Z Args: 2025-09-07T08:19:26.4466819Z output (Tensor): Gathered concatenated output tensor. 2025-09-07T08:19:26.4466940Z input (Tensor): Input tensor to scatter. 2025-09-07T08:19:26.4467193Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-09-07T08:19:26.4467398Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-09-07T08:19:26.4467512Z equally by ``world_size``. 2025-09-07T08:19:26.4467721Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-09-07T08:19:26.4467916Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-09-07T08:19:26.4468028Z equally by ``world_size``. 2025-09-07T08:19:26.4468255Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:26.4468387Z the default process group will be used. 2025-09-07T08:19:26.4468580Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T08:19:26.4468655Z 2025-09-07T08:19:26.4468748Z Returns: 2025-09-07T08:19:26.4468886Z Async work handle, if async_op is set to True. 2025-09-07T08:19:26.4469042Z None, if not async_op or if not part of the group. 2025-09-07T08:19:26.4469120Z 2025-09-07T08:19:26.4469205Z .. warning:: 2025-09-07T08:19:26.4469410Z `all_to_all_single` is experimental and subject to change. 2025-09-07T08:19:26.4469488Z 2025-09-07T08:19:26.4469568Z Examples: 2025-09-07T08:19:26.4469694Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T08:19:26.4469804Z >>> input = torch.arange(4) + rank * 4 2025-09-07T08:19:26.4469896Z >>> input 2025-09-07T08:19:26.4469994Z tensor([0, 1, 2, 3]) # Rank 0 2025-09-07T08:19:26.4470087Z tensor([4, 5, 6, 7]) # Rank 1 2025-09-07T08:19:26.4470191Z tensor([8, 9, 10, 11]) # Rank 2 2025-09-07T08:19:26.4470285Z tensor([12, 13, 14, 15]) # Rank 3 2025-09-07T08:19:26.4470438Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T08:19:26.4470563Z >>> dist.all_to_all_single(output, input) 2025-09-07T08:19:26.4470640Z >>> output 2025-09-07T08:19:26.4470750Z tensor([0, 4, 8, 12]) # Rank 0 2025-09-07T08:19:26.4470868Z tensor([1, 5, 9, 13]) # Rank 1 2025-09-07T08:19:26.4470976Z tensor([2, 6, 10, 14]) # Rank 2 2025-09-07T08:19:26.4471071Z tensor([3, 7, 11, 15]) # Rank 3 2025-09-07T08:19:26.4471144Z 2025-09-07T08:19:26.4471314Z >>> # Essentially, it is similar to following operation: 2025-09-07T08:19:26.4471449Z >>> scatter_list = list(input.chunk(world_size)) 2025-09-07T08:19:26.4471593Z >>> gather_list = list(output.chunk(world_size)) 2025-09-07T08:19:26.4471696Z >>> for i in range(world_size): 2025-09-07T08:19:26.4471921Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-09-07T08:19:26.4472009Z 2025-09-07T08:19:26.4472122Z >>> # Another example with uneven split 2025-09-07T08:19:26.4472216Z >>> input 2025-09-07T08:19:26.4472368Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T08:19:26.4472523Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T08:19:26.4472686Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T08:19:26.4472841Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T08:19:26.4472941Z >>> input_splits 2025-09-07T08:19:26.4473059Z [2, 2, 1, 1] # Rank 0 2025-09-07T08:19:26.4473172Z [3, 2, 2, 2] # Rank 1 2025-09-07T08:19:26.4473481Z [2, 1, 1, 1] # Rank 2 2025-09-07T08:19:26.4473596Z [2, 2, 2, 1] # Rank 3 2025-09-07T08:19:26.4473701Z >>> output_splits 2025-09-07T08:19:26.4473887Z [2, 3, 2, 2] # Rank 0 2025-09-07T08:19:26.4474034Z [2, 2, 1, 2] # Rank 1 2025-09-07T08:19:26.4474164Z [1, 2, 1, 2] # Rank 2 2025-09-07T08:19:26.4474280Z [1, 2, 1, 1] # Rank 3 2025-09-07T08:19:26.4474383Z >>> output = ... 2025-09-07T08:19:26.4474587Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-09-07T08:19:26.4474668Z >>> output 2025-09-07T08:19:26.4474836Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-09-07T08:19:26.4474985Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-09-07T08:19:26.4475149Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-09-07T08:19:26.4475302Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-09-07T08:19:26.4475384Z 2025-09-07T08:19:26.4475473Z 2025-09-07T08:19:26.4475630Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T08:19:26.4475741Z >>> input = torch.tensor( 2025-09-07T08:19:26.4475906Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T08:19:26.4475996Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T08:19:26.4476089Z >>> input 2025-09-07T08:19:26.4476258Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-09-07T08:19:26.4476431Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-09-07T08:19:26.4476607Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-09-07T08:19:26.4476783Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-09-07T08:19:26.4476931Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T08:19:26.4477054Z >>> dist.all_to_all_single(output, input) 2025-09-07T08:19:26.4477142Z >>> output 2025-09-07T08:19:26.4477312Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-09-07T08:19:26.4477511Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-09-07T08:19:26.4477697Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-09-07T08:19:26.4477861Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-09-07T08:19:26.4477946Z 2025-09-07T08:19:26.4478194Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4478265Z 2025-09-07T08:19:26.4478368Z warnings.warn(msg) 2025-09-07T08:19:26.4478443Z 2025-09-07T08:19:26.4478665Z --- Parse Warning: 53 / 146 --- 2025-09-07T08:19:26.4479595Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4697. 2025-09-07T08:19:26.4479858Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4479947Z 2025-09-07T08:19:26.4480311Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-09-07T08:19:26.4480400Z 2025-09-07T08:19:26.4480506Z Complex tensors are supported. 2025-09-07T08:19:26.4480580Z 2025-09-07T08:19:26.4480669Z Args: 2025-09-07T08:19:26.4480885Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-09-07T08:19:26.4480975Z per rank. 2025-09-07T08:19:26.4481201Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-09-07T08:19:26.4481427Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:26.4481591Z the default process group will be used. 2025-09-07T08:19:26.4481811Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T08:19:26.4481897Z 2025-09-07T08:19:26.4481978Z Returns: 2025-09-07T08:19:26.4482116Z Async work handle, if async_op is set to True. 2025-09-07T08:19:26.4482272Z None, if not async_op or if not part of the group. 2025-09-07T08:19:26.4482350Z 2025-09-07T08:19:26.4482452Z .. warning:: 2025-09-07T08:19:26.4482601Z `all_to_all` is experimental and subject to change. 2025-09-07T08:19:26.4482671Z 2025-09-07T08:19:26.4482766Z Examples: 2025-09-07T08:19:26.4482883Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T08:19:26.4483006Z >>> input = torch.arange(4) + rank * 4 2025-09-07T08:19:26.4483108Z >>> input = list(input.chunk(4)) 2025-09-07T08:19:26.4483186Z >>> input 2025-09-07T08:19:26.4483360Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-09-07T08:19:26.4483522Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-09-07T08:19:26.4483691Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-09-07T08:19:26.4483856Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-09-07T08:19:26.4484059Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T08:19:26.4484267Z >>> dist.all_to_all(output, input) 2025-09-07T08:19:26.4484350Z >>> output 2025-09-07T08:19:26.4484525Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-09-07T08:19:26.4484685Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-09-07T08:19:26.4484841Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-09-07T08:19:26.4485010Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-09-07T08:19:26.4485085Z 2025-09-07T08:19:26.4485254Z >>> # Essentially, it is similar to following operation: 2025-09-07T08:19:26.4485352Z >>> scatter_list = input 2025-09-07T08:19:26.4485443Z >>> gather_list = output 2025-09-07T08:19:26.4485557Z >>> for i in range(world_size): 2025-09-07T08:19:26.4485805Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-09-07T08:19:26.4485881Z 2025-09-07T08:19:26.4485972Z >>> input 2025-09-07T08:19:26.4486119Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T08:19:26.4486282Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T08:19:26.4486426Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T08:19:26.4486586Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T08:19:26.4486674Z >>> input_splits 2025-09-07T08:19:26.4486792Z [2, 2, 1, 1] # Rank 0 2025-09-07T08:19:26.4486922Z [3, 2, 2, 2] # Rank 1 2025-09-07T08:19:26.4487036Z [2, 1, 1, 1] # Rank 2 2025-09-07T08:19:26.4487166Z [2, 2, 2, 1] # Rank 3 2025-09-07T08:19:26.4487257Z >>> output_splits 2025-09-07T08:19:26.4487372Z [2, 3, 2, 2] # Rank 0 2025-09-07T08:19:26.4487500Z [2, 2, 1, 2] # Rank 1 2025-09-07T08:19:26.4487611Z [1, 2, 1, 2] # Rank 2 2025-09-07T08:19:26.4487734Z [1, 2, 1, 1] # Rank 3 2025-09-07T08:19:26.4487860Z >>> input = list(input.split(input_splits)) 2025-09-07T08:19:26.4487936Z >>> input 2025-09-07T08:19:26.4488174Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-09-07T08:19:26.4488405Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-09-07T08:19:26.4488617Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-09-07T08:19:26.4488816Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-09-07T08:19:26.4488907Z >>> output = ... 2025-09-07T08:19:26.4489030Z >>> dist.all_to_all(output, input) 2025-09-07T08:19:26.4489110Z >>> output 2025-09-07T08:19:26.4489317Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-09-07T08:19:26.4489514Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-09-07T08:19:26.4489707Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-09-07T08:19:26.4489914Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-09-07T08:19:26.4489990Z 2025-09-07T08:19:26.4490161Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T08:19:26.4490261Z >>> input = torch.tensor( 2025-09-07T08:19:26.4490428Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T08:19:26.4490526Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T08:19:26.4490631Z >>> input = list(input.chunk(4)) 2025-09-07T08:19:26.4490709Z >>> input 2025-09-07T08:19:26.4490928Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-09-07T08:19:26.4491133Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-09-07T08:19:26.4491358Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-09-07T08:19:26.4491576Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-09-07T08:19:26.4491755Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T08:19:26.4491878Z >>> dist.all_to_all(output, input) 2025-09-07T08:19:26.4491986Z >>> output 2025-09-07T08:19:26.4492199Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-09-07T08:19:26.4492407Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-09-07T08:19:26.4492609Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-09-07T08:19:26.4492822Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-09-07T08:19:26.4492896Z 2025-09-07T08:19:26.4492980Z 2025-09-07T08:19:26.4493234Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4493309Z 2025-09-07T08:19:26.4493410Z warnings.warn(msg) 2025-09-07T08:19:26.4493488Z 2025-09-07T08:19:26.4493691Z --- Parse Warning: 54 / 146 --- 2025-09-07T08:19:26.4494554Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-09-07T08:19:26.4494816Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4494900Z 2025-09-07T08:19:26.4495015Z Module ``torch.distributed.launch``. 2025-09-07T08:19:26.4495097Z 2025-09-07T08:19:26.4495344Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-09-07T08:19:26.4495496Z training processes on each of the training nodes. 2025-09-07T08:19:26.4495579Z 2025-09-07T08:19:26.4495664Z .. warning:: 2025-09-07T08:19:26.4495739Z 2025-09-07T08:19:26.4495999Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-09-07T08:19:26.4496108Z 2025-09-07T08:19:26.4496356Z The utility can be used for single-node distributed training, in which one or 2025-09-07T08:19:26.4496616Z more processes per node will be spawned. The utility can be used for either 2025-09-07T08:19:26.4496834Z CPU training or GPU training. If the utility is used for GPU training, 2025-09-07T08:19:26.4497086Z each distributed process will be operating on a single GPU. This can achieve 2025-09-07T08:19:26.4497315Z well-improved single-node training performance. It can also be used in 2025-09-07T08:19:26.4497590Z multi-node distributed training, by spawning up multiple processes on each node 2025-09-07T08:19:26.4497819Z for well-improved multi-node distributed training performance as well. 2025-09-07T08:19:26.4498054Z This will especially be beneficial for systems with multiple Infiniband 2025-09-07T08:19:26.4498303Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-09-07T08:19:26.4498417Z aggregated communication bandwidth. 2025-09-07T08:19:26.4498499Z 2025-09-07T08:19:26.4498729Z In both cases of single-node distributed training or multi-node distributed 2025-09-07T08:19:26.4498974Z training, this utility will launch the given number of processes per node 2025-09-07T08:19:26.4499222Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-09-07T08:19:26.4499438Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-09-07T08:19:26.4499644Z and each process will be operating on a single GPU from *GPU 0 to 2025-09-07T08:19:26.4499748Z GPU (nproc_per_node - 1)*. 2025-09-07T08:19:26.4499832Z 2025-09-07T08:19:26.4499928Z **How to use this module:** 2025-09-07T08:19:26.4500004Z 2025-09-07T08:19:26.4500162Z 1. Single-Node multi-process distributed training 2025-09-07T08:19:26.4500237Z 2025-09-07T08:19:26.4500318Z :: 2025-09-07T08:19:26.4500405Z 2025-09-07T08:19:26.4500640Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T08:19:26.4500834Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-09-07T08:19:26.4500982Z arguments of your training script) 2025-09-07T08:19:26.4501062Z 2025-09-07T08:19:26.4501275Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-09-07T08:19:26.4501352Z 2025-09-07T08:19:26.4501433Z 2025-09-07T08:19:26.4501576Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-09-07T08:19:26.4501652Z 2025-09-07T08:19:26.4501739Z :: 2025-09-07T08:19:26.4501816Z 2025-09-07T08:19:26.4502054Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T08:19:26.4502215Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-09-07T08:19:26.4502421Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T08:19:26.4502574Z and all other arguments of your training script) 2025-09-07T08:19:26.4502650Z 2025-09-07T08:19:26.4502736Z Node 2: 2025-09-07T08:19:26.4502808Z 2025-09-07T08:19:26.4502889Z :: 2025-09-07T08:19:26.4502972Z 2025-09-07T08:19:26.4503207Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T08:19:26.4503360Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-09-07T08:19:26.4503571Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T08:19:26.4503718Z and all other arguments of your training script) 2025-09-07T08:19:26.4503799Z 2025-09-07T08:19:26.4503958Z 3. To look up what optional arguments this module offers: 2025-09-07T08:19:26.4504036Z 2025-09-07T08:19:26.4504121Z :: 2025-09-07T08:19:26.4504197Z 2025-09-07T08:19:26.4504343Z python -m torch.distributed.launch --help 2025-09-07T08:19:26.4504417Z 2025-09-07T08:19:26.4504492Z 2025-09-07T08:19:26.4504623Z **Important Notices:** 2025-09-07T08:19:26.4504723Z 2025-09-07T08:19:26.4504918Z 1. This utility and multi-process distributed (single-node or 2025-09-07T08:19:26.4505168Z multi-node) GPU training currently only achieves the best performance using 2025-09-07T08:19:26.4505420Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-09-07T08:19:26.4505523Z use for GPU training. 2025-09-07T08:19:26.4505600Z 2025-09-07T08:19:26.4505814Z 2. In your training program, you must parse the command-line argument: 2025-09-07T08:19:26.4506056Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-09-07T08:19:26.4506278Z If your training program uses GPUs, you should ensure that your code only 2025-09-07T08:19:26.4506473Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-09-07T08:19:26.4506550Z 2025-09-07T08:19:26.4506651Z Parsing the local_rank argument 2025-09-07T08:19:26.4506736Z 2025-09-07T08:19:26.4506810Z :: 2025-09-07T08:19:26.4506896Z 2025-09-07T08:19:26.4506988Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4507075Z >>> import argparse 2025-09-07T08:19:26.4507207Z >>> parser = argparse.ArgumentParser() 2025-09-07T08:19:26.4507423Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-09-07T08:19:26.4507527Z >>> args = parser.parse_args() 2025-09-07T08:19:26.4507610Z 2025-09-07T08:19:26.4507728Z Set your device to local rank using either 2025-09-07T08:19:26.4507812Z 2025-09-07T08:19:26.4507889Z :: 2025-09-07T08:19:26.4507962Z 2025-09-07T08:19:26.4508167Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-09-07T08:19:26.4508241Z 2025-09-07T08:19:26.4508326Z or 2025-09-07T08:19:26.4508400Z 2025-09-07T08:19:26.4508474Z :: 2025-09-07T08:19:26.4508560Z 2025-09-07T08:19:26.4508686Z >>> with torch.cuda.device(args.local_rank): 2025-09-07T08:19:26.4508777Z >>> # your code to run 2025-09-07T08:19:26.4508865Z >>> ... 2025-09-07T08:19:26.4508938Z 2025-09-07T08:19:26.4509045Z .. versionchanged:: 2.0.0 2025-09-07T08:19:26.4509143Z 2025-09-07T08:19:26.4509387Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-09-07T08:19:26.4509628Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-09-07T08:19:26.4509764Z previously used underscored ``--local_rank``. 2025-09-07T08:19:26.4509845Z 2025-09-07T08:19:26.4510073Z For backward compatibility, it may be necessary for users to handle both 2025-09-07T08:19:26.4510339Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-09-07T08:19:26.4510555Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-09-07T08:19:26.4510804Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-09-07T08:19:26.4511036Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-09-07T08:19:26.4511180Z including ``"--local-rank"`` should be sufficient. 2025-09-07T08:19:26.4511258Z 2025-09-07T08:19:26.4511496Z 3. In your training program, you are supposed to call the following function 2025-09-07T08:19:26.4511732Z at the beginning to start the distributed backend. It is strongly recommended 2025-09-07T08:19:26.4511950Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-09-07T08:19:26.4512138Z but ``env://`` is the one that is officially supported by this module. 2025-09-07T08:19:26.4512211Z 2025-09-07T08:19:26.4512295Z :: 2025-09-07T08:19:26.4512368Z 2025-09-07T08:19:26.4512571Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-09-07T08:19:26.4512703Z >>> init_method='env://') 2025-09-07T08:19:26.4512777Z 2025-09-07T08:19:26.4513046Z 4. In your training program, you can either use regular distributed functions 2025-09-07T08:19:26.4513301Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-09-07T08:19:26.4513516Z training program uses GPUs for training and you would like to use 2025-09-07T08:19:26.4513700Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-09-07T08:19:26.4513802Z here is how to configure it. 2025-09-07T08:19:26.4513883Z 2025-09-07T08:19:26.4513957Z :: 2025-09-07T08:19:26.4514042Z 2025-09-07T08:19:26.4514230Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-09-07T08:19:26.4514365Z >>> device_ids=[args.local_rank], 2025-09-07T08:19:26.4514508Z >>> output_device=args.local_rank) 2025-09-07T08:19:26.4514580Z 2025-09-07T08:19:26.4514826Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-09-07T08:19:26.4515055Z that your code will be operating on. This is generally the local rank of the 2025-09-07T08:19:26.4515290Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-09-07T08:19:26.4515528Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-09-07T08:19:26.4515602Z utility 2025-09-07T08:19:26.4515677Z 2025-09-07T08:19:26.4515911Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-09-07T08:19:26.4516119Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-09-07T08:19:26.4516330Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-09-07T08:19:26.4516515Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-09-07T08:19:26.4516680Z will not pass ``--local-rank`` when you specify this flag. 2025-09-07T08:19:26.4516749Z 2025-09-07T08:19:26.4516830Z .. warning:: 2025-09-07T08:19:26.4516908Z 2025-09-07T08:19:26.4517107Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-09-07T08:19:26.4517321Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-09-07T08:19:26.4517439Z write to a networked filesystem. See 2025-09-07T08:19:26.4517652Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-09-07T08:19:26.4517819Z how things can go wrong if you don't do this correctly. 2025-09-07T08:19:26.4517893Z 2025-09-07T08:19:26.4517974Z 2025-09-07T08:19:26.4518047Z 2025-09-07T08:19:26.4518118Z 2025-09-07T08:19:26.4518372Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4518446Z 2025-09-07T08:19:26.4518533Z warnings.warn(msg) 2025-09-07T08:19:26.4518617Z 2025-09-07T08:19:26.4518815Z --- Parse Warning: 55 / 146 --- 2025-09-07T08:19:26.4519872Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2025-09-07T08:19:26.4520140Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4520224Z 2025-09-07T08:19:26.4520457Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-09-07T08:19:26.4520599Z Needs to be called on all ranks in an SPMD fashion. 2025-09-07T08:19:26.4520682Z 2025-09-07T08:19:26.4520764Z Args: 2025-09-07T08:19:26.4521044Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-09-07T08:19:26.4521211Z of shards that represent the local shards on this rank. 2025-09-07T08:19:26.4521433Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-09-07T08:19:26.4521598Z shape of the overall sharded tensor. 2025-09-07T08:19:26.4521699Z 2025-09-07T08:19:26.4521788Z Keyword args: 2025-09-07T08:19:26.4522049Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T08:19:26.4522168Z the default process group will be used. 2025-09-07T08:19:26.4522343Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T08:19:26.4522545Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T08:19:26.4522737Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T08:19:26.4522827Z Default: ``False``. 2025-09-07T08:19:26.4522899Z 2025-09-07T08:19:26.4522987Z Returns: 2025-09-07T08:19:26.4523133Z A :class:`ShardedTensor` object handle on this rank 2025-09-07T08:19:26.4523205Z 2025-09-07T08:19:26.4523289Z 2025-09-07T08:19:26.4523368Z Examples: 2025-09-07T08:19:26.4523624Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-09-07T08:19:26.4523802Z each shard have a (5, 5) local tensor, we can do it like below: 2025-09-07T08:19:26.4523877Z 2025-09-07T08:19:26.4523969Z on rank 0: 2025-09-07T08:19:26.4524189Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T08:19:26.4524324Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T08:19:26.4524423Z >>> shard_offsets=[0, 0], 2025-09-07T08:19:26.4524518Z >>> shard_lengths=[5, 5], 2025-09-07T08:19:26.4524634Z >>> placement="rank:0/cuda:0" 2025-09-07T08:19:26.4524708Z >>> ) 2025-09-07T08:19:26.4524906Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T08:19:26.4525096Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T08:19:26.4525168Z 2025-09-07T08:19:26.4525261Z on rank 1: 2025-09-07T08:19:26.4525374Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T08:19:26.4525505Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T08:19:26.4525605Z >>> shard_offsets=[5, 0], 2025-09-07T08:19:26.4525697Z >>> shard_lengths=[5, 5], 2025-09-07T08:19:26.4525841Z >>> placement="rank:1/cuda:1" 2025-09-07T08:19:26.4525919Z >>> ) 2025-09-07T08:19:26.4526106Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T08:19:26.4526302Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T08:19:26.4526376Z 2025-09-07T08:19:26.4526632Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4526705Z 2025-09-07T08:19:26.4526794Z warnings.warn(msg) 2025-09-07T08:19:26.4526879Z 2025-09-07T08:19:26.4527070Z --- Parse Warning: 56 / 146 --- 2025-09-07T08:19:26.4528167Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=835. 2025-09-07T08:19:26.4528428Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4528513Z 2025-09-07T08:19:26.4528767Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-09-07T08:19:26.4528874Z size and sharding spec on each rank. 2025-09-07T08:19:26.4528959Z 2025-09-07T08:19:26.4529036Z Args: 2025-09-07T08:19:26.4529253Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-09-07T08:19:26.4529519Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-09-07T08:19:26.4529680Z The specification describing how to shard the Tensor. 2025-09-07T08:19:26.4529853Z global_size (Sequence[int]): Size of the sharded tensor. 2025-09-07T08:19:26.4530156Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-09-07T08:19:26.4530277Z Default: None 2025-09-07T08:19:26.4530443Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T08:19:26.4530643Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T08:19:26.4530837Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T08:19:26.4530926Z Default: ``False``. 2025-09-07T08:19:26.4531001Z 2025-09-07T08:19:26.4531091Z Returns: 2025-09-07T08:19:26.4531329Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-09-07T08:19:26.4531452Z tensor stored in the current rank. 2025-09-07T08:19:26.4531526Z 2025-09-07T08:19:26.4531603Z Examples: 2025-09-07T08:19:26.4531703Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4531830Z >>> # All tensors below are of torch.int64 type. 2025-09-07T08:19:26.4531959Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:26.4532126Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T08:19:26.4532320Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-09-07T08:19:26.4532419Z >>> local_tensor 2025-09-07T08:19:26.4532537Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-09-07T08:19:26.4532639Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-09-07T08:19:26.4532731Z >>> sharding_dim = 0 2025-09-07T08:19:26.4532848Z >>> sharding_spec = ChunkShardingSpec( 2025-09-07T08:19:26.4532947Z dim=sharding_dim, 2025-09-07T08:19:26.4533036Z placements=[ 2025-09-07T08:19:26.4533139Z "rank:0/cuda:0", 2025-09-07T08:19:26.4533225Z "rank:1/cuda:1", 2025-09-07T08:19:26.4533302Z ], 2025-09-07T08:19:26.4533388Z ) 2025-09-07T08:19:26.4533516Z >>> st = ShardedTensor._init_from_local_tensor( 2025-09-07T08:19:26.4533638Z ... local_tensor, sharding_spec, [2, 4] 2025-09-07T08:19:26.4533726Z ... ) 2025-09-07T08:19:26.4533806Z >>> st 2025-09-07T08:19:26.4533910Z ShardedTensor( 2025-09-07T08:19:26.4534035Z ShardedTensorMetadata( 2025-09-07T08:19:26.4534128Z shards_metadata=[ 2025-09-07T08:19:26.4534403Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-09-07T08:19:26.4534664Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-09-07T08:19:26.4534755Z ], 2025-09-07T08:19:26.4534853Z size=torch.Size([2, 4]) 2025-09-07T08:19:26.4534928Z ) 2025-09-07T08:19:26.4535029Z >>> st.local_tensor() 2025-09-07T08:19:26.4535122Z tensor([1, 2, 3, 4]) # Rank 0 2025-09-07T08:19:26.4535226Z tensor([3, 4, 5, 6]) # Rank 1 2025-09-07T08:19:26.4535299Z 2025-09-07T08:19:26.4535565Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-09-07T08:19:26.4535814Z rank validations, and we only validate the local shard on the current rank. 2025-09-07T08:19:26.4536025Z We fully rely on the user to ensure local tensor is sharded based on the 2025-09-07T08:19:26.4536129Z sharding spec. 2025-09-07T08:19:26.4536203Z 2025-09-07T08:19:26.4536450Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4536535Z 2025-09-07T08:19:26.4536624Z warnings.warn(msg) 2025-09-07T08:19:26.4536712Z 2025-09-07T08:19:26.4536897Z --- Parse Warning: 57 / 146 --- 2025-09-07T08:19:26.4537939Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1076. 2025-09-07T08:19:26.4538235Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4538333Z 2025-09-07T08:19:26.4538594Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-09-07T08:19:26.4538683Z single local shard. 2025-09-07T08:19:26.4538757Z 2025-09-07T08:19:26.4538987Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-09-07T08:19:26.4539225Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-09-07T08:19:26.4539336Z we swap local shards directly. 2025-09-07T08:19:26.4539590Z For more generic cases, we merge different shards across different ranks and split 2025-09-07T08:19:26.4539835Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-09-07T08:19:26.4539922Z 2025-09-07T08:19:26.4540000Z Args: 2025-09-07T08:19:26.4540297Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-09-07T08:19:26.4540459Z specification describing how the tensor is sharded. 2025-09-07T08:19:26.4540530Z 2025-09-07T08:19:26.4540612Z Returns: 2025-09-07T08:19:26.4540812Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-09-07T08:19:26.4540920Z 2025-09-07T08:19:26.4541001Z Examples: 2025-09-07T08:19:26.4541093Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4541212Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T08:19:26.4541381Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T08:19:26.4541506Z >>> tensor = torch.stack([tensor, tensor]) 2025-09-07T08:19:26.4541582Z >>> tensor 2025-09-07T08:19:26.4541699Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-09-07T08:19:26.4541819Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-09-07T08:19:26.4541926Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-09-07T08:19:26.4542040Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-09-07T08:19:26.4542138Z >>> sharding_dim = 0 2025-09-07T08:19:26.4542244Z >>> spec = ChunkShardingSpec( 2025-09-07T08:19:26.4542339Z dim=sharding_dim, 2025-09-07T08:19:26.4542450Z placements=[ 2025-09-07T08:19:26.4542540Z "rank:0/cuda:0", 2025-09-07T08:19:26.4542631Z "rank:1/cuda:1", 2025-09-07T08:19:26.4542715Z "rank:2/cuda:2", 2025-09-07T08:19:26.4542811Z "rank:3/cuda:3", 2025-09-07T08:19:26.4542887Z ], 2025-09-07T08:19:26.4542960Z ) 2025-09-07T08:19:26.4543063Z >>> current_offsets = [0] * 2 2025-09-07T08:19:26.4543163Z >>> current_offsets[0] = rank * 2 2025-09-07T08:19:26.4543276Z >>> shard_metadata = ShardMetadata( 2025-09-07T08:19:26.4543418Z shard_offsets=copy.deepcopy(current_offsets), 2025-09-07T08:19:26.4543521Z shard_sizes=tensor.size(), 2025-09-07T08:19:26.4543648Z placement=spec.placements[rank], 2025-09-07T08:19:26.4543721Z ) 2025-09-07T08:19:26.4543818Z >>> local_shards = [ 2025-09-07T08:19:26.4543894Z Shard( 2025-09-07T08:19:26.4543983Z tensor=tensor, 2025-09-07T08:19:26.4544095Z metadata=shard_metadata, 2025-09-07T08:19:26.4544168Z ) 2025-09-07T08:19:26.4544242Z ] 2025-09-07T08:19:26.4544469Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-09-07T08:19:26.4544557Z >>> sharding_dim = 1 2025-09-07T08:19:26.4544683Z >>> resharding_spec = ChunkShardingSpec( 2025-09-07T08:19:26.4544770Z dim=sharding_dim, 2025-09-07T08:19:26.4544858Z placements=[ 2025-09-07T08:19:26.4544948Z "rank:0/cuda:0", 2025-09-07T08:19:26.4545034Z "rank:1/cuda:1", 2025-09-07T08:19:26.4545128Z "rank:2/cuda:2", 2025-09-07T08:19:26.4545236Z "rank:3/cuda:3", 2025-09-07T08:19:26.4545310Z ], 2025-09-07T08:19:26.4545412Z ) 2025-09-07T08:19:26.4545513Z >>> st.reshard(resharding_spec) 2025-09-07T08:19:26.4545629Z >>> tensor = st.local_shards()[0].tensor 2025-09-07T08:19:26.4545708Z >>> tensor 2025-09-07T08:19:26.4545846Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-09-07T08:19:26.4545983Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-09-07T08:19:26.4546113Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-09-07T08:19:26.4546253Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-09-07T08:19:26.4546327Z 2025-09-07T08:19:26.4546575Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4546654Z 2025-09-07T08:19:26.4546743Z warnings.warn(msg) 2025-09-07T08:19:26.4546817Z 2025-09-07T08:19:26.4547007Z --- Parse Warning: 58 / 146 --- 2025-09-07T08:19:26.4547992Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2025-09-07T08:19:26.4548291Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4548365Z 2025-09-07T08:19:26.4548585Z Representation of a sharding plan, describes how to shard a module 2025-09-07T08:19:26.4548853Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-09-07T08:19:26.4549134Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-09-07T08:19:26.4549386Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-09-07T08:19:26.4549461Z 2025-09-07T08:19:26.4549542Z Args: 2025-09-07T08:19:26.4549804Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-09-07T08:19:26.4549970Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-09-07T08:19:26.4550281Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-09-07T08:19:26.4550534Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-09-07T08:19:26.4550659Z a parameter to a `ShardingSpec`. 2025-09-07T08:19:26.4550909Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-09-07T08:19:26.4551009Z to a `Sharder` object. 2025-09-07T08:19:26.4551339Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-09-07T08:19:26.4551594Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-09-07T08:19:26.4551831Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-09-07T08:19:26.4551921Z Default: `None` 2025-09-07T08:19:26.4552172Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-09-07T08:19:26.4552420Z a module's sharded output to be returned as a Tensor from its local shards to 2025-09-07T08:19:26.4552664Z ensure further processing in a data parallel fashion. ("" in list means the 2025-09-07T08:19:26.4552762Z root module). 2025-09-07T08:19:26.4552848Z Default: None 2025-09-07T08:19:26.4552926Z Example: 2025-09-07T08:19:26.4553210Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-09-07T08:19:26.4553486Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-09-07T08:19:26.4553571Z 2025-09-07T08:19:26.4553742Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-09-07T08:19:26.4553870Z >>> class MyModule(nn.Module): 2025-09-07T08:19:26.4553980Z >>> def __init__(self) -> None: 2025-09-07T08:19:26.4554099Z >>> super().__init__() 2025-09-07T08:19:26.4554201Z >>> self.fc1 = nn.Linear() 2025-09-07T08:19:26.4554303Z >>> self.gelu = nn.GELU() 2025-09-07T08:19:26.4554395Z >>> self.fc2 = nn.Linear() 2025-09-07T08:19:26.4554499Z >>> self.relu = nn.Linear() 2025-09-07T08:19:26.4554576Z >>> 2025-09-07T08:19:26.4554681Z >>> def forward(self, input): 2025-09-07T08:19:26.4554848Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-09-07T08:19:26.4554921Z 2025-09-07T08:19:26.4555001Z 2025-09-07T08:19:26.4555127Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-09-07T08:19:26.4555239Z >>> sharding_plan = ShardingPlan( 2025-09-07T08:19:26.4555318Z >>> plan={ 2025-09-07T08:19:26.4555414Z >>> "fc1.weight": spec1, 2025-09-07T08:19:26.4555524Z >>> "fc2.weight": spec2 2025-09-07T08:19:26.4555603Z >>> }, 2025-09-07T08:19:26.4555699Z >>> output_plan={ 2025-09-07T08:19:26.4555791Z >>> "fc2": output_spec 2025-09-07T08:19:26.4555865Z >>> }, 2025-09-07T08:19:26.4555998Z >>> return_local_tensor=["fc2"] 2025-09-07T08:19:26.4556072Z >>> ) 2025-09-07T08:19:26.4556141Z 2025-09-07T08:19:26.4556397Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4556471Z 2025-09-07T08:19:26.4556566Z warnings.warn(msg) 2025-09-07T08:19:26.4556641Z 2025-09-07T08:19:26.4556823Z --- Parse Warning: 59 / 146 --- 2025-09-07T08:19:26.4557933Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2025-09-07T08:19:26.4558188Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4558273Z 2025-09-07T08:19:26.4558375Z Run post-localSGD algorithm. 2025-09-07T08:19:26.4558478Z 2025-09-07T08:19:26.4558723Z This DDP communication hook is used for running post-localSGD algorithm, 2025-09-07T08:19:26.4558873Z by combining with a model averaging component (e.g., 2025-09-07T08:19:26.4559204Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-09-07T08:19:26.4559313Z that runs after the optimizer step. 2025-09-07T08:19:26.4559383Z 2025-09-07T08:19:26.4559471Z Args: 2025-09-07T08:19:26.4559684Z state (PostLocalSGDState): State information to run post-localSGD. 2025-09-07T08:19:26.4559963Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-09-07T08:19:26.4560381Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T08:19:26.4560622Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T08:19:26.4560780Z only exactly one tensor is stored in this bucket. 2025-09-07T08:19:26.4560853Z 2025-09-07T08:19:26.4560934Z Returns: 2025-09-07T08:19:26.4561167Z Future handler of the communication, which updates the gradients in place. 2025-09-07T08:19:26.4561239Z 2025-09-07T08:19:26.4561330Z Example:: 2025-09-07T08:19:26.4561430Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4561687Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-09-07T08:19:26.4561800Z start_localSGD_iter=10) 2025-09-07T08:19:26.4561972Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T08:19:26.4562341Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-09-07T08:19:26.4562687Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-09-07T08:19:26.4562797Z 2025-09-07T08:19:26.4563051Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4563129Z 2025-09-07T08:19:26.4563228Z warnings.warn(msg) 2025-09-07T08:19:26.4563306Z 2025-09-07T08:19:26.4563493Z --- Parse Warning: 60 / 146 --- 2025-09-07T08:19:26.4564677Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2025-09-07T08:19:26.4564952Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4565028Z 2025-09-07T08:19:26.4565137Z Implement PowerSGD algorithm. 2025-09-07T08:19:26.4565226Z 2025-09-07T08:19:26.4565447Z This DDP communication hook implements PowerSGD gradient compression 2025-09-07T08:19:26.4565690Z algorithm described in the `paper `_. 2025-09-07T08:19:26.4565935Z Once gradient tensors are aggregated across all workers, this hook applies 2025-09-07T08:19:26.4566071Z compression as follows: 2025-09-07T08:19:26.4566157Z 2025-09-07T08:19:26.4566593Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-09-07T08:19:26.4566670Z 2025-09-07T08:19:26.4567096Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-09-07T08:19:26.4567174Z 2025-09-07T08:19:26.4567587Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-09-07T08:19:26.4567664Z 2025-09-07T08:19:26.4567774Z 2. Handles uncompressed tensors: 2025-09-07T08:19:26.4567864Z 2025-09-07T08:19:26.4568400Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-09-07T08:19:26.4568489Z 2025-09-07T08:19:26.4568823Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-09-07T08:19:26.4568902Z 2025-09-07T08:19:26.4569141Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-09-07T08:19:26.4569216Z 2025-09-07T08:19:26.4569458Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-09-07T08:19:26.4569766Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-09-07T08:19:26.4569845Z 2025-09-07T08:19:26.4569997Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-09-07T08:19:26.4570075Z 2025-09-07T08:19:26.4570187Z 3.3. Allreduces Ps as a batch; 2025-09-07T08:19:26.4570262Z 2025-09-07T08:19:26.4570375Z 3.4. Orthogonalizes each P in Ps; 2025-09-07T08:19:26.4570462Z 2025-09-07T08:19:26.4570657Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-09-07T08:19:26.4570741Z 2025-09-07T08:19:26.4570841Z 3.6. Allreduces Qs as a batch; 2025-09-07T08:19:26.4570919Z 2025-09-07T08:19:26.4571219Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-09-07T08:19:26.4571294Z 2025-09-07T08:19:26.4571708Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-09-07T08:19:26.4571988Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-09-07T08:19:26.4572440Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-09-07T08:19:26.4572522Z 2025-09-07T08:19:26.4572628Z Args: 2025-09-07T08:19:26.4573066Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-09-07T08:19:26.4573597Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-09-07T08:19:26.4573707Z and ``min_compression_rate``. 2025-09-07T08:19:26.4574131Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T08:19:26.4574374Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T08:19:26.4574533Z only exactly one tensor is stored in this bucket. 2025-09-07T08:19:26.4574612Z 2025-09-07T08:19:26.4574691Z Returns: 2025-09-07T08:19:26.4574935Z Future handler of the communication, which updates the gradients in place. 2025-09-07T08:19:26.4575013Z 2025-09-07T08:19:26.4575109Z Example:: 2025-09-07T08:19:26.4575204Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4575478Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-09-07T08:19:26.4575710Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-09-07T08:19:26.4575865Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-09-07T08:19:26.4575954Z 2025-09-07T08:19:26.4576203Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4576280Z 2025-09-07T08:19:26.4576381Z warnings.warn(msg) 2025-09-07T08:19:26.4576459Z 2025-09-07T08:19:26.4576675Z --- Parse Warning: 61 / 146 --- 2025-09-07T08:19:26.4577788Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38. 2025-09-07T08:19:26.4578050Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4578134Z 2025-09-07T08:19:26.4578354Z Averages parameters periodically after the warm-up stage. 2025-09-07T08:19:26.4578439Z 2025-09-07T08:19:26.4578691Z This can be used for running `post-local SGD `_, 2025-09-07T08:19:26.4578880Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-09-07T08:19:26.4579123Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-09-07T08:19:26.4579198Z 2025-09-07T08:19:26.4579287Z Args: 2025-09-07T08:19:26.4579447Z period (int): The number of steps per model averaging. 2025-09-07T08:19:26.4579714Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-09-07T08:19:26.4579852Z Otherwise, only DDP needs to be used. 2025-09-07T08:19:26.4580053Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-09-07T08:19:26.4580184Z model averaging is skipped. 2025-09-07T08:19:26.4580366Z process_group: The process group to be used for all-reduce. 2025-09-07T08:19:26.4580506Z If ``None``, the default process group, which 2025-09-07T08:19:26.4580702Z is created by :func:`torch.distributed.init_process_group`, 2025-09-07T08:19:26.4580818Z will be used. (default: ``None``) 2025-09-07T08:19:26.4580904Z 2025-09-07T08:19:26.4580987Z Example:: 2025-09-07T08:19:26.4581062Z 2025-09-07T08:19:26.4581195Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4581282Z >>> import torch 2025-09-07T08:19:26.4581409Z >>> import torch.distributed as dist 2025-09-07T08:19:26.4581751Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-09-07T08:19:26.4582018Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T08:19:26.4582159Z >>> import torch.nn as nn 2025-09-07T08:19:26.4582239Z >>> 2025-09-07T08:19:26.4582428Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T08:19:26.4582533Z >>> torch.cuda.set_device(rank) 2025-09-07T08:19:26.4582657Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-09-07T08:19:26.4582824Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T08:19:26.4582960Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T08:19:26.4583053Z >>> ) 2025-09-07T08:19:26.4583194Z >>> # Register a post-localSGD communication hook. 2025-09-07T08:19:26.4583491Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T08:19:26.4583662Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T08:19:26.4583741Z >>> 2025-09-07T08:19:26.4584018Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T08:19:26.4584169Z >>> # After 100 steps, run model averaging every 4 steps. 2025-09-07T08:19:26.4584519Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T08:19:26.4584777Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T08:19:26.4584876Z >>> for step in range(0, 200): 2025-09-07T08:19:26.4584987Z >>> optimizer.zero_grad() 2025-09-07T08:19:26.4585094Z >>> loss = loss_fn(output, labels) 2025-09-07T08:19:26.4585189Z >>> loss.backward() 2025-09-07T08:19:26.4585297Z >>> optimizer.step() 2025-09-07T08:19:26.4585482Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-09-07T08:19:26.4585701Z >>> # inter-node communication only occurs every 4 iterations after 2025-09-07T08:19:26.4585823Z >>> # the initial ``warmup_steps`` period. 2025-09-07T08:19:26.4585975Z >>> averager.average_parameters(model.parameters()) 2025-09-07T08:19:26.4586092Z 2025-09-07T08:19:26.4586337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4586424Z 2025-09-07T08:19:26.4586512Z warnings.warn(msg) 2025-09-07T08:19:26.4586590Z 2025-09-07T08:19:26.4586784Z --- Parse Warning: 62 / 146 --- 2025-09-07T08:19:26.4587998Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19. 2025-09-07T08:19:26.4588268Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4588341Z 2025-09-07T08:19:26.4588671Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-09-07T08:19:26.4588758Z 2025-09-07T08:19:26.4589064Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-09-07T08:19:26.4589282Z by using different periods concurrently after the warm-up stage. 2025-09-07T08:19:26.4589687Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-09-07T08:19:26.4590024Z that supports `post-local SGD `_, which essentially only supports 2025-09-07T08:19:26.4590328Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-09-07T08:19:26.4590678Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-09-07T08:19:26.4591014Z Similarly, the process groups within this class do not have such an intra-machine process 2025-09-07T08:19:26.4591284Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-09-07T08:19:26.4591399Z 2025-09-07T08:19:26.4591480Z Args: 2025-09-07T08:19:26.4591737Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-09-07T08:19:26.4591946Z process group size, used for initializing process groups of 2025-09-07T08:19:26.4592167Z different sizes in a hierarchy to average parameters concurrently. 2025-09-07T08:19:26.4592387Z Particularly, at each iteration, there will be at most a single 2025-09-07T08:19:26.4592613Z process group that runs averaging -- the period of such group should 2025-09-07T08:19:26.4592821Z have the largest period which the current step can be divided by. 2025-09-07T08:19:26.4592995Z For example, if the dict has three keys: 2, 4, and 8, 2025-09-07T08:19:26.4593197Z then this means totally three process groups will be created to 2025-09-07T08:19:26.4593417Z average parameters every 2, 4, and 8 iterations, respectively. 2025-09-07T08:19:26.4593628Z At the 4th iteration, only the second process group will run 2025-09-07T08:19:26.4593811Z averaging, because the first process group should be a 2025-09-07T08:19:26.4594031Z subset of the second process group, and no need to execute the first 2025-09-07T08:19:26.4594152Z process group redundantly. 2025-09-07T08:19:26.4594357Z On the other hand, the third process group can only be triggered 2025-09-07T08:19:26.4594572Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-09-07T08:19:26.4594884Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-09-07T08:19:26.4595355Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-09-07T08:19:26.4595535Z If ``None``, the default process group, which is created 2025-09-07T08:19:26.4595743Z by :func:`torch.distributed.init_process_group`, will be used. 2025-09-07T08:19:26.4595862Z (default: ``None``) 2025-09-07T08:19:26.4595948Z 2025-09-07T08:19:26.4596034Z Example:: 2025-09-07T08:19:26.4596159Z >>> # xdoctest: +SKIP('undefined rank') 2025-09-07T08:19:26.4596277Z >>> from collections import OrderedDict 2025-09-07T08:19:26.4596366Z >>> import torch 2025-09-07T08:19:26.4596490Z >>> import torch.distributed as dist 2025-09-07T08:19:26.4596763Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T08:19:26.4596860Z >>> PostLocalSGDState, 2025-09-07T08:19:26.4596969Z >>> post_localSGD_hook, 2025-09-07T08:19:26.4597049Z >>> ) 2025-09-07T08:19:26.4597429Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-09-07T08:19:26.4597528Z >>> import torch.nn as nn 2025-09-07T08:19:26.4597601Z >>> 2025-09-07T08:19:26.4597788Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T08:19:26.4597895Z >>> torch.cuda.set_device(rank) 2025-09-07T08:19:26.4598034Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T08:19:26.4598187Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T08:19:26.4598323Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T08:19:26.4598414Z >>> ) 2025-09-07T08:19:26.4598581Z >>> # Register a post-localSGD communication hook. 2025-09-07T08:19:26.4598890Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-09-07T08:19:26.4599011Z >>> subgroup, _ = dist.new_subgroups() 2025-09-07T08:19:26.4599320Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-09-07T08:19:26.4599491Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T08:19:26.4599573Z >>> 2025-09-07T08:19:26.4599864Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-09-07T08:19:26.4599985Z >>> # the 16 processes every 16 iterations. 2025-09-07T08:19:26.4600171Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-09-07T08:19:26.4600412Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-09-07T08:19:26.4600730Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T08:19:26.4601008Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T08:19:26.4601165Z >>> # After 100 steps, run model averaging at two levels. 2025-09-07T08:19:26.4601301Z >>> for step in range(0, 200): 2025-09-07T08:19:26.4601402Z >>> optimizer.zero_grad() 2025-09-07T08:19:26.4601513Z >>> loss = loss_fn(output, labels) 2025-09-07T08:19:26.4601617Z >>> loss.backward() 2025-09-07T08:19:26.4601713Z >>> optimizer.step() 2025-09-07T08:19:26.4601876Z >>> # Average parameters after ``optimizer.step()``. 2025-09-07T08:19:26.4602163Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-09-07T08:19:26.4602323Z >>> averager.average_parameters(model.parameters()) 2025-09-07T08:19:26.4602411Z 2025-09-07T08:19:26.4602497Z .. warning :: 2025-09-07T08:19:26.4602760Z The last group size in the dict must be the size of the provided ``process_group``, 2025-09-07T08:19:26.4602994Z which indicates model averaging at the highest level of the hierarchy. 2025-09-07T08:19:26.4603318Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-09-07T08:19:26.4603404Z 2025-09-07T08:19:26.4603488Z .. warning :: 2025-09-07T08:19:26.4603726Z `HierarchicalModelAverager` is experimental and subject to change. 2025-09-07T08:19:26.4603800Z 2025-09-07T08:19:26.4604047Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4604222Z 2025-09-07T08:19:26.4604318Z warnings.warn(msg) 2025-09-07T08:19:26.4604392Z 2025-09-07T08:19:26.4604599Z --- Parse Warning: 63 / 146 --- 2025-09-07T08:19:26.4605680Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2025-09-07T08:19:26.4605955Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4606034Z 2025-09-07T08:19:26.4606329Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-09-07T08:19:26.4606570Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-09-07T08:19:26.4606646Z 2025-09-07T08:19:26.4606815Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-09-07T08:19:26.4606889Z 2025-09-07T08:19:26.4606982Z .. warning:: 2025-09-07T08:19:26.4607151Z Current implementation only supports loading Tensors. 2025-09-07T08:19:26.4607226Z 2025-09-07T08:19:26.4607342Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4607432Z >>> sd = {"mode": model} 2025-09-07T08:19:26.4607548Z >>> dcp.load( 2025-09-07T08:19:26.4607636Z >>> sd, 2025-09-07T08:19:26.4607808Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T08:19:26.4607938Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T08:19:26.4608052Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T08:19:26.4608129Z >>> ) 2025-09-07T08:19:26.4608211Z 2025-09-07T08:19:26.4608461Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4608545Z 2025-09-07T08:19:26.4608637Z warnings.warn(msg) 2025-09-07T08:19:26.4608713Z 2025-09-07T08:19:26.4608907Z --- Parse Warning: 64 / 146 --- 2025-09-07T08:19:26.4609946Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2025-09-07T08:19:26.4610211Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4610290Z 2025-09-07T08:19:26.4610655Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-09-07T08:19:26.4610981Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-09-07T08:19:26.4611118Z metadata file, like Torch Save files. 2025-09-07T08:19:26.4611200Z 2025-09-07T08:19:26.4611380Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-09-07T08:19:26.4611457Z 2025-09-07T08:19:26.4611549Z .. warning:: 2025-09-07T08:19:26.4611722Z Current implementation only supports loading Tensors. 2025-09-07T08:19:26.4611806Z 2025-09-07T08:19:26.4611918Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4612009Z >>> sd = {"mode": model} 2025-09-07T08:19:26.4612100Z >>> dcp.load( 2025-09-07T08:19:26.4612177Z >>> sd, 2025-09-07T08:19:26.4612336Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T08:19:26.4612460Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T08:19:26.4612569Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T08:19:26.4612651Z >>> ) 2025-09-07T08:19:26.4612754Z 2025-09-07T08:19:26.4613006Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4613091Z 2025-09-07T08:19:26.4613181Z warnings.warn(msg) 2025-09-07T08:19:26.4613263Z 2025-09-07T08:19:26.4613440Z --- Parse Warning: 65 / 146 --- 2025-09-07T08:19:26.4614499Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=221. 2025-09-07T08:19:26.4614768Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4614846Z 2025-09-07T08:19:26.4615061Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-09-07T08:19:26.4615140Z 2025-09-07T08:19:26.4615302Z This is the current recommended way to checkpoint FSDP. 2025-09-07T08:19:26.4615406Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4615558Z >>> import torch.distributed.checkpoint as dist_cp 2025-09-07T08:19:26.4615645Z >>> # Save 2025-09-07T08:19:26.4615742Z >>> model: torch.nn.Model 2025-09-07T08:19:26.4615858Z >>> optim_params = model.parameters() 2025-09-07T08:19:26.4616005Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-09-07T08:19:26.4616085Z >>> # Save 2025-09-07T08:19:26.4616308Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-09-07T08:19:26.4616400Z >>> state_dict = { 2025-09-07T08:19:26.4616550Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-09-07T08:19:26.4616663Z >>> "model": model.state_dict() 2025-09-07T08:19:26.4616771Z >>> } 2025-09-07T08:19:26.4616878Z >>> dist_cp.save_state_dict( 2025-09-07T08:19:26.4617014Z >>> state_dict=optim_state, 2025-09-07T08:19:26.4617191Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-09-07T08:19:26.4617327Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-09-07T08:19:26.4617405Z >>> ) 2025-09-07T08:19:26.4617492Z >>> 2025-09-07T08:19:26.4617572Z >>> # Load 2025-09-07T08:19:26.4617796Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-09-07T08:19:26.4617929Z >>> model_state_dict = model_tp.state_dict() 2025-09-07T08:19:26.4618018Z >>> checkpoint = { 2025-09-07T08:19:26.4618116Z >>> "model": model_state_dict 2025-09-07T08:19:26.4618205Z >>> } 2025-09-07T08:19:26.4618305Z >>> dist_cp.load_state_dict( 2025-09-07T08:19:26.4618417Z >>> state_dict=checkpoint, 2025-09-07T08:19:26.4618606Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-09-07T08:19:26.4618731Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-09-07T08:19:26.4618822Z >>> ) 2025-09-07T08:19:26.4618972Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-09-07T08:19:26.4619060Z >>> 2025-09-07T08:19:26.4619254Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-09-07T08:19:26.4619345Z >>> model_state_dict, 2025-09-07T08:19:26.4619461Z >>> optimizer_key="optimizer", 2025-09-07T08:19:26.4619635Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-09-07T08:19:26.4619721Z >>> ) 2025-09-07T08:19:26.4619798Z >>> 2025-09-07T08:19:26.4619936Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-09-07T08:19:26.4620069Z >>> model, optim, optim_state["optimizer"] 2025-09-07T08:19:26.4620146Z >>> ) 2025-09-07T08:19:26.4620233Z >>> 2025-09-07T08:19:26.4620350Z >>> optim.load_state_dict(flattened_osd) 2025-09-07T08:19:26.4620425Z 2025-09-07T08:19:26.4620686Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4620762Z 2025-09-07T08:19:26.4620853Z warnings.warn(msg) 2025-09-07T08:19:26.4620963Z 2025-09-07T08:19:26.4621144Z --- Parse Warning: 66 / 146 --- 2025-09-07T08:19:26.4622120Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-09-07T08:19:26.4622383Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4622468Z 2025-09-07T08:19:26.4622753Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-09-07T08:19:26.4622826Z 2025-09-07T08:19:26.4623129Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-09-07T08:19:26.4623209Z 2025-09-07T08:19:26.4623492Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T08:19:26.4623605Z will be visible to the whole process. 2025-09-07T08:19:26.4623683Z 2025-09-07T08:19:26.4623965Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-09-07T08:19:26.4624042Z 2025-09-07T08:19:26.4624157Z 1) set_up_planner - called on all ranks. 2025-09-07T08:19:26.4624288Z Signals the start of a checkpoint save. 2025-09-07T08:19:26.4624360Z 2025-09-07T08:19:26.4624488Z 2) create_local_plan - called on all ranks. 2025-09-07T08:19:26.4624771Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-09-07T08:19:26.4624846Z 2025-09-07T08:19:26.4625037Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T08:19:26.4625261Z Takes the SavePlan from all ranks and make any global decision. 2025-09-07T08:19:26.4625350Z 2025-09-07T08:19:26.4625486Z 4) finish_plan - called on all ranks. 2025-09-07T08:19:26.4625695Z This gives each rank a chance to adjust to global planning decisions. 2025-09-07T08:19:26.4625783Z 2025-09-07T08:19:26.4625936Z 5) resolve_data - called multiple times on each rank 2025-09-07T08:19:26.4626144Z Lookups a value on the `state_dict` for the storage layer to write. 2025-09-07T08:19:26.4626219Z 2025-09-07T08:19:26.4626518Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-09-07T08:19:26.4626704Z most changes can be expressed by changes in a single method. 2025-09-07T08:19:26.4626779Z 2025-09-07T08:19:26.4626907Z There are 3 usual patterns of extension: 2025-09-07T08:19:26.4626983Z 2025-09-07T08:19:26.4627233Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-09-07T08:19:26.4627471Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-09-07T08:19:26.4627550Z 2025-09-07T08:19:26.4627683Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4627817Z >>> class RenamePlanner(DefaultSavePlanner): 2025-09-07T08:19:26.4627916Z >>> def set_up_planner( 2025-09-07T08:19:26.4628037Z >>> self, 2025-09-07T08:19:26.4628147Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T08:19:26.4628284Z >>> storage_meta: Optional[StorageMeta], 2025-09-07T08:19:26.4628389Z >>> is_coordinator: bool, 2025-09-07T08:19:26.4628476Z >>> ) -> None: 2025-09-07T08:19:26.4628594Z >>> # prefix all keys with `foo_`` 2025-09-07T08:19:26.4628888Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-09-07T08:19:26.4628968Z 2025-09-07T08:19:26.4629311Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-09-07T08:19:26.4629390Z 2025-09-07T08:19:26.4629515Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4629640Z >>> class FP16Planner(DefaultSavePlanner): 2025-09-07T08:19:26.4629746Z >>> def create_local_plan(self): 2025-09-07T08:19:26.4629905Z >>> plan = super().create_local_plan() 2025-09-07T08:19:26.4630005Z >>> for p in plan: 2025-09-07T08:19:26.4630135Z >>> if p.tensor_data is not None: 2025-09-07T08:19:26.4630295Z >>> p.tensor_data.properties.dtype = torch.float16 2025-09-07T08:19:26.4630388Z >>> return plan 2025-09-07T08:19:26.4630487Z >>> 2025-09-07T08:19:26.4630604Z >>> def resolve_data(self, write_item): 2025-09-07T08:19:26.4630739Z >>> item = super().resolve_data(write_item) 2025-09-07T08:19:26.4631010Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-09-07T08:19:26.4631090Z 2025-09-07T08:19:26.4631447Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-09-07T08:19:26.4631529Z 2025-09-07T08:19:26.4631656Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4631771Z >>> from itertools import zip_longest 2025-09-07T08:19:26.4631884Z >>> from dataclasses import replace 2025-09-07T08:19:26.4632063Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-09-07T08:19:26.4632333Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-09-07T08:19:26.4632474Z >>> # This sample doesn't handle ShardedTensors 2025-09-07T08:19:26.4632601Z >>> def create_global_plan(self, all_plans): 2025-09-07T08:19:26.4632748Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-09-07T08:19:26.4632859Z >>> items_per_rank = [ 2025-09-07T08:19:26.4632993Z >>> [item for item in items if item is not None] 2025-09-07T08:19:26.4633182Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-09-07T08:19:26.4633265Z >>> ] 2025-09-07T08:19:26.4633382Z >>> all_plans = [ 2025-09-07T08:19:26.4633503Z >>> replace(plan, items=items) 2025-09-07T08:19:26.4633691Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-09-07T08:19:26.4633787Z >>> ] 2025-09-07T08:19:26.4633925Z >>> return super().create_global_plan(all_plans) 2025-09-07T08:19:26.4634004Z 2025-09-07T08:19:26.4634277Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-09-07T08:19:26.4634546Z accomplished by having each rank contribute their data items in the local plan and 2025-09-07T08:19:26.4634667Z the global planner aggregate them: 2025-09-07T08:19:26.4634745Z 2025-09-07T08:19:26.4634855Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4635017Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-09-07T08:19:26.4635145Z >>> def create_local_plan(self) -> SavePlan: 2025-09-07T08:19:26.4635270Z >>> plan = super().create_local_plan() 2025-09-07T08:19:26.4635431Z >>> return replace(plan, planner_data="per-rank-data") 2025-09-07T08:19:26.4635512Z >>> 2025-09-07T08:19:26.4635842Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-09-07T08:19:26.4636036Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-09-07T08:19:26.4636201Z >>> merged_data = [p.planner_data for p in global_plan] 2025-09-07T08:19:26.4636368Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-09-07T08:19:26.4636478Z >>> return global_plan, metadata 2025-09-07T08:19:26.4636564Z 2025-09-07T08:19:26.4636813Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4636901Z 2025-09-07T08:19:26.4636994Z warnings.warn(msg) 2025-09-07T08:19:26.4637069Z 2025-09-07T08:19:26.4637277Z --- Parse Warning: 67 / 146 --- 2025-09-07T08:19:26.4638272Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-09-07T08:19:26.4638549Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4638628Z 2025-09-07T08:19:26.4638914Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-09-07T08:19:26.4639004Z 2025-09-07T08:19:26.4639290Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-09-07T08:19:26.4639385Z 2025-09-07T08:19:26.4639662Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T08:19:26.4639775Z will be visible to the whole process. 2025-09-07T08:19:26.4639863Z 2025-09-07T08:19:26.4640139Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-09-07T08:19:26.4640230Z 2025-09-07T08:19:26.4640347Z 1) set_up_planner - called on all ranks. 2025-09-07T08:19:26.4640478Z Signals the start of loading a checkpoint. 2025-09-07T08:19:26.4640567Z 2025-09-07T08:19:26.4640688Z 2) create_local_plan - called on all ranks. 2025-09-07T08:19:26.4640978Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-09-07T08:19:26.4641055Z 2025-09-07T08:19:26.4641231Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T08:19:26.4641437Z Takes the LoadPlan from all ranks and make any global decision. 2025-09-07T08:19:26.4641511Z 2025-09-07T08:19:26.4641654Z 4) load_bytes - called multiple times on each rank 2025-09-07T08:19:26.4641824Z This is called once per non-tensor value in state_dict. 2025-09-07T08:19:26.4641900Z 2025-09-07T08:19:26.4642156Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-09-07T08:19:26.4642362Z They are called in pair for each Tensor value in state_dict. 2025-09-07T08:19:26.4642439Z 2025-09-07T08:19:26.4642752Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-09-07T08:19:26.4642934Z most changes can be expressed by changes in a single method. 2025-09-07T08:19:26.4643022Z 2025-09-07T08:19:26.4643148Z There are two usual patterns of extension: 2025-09-07T08:19:26.4643225Z 2025-09-07T08:19:26.4643490Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-09-07T08:19:26.4643741Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-09-07T08:19:26.4643978Z to keep a reference to the original state_dict as load happens in place so 2025-09-07T08:19:26.4644187Z we need to be able to perform it in place 2025-09-07T08:19:26.4644268Z 2025-09-07T08:19:26.4644402Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4644537Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-09-07T08:19:26.4644649Z >>> def set_up_planner( 2025-09-07T08:19:26.4644737Z >>> self, 2025-09-07T08:19:26.4644878Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T08:19:26.4644993Z >>> metadata: Metadata, 2025-09-07T08:19:26.4645095Z >>> is_coordinator: bool, 2025-09-07T08:19:26.4645189Z >>> ) -> None: 2025-09-07T08:19:26.4645313Z >>> self.original_state_dict = state_dict 2025-09-07T08:19:26.4645484Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-09-07T08:19:26.4645575Z >>> 2025-09-07T08:19:26.4645687Z >>> if self.flatten_sharded_tensors: 2025-09-07T08:19:26.4645845Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-09-07T08:19:26.4645920Z >>> 2025-09-07T08:19:26.4646031Z >>> if self.flatten_state_dict: 2025-09-07T08:19:26.4646217Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-09-07T08:19:26.4646296Z >>> 2025-09-07T08:19:26.4646403Z >>> self.state_dict = state_dict 2025-09-07T08:19:26.4646542Z >>> self.metadata = metadata 2025-09-07T08:19:26.4646668Z >>> self.is_coordinator = is_coordinator 2025-09-07T08:19:26.4646759Z >>> 2025-09-07T08:19:26.4646878Z >>> def load_bytes(self, read_item, value): 2025-09-07T08:19:26.4646978Z >>> # Remove the "foo_" prefix 2025-09-07T08:19:26.4647306Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-09-07T08:19:26.4647383Z 2025-09-07T08:19:26.4647474Z 2025-09-07T08:19:26.4647732Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-09-07T08:19:26.4647809Z 2025-09-07T08:19:26.4647930Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4648086Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-09-07T08:19:26.4648214Z >>> def resolve_tensor(self, read_item): 2025-09-07T08:19:26.4648343Z >>> tensor = super().resolve_tensor(read_item) 2025-09-07T08:19:26.4648489Z >>> return torch.empty_like(tensor, device="cpu") 2025-09-07T08:19:26.4648583Z >>> 2025-09-07T08:19:26.4648711Z >>> def commit_tensor(self, read_item, tensor): 2025-09-07T08:19:26.4648879Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-09-07T08:19:26.4648955Z 2025-09-07T08:19:26.4649204Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4649291Z 2025-09-07T08:19:26.4649383Z warnings.warn(msg) 2025-09-07T08:19:26.4649469Z 2025-09-07T08:19:26.4649664Z --- Parse Warning: 68 / 146 --- 2025-09-07T08:19:26.4650690Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-09-07T08:19:26.4650992Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4651069Z 2025-09-07T08:19:26.4651242Z Return the model state_dict and optimizers state_dict. 2025-09-07T08:19:26.4651318Z 2025-09-07T08:19:26.4651536Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-09-07T08:19:26.4651800Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-09-07T08:19:26.4652050Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-09-07T08:19:26.4652277Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-09-07T08:19:26.4652484Z with a different number of trainers and/or different parallelisms. 2025-09-07T08:19:26.4652735Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-09-07T08:19:26.4652837Z these APIs. 2025-09-07T08:19:26.4652959Z 3.) sanity checking the result state_dict. 2025-09-07T08:19:26.4653047Z 2025-09-07T08:19:26.4653253Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-09-07T08:19:26.4653510Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-09-07T08:19:26.4653756Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-09-07T08:19:26.4653960Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-09-07T08:19:26.4654177Z ``module.named_buffers()`` when the module is not distributed by any 2025-09-07T08:19:26.4654431Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-09-07T08:19:26.4654643Z a parameter, there will be a conversion from the parameter IDs to the 2025-09-07T08:19:26.4654770Z canonical FQNs when calling this API. 2025-09-07T08:19:26.4654846Z 2025-09-07T08:19:26.4655071Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-09-07T08:19:26.4655291Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-09-07T08:19:26.4655457Z optimizer parameter IDs to the canonical FQNs. 2025-09-07T08:19:26.4655546Z 2025-09-07T08:19:26.4655628Z Example: 2025-09-07T08:19:26.4655732Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4655821Z >>> import torch 2025-09-07T08:19:26.4656054Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T08:19:26.4656264Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T08:19:26.4656484Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-09-07T08:19:26.4656568Z 2025-09-07T08:19:26.4656692Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-09-07T08:19:26.4656876Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T08:19:26.4657009Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-09-07T08:19:26.4657184Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T08:19:26.4657273Z 2025-09-07T08:19:26.4657350Z 2025-09-07T08:19:26.4657586Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-09-07T08:19:26.4657770Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-09-07T08:19:26.4657878Z ... fsdp_model, fsdp_optim 2025-09-07T08:19:26.4657967Z ... ) 2025-09-07T08:19:26.4658048Z 2025-09-07T08:19:26.4658259Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-09-07T08:19:26.4658371Z >>> # the asserts will fail. 2025-09-07T08:19:26.4658499Z >>> assert ddp_state_dict == fsdp_state_dict 2025-09-07T08:19:26.4658658Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-09-07T08:19:26.4658734Z 2025-09-07T08:19:26.4658812Z 2025-09-07T08:19:26.4658934Z Args: 2025-09-07T08:19:26.4659082Z model (nn.Module): the nn.Module to the model. 2025-09-07T08:19:26.4659288Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-09-07T08:19:26.4659458Z The optimizers that are used to optimize ``model``. 2025-09-07T08:19:26.4659738Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-09-07T08:19:26.4659860Z that belong to the submodules. 2025-09-07T08:19:26.4660033Z options (StateDictOptions): the options to control how 2025-09-07T08:19:26.4660238Z model state_dict and optimizer state_dict should be returned. See 2025-09-07T08:19:26.4660372Z `StateDictOptions` for the details. 2025-09-07T08:19:26.4660453Z 2025-09-07T08:19:26.4660550Z Returns: 2025-09-07T08:19:26.4660743Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-09-07T08:19:26.4660826Z 2025-09-07T08:19:26.4661068Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-09-07T08:19:26.4661149Z 2025-09-07T08:19:26.4661413Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4661494Z 2025-09-07T08:19:26.4661591Z warnings.warn(msg) 2025-09-07T08:19:26.4661710Z 2025-09-07T08:19:26.4661900Z --- Parse Warning: 69 / 146 --- 2025-09-07T08:19:26.4662884Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=69. 2025-09-07T08:19:26.4663150Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4663229Z 2025-09-07T08:19:26.4663435Z Load a checkpoint into a distributed state dict in SPMD style. 2025-09-07T08:19:26.4663516Z 2025-09-07T08:19:26.4663750Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-09-07T08:19:26.4663979Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-09-07T08:19:26.4664258Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-09-07T08:19:26.4664359Z costs). 2025-09-07T08:19:26.4664435Z 2025-09-07T08:19:26.4664621Z Each rank will try to read the least amount of data necessary 2025-09-07T08:19:26.4664849Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-09-07T08:19:26.4665094Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-09-07T08:19:26.4665181Z 2025-09-07T08:19:26.4665437Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T08:19:26.4665696Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-09-07T08:19:26.4665862Z ``load_state_dict`` once the deserialization is complete. 2025-09-07T08:19:26.4666119Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-09-07T08:19:26.4666276Z it in the ``state_dict`` with the deserialized object. 2025-09-07T08:19:26.4666353Z 2025-09-07T08:19:26.4666454Z .. warning:: 2025-09-07T08:19:26.4666622Z All tensors in ``state_dict`` must be allocated on their 2025-09-07T08:19:26.4666784Z destination device *prior to* calling this function. 2025-09-07T08:19:26.4666870Z 2025-09-07T08:19:26.4667095Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-09-07T08:19:26.4667192Z on state_dict. 2025-09-07T08:19:26.4667272Z 2025-09-07T08:19:26.4667356Z .. warning:: 2025-09-07T08:19:26.4667568Z Users must call `load_state_dict` on the root module to ensure load 2025-09-07T08:19:26.4667751Z pos-processing and non-tensor data properly propagates. 2025-09-07T08:19:26.4667836Z 2025-09-07T08:19:26.4667914Z .. note: 2025-09-07T08:19:26.4668162Z If no process group is initialized, this function will assume the intent 2025-09-07T08:19:26.4668415Z is to load a checkpoint into the local process. This can be useful in the 2025-09-07T08:19:26.4668659Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-09-07T08:19:26.4668771Z or ShardedTensor) 2025-09-07T08:19:26.4668848Z 2025-09-07T08:19:26.4668930Z .. note: 2025-09-07T08:19:26.4669077Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T08:19:26.4669156Z 2025-09-07T08:19:26.4669234Z Args: 2025-09-07T08:19:26.4669454Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-09-07T08:19:26.4669596Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T08:19:26.4669808Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T08:19:26.4670008Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T08:19:26.4670172Z It can also be a key if the storage is a key-value store. 2025-09-07T08:19:26.4670279Z (Default: ``None``) 2025-09-07T08:19:26.4670407Z storage_reader (Optional[StorageReader]): 2025-09-07T08:19:26.4670620Z Instance of StorageWriter used to perform reads. If this is not 2025-09-07T08:19:26.4670842Z specified, DCP will automatically infer the reader based on the 2025-09-07T08:19:26.4671042Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T08:19:26.4671158Z be raised. (Default: ``None``) 2025-09-07T08:19:26.4671270Z planner (Optional[LoadPlanner]): 2025-09-07T08:19:26.4671478Z Instance of LoadPlanner. If this is not specified, the default 2025-09-07T08:19:26.4671603Z planner will be used. (Default: ``None``) 2025-09-07T08:19:26.4671728Z process_group (Optional[ProcessGroup]): 2025-09-07T08:19:26.4671921Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T08:19:26.4672013Z (Default: ``None``) 2025-09-07T08:19:26.4672234Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-09-07T08:19:26.4672512Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-09-07T08:19:26.4672598Z Returns: 2025-09-07T08:19:26.4672692Z None. 2025-09-07T08:19:26.4672771Z 2025-09-07T08:19:26.4672860Z Examples 2025-09-07T08:19:26.4672953Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4673052Z >>> my_model = MyModule() 2025-09-07T08:19:26.4673199Z >>> optimizer = Adagrad(my_model.parameters()) 2025-09-07T08:19:26.4673544Z >>> model_state_dict = my_model.state_dict() 2025-09-07T08:19:26.4673795Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-09-07T08:19:26.4673890Z ... "/checkpoint/1" 2025-09-07T08:19:26.4673972Z ... ) 2025-09-07T08:19:26.4674059Z 2025-09-07T08:19:26.4674215Z >>> torch.distributed.checkpoint.load_state_dict( 2025-09-07T08:19:26.4674343Z >>> state_dict=model_state_dict, 2025-09-07T08:19:26.4674460Z >>> storage_reader=fs_storage_reader, 2025-09-07T08:19:26.4674541Z >>> ) 2025-09-07T08:19:26.4674627Z 2025-09-07T08:19:26.4674820Z >>> # module.load_state_dict() function might have customized steps 2025-09-07T08:19:26.4674955Z >>> # to flush the state_dict, must call it to 2025-09-07T08:19:26.4675058Z >>> # ensure correct behavior. 2025-09-07T08:19:26.4675185Z >>> my_model.load_state_dict(model_state_dict) 2025-09-07T08:19:26.4675269Z 2025-09-07T08:19:26.4675352Z .. note:: 2025-09-07T08:19:26.4675557Z load_state_dict uses collectives to coordinate reads across ranks. 2025-09-07T08:19:26.4675776Z For NCCL-based process groups, internal tensor representations of 2025-09-07T08:19:26.4676068Z objects must be moved to the GPU device before communication takes place. 2025-09-07T08:19:26.4676299Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T08:19:26.4676559Z and it is the user's responsibility to ensure that this is set so that each 2025-09-07T08:19:26.4676753Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T08:19:26.4676835Z 2025-09-07T08:19:26.4677085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4677178Z 2025-09-07T08:19:26.4677272Z warnings.warn(msg) 2025-09-07T08:19:26.4677351Z 2025-09-07T08:19:26.4677575Z --- Parse Warning: 70 / 146 --- 2025-09-07T08:19:26.4678541Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=97. 2025-09-07T08:19:26.4678817Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4678895Z 2025-09-07T08:19:26.4679027Z Save a distributed model in SPMD style. 2025-09-07T08:19:26.4679100Z 2025-09-07T08:19:26.4679295Z This function is different from ``torch.save()`` as it handles 2025-09-07T08:19:26.4679593Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-09-07T08:19:26.4679671Z 2025-09-07T08:19:26.4679939Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T08:19:26.4680091Z save will call ``state_dict`` before serialization. 2025-09-07T08:19:26.4680166Z 2025-09-07T08:19:26.4680263Z .. warning:: 2025-09-07T08:19:26.4680497Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-09-07T08:19:26.4680597Z for saved state_dicts. 2025-09-07T08:19:26.4680683Z 2025-09-07T08:19:26.4680766Z .. warning:: 2025-09-07T08:19:26.4680983Z If using the `process_group` argument, make sure that only its ranks 2025-09-07T08:19:26.4681185Z call `save_state_dict` and that all data in state_dict belong to it. 2025-09-07T08:19:26.4681261Z 2025-09-07T08:19:26.4681352Z .. note:: 2025-09-07T08:19:26.4681652Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-09-07T08:19:26.4681922Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-09-07T08:19:26.4682025Z group needs to be passed in. 2025-09-07T08:19:26.4682100Z 2025-09-07T08:19:26.4682189Z .. note:: 2025-09-07T08:19:26.4682451Z If no process group is available, this function assumes the intention is to save the 2025-09-07T08:19:26.4682573Z state_dict in the local process. 2025-09-07T08:19:26.4682646Z 2025-09-07T08:19:26.4682725Z .. note: 2025-09-07T08:19:26.4682871Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T08:19:26.4682950Z 2025-09-07T08:19:26.4683042Z 2025-09-07T08:19:26.4683123Z Args: 2025-09-07T08:19:26.4683274Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T08:19:26.4683432Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T08:19:26.4683640Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T08:19:26.4683854Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T08:19:26.4684016Z It can also be a key if the storage is a key-value store. 2025-09-07T08:19:26.4684184Z (Default: ``None``) 2025-09-07T08:19:26.4684327Z storage_writer (Optional[StorageWriter]): 2025-09-07T08:19:26.4684534Z Instance of StorageWriter used to perform writes. If this is not 2025-09-07T08:19:26.4684841Z specified, DCP will automatically infer the writer based on the 2025-09-07T08:19:26.4685064Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T08:19:26.4685208Z be raised. (Default: ``None``) 2025-09-07T08:19:26.4685332Z planner (Optional[SavePlanner]): 2025-09-07T08:19:26.4685553Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T08:19:26.4685696Z planner will be used. (Default: ``None``) 2025-09-07T08:19:26.4685824Z process_group (Optional[ProcessGroup]): 2025-09-07T08:19:26.4686003Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T08:19:26.4686111Z (Default: ``None``) 2025-09-07T08:19:26.4686200Z no_dist (bool): 2025-09-07T08:19:26.4686379Z If ``True``, this function will assume the intent is to load 2025-09-07T08:19:26.4686504Z a checkpoint on a single rank/process. 2025-09-07T08:19:26.4686600Z (Default: ``False``) 2025-09-07T08:19:26.4686875Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-09-07T08:19:26.4687046Z a checkpoint without using cross-rank synchronization. 2025-09-07T08:19:26.4687143Z (Default: ``True``) 2025-09-07T08:19:26.4687369Z This configuration is experimental and should be used with caution. 2025-09-07T08:19:26.4687636Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T08:19:26.4687767Z 2025-09-07T08:19:26.4687850Z Returns: 2025-09-07T08:19:26.4688014Z Metadata: Metadata object for the saved checkpoint. 2025-09-07T08:19:26.4688090Z 2025-09-07T08:19:26.4688171Z Example: 2025-09-07T08:19:26.4688274Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4688373Z >>> my_model = MyModule() 2025-09-07T08:19:26.4688447Z 2025-09-07T08:19:26.4688567Z >>> state_dict = {"model": my_model} 2025-09-07T08:19:26.4688644Z 2025-09-07T08:19:26.4688884Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T08:19:26.4688977Z ... "/checkpoint/1" 2025-09-07T08:19:26.4689056Z ... ) 2025-09-07T08:19:26.4689196Z >>> torch.distributed.checkpoint.save( 2025-09-07T08:19:26.4689299Z >>> state_dict=state_dict, 2025-09-07T08:19:26.4689427Z >>> storage_writer=fs_storage_writer, 2025-09-07T08:19:26.4689536Z >>> ) 2025-09-07T08:19:26.4689615Z 2025-09-07T08:19:26.4689712Z .. note:: 2025-09-07T08:19:26.4689923Z save_state_dict uses collectives to coordinate writes across ranks. 2025-09-07T08:19:26.4690144Z For NCCL-based process groups, internal tensor representations of 2025-09-07T08:19:26.4690371Z objects must be moved to the GPU device before communication takes place. 2025-09-07T08:19:26.4690586Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T08:19:26.4690804Z and it is the user's responsibility to ensure that this is set so that 2025-09-07T08:19:26.4690997Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T08:19:26.4691083Z 2025-09-07T08:19:26.4691333Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4691411Z 2025-09-07T08:19:26.4691517Z warnings.warn(msg) 2025-09-07T08:19:26.4691595Z 2025-09-07T08:19:26.4691809Z --- Parse Warning: 71 / 146 --- 2025-09-07T08:19:26.4692815Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=230. 2025-09-07T08:19:26.4693076Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4693350Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-09-07T08:19:26.4693636Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-09-07T08:19:26.4693729Z 2025-09-07T08:19:26.4693816Z .. warning:: 2025-09-07T08:19:26.4694001Z This feature is experimental and subject to change. 2025-09-07T08:19:26.4694177Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-09-07T08:19:26.4694250Z 2025-09-07T08:19:26.4694340Z Args: 2025-09-07T08:19:26.4694497Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T08:19:26.4694639Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T08:19:26.4694856Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T08:19:26.4695058Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T08:19:26.4695227Z It can also be a key if the storage is a key-value store. 2025-09-07T08:19:26.4695323Z (Default: ``None``) 2025-09-07T08:19:26.4695454Z storage_writer (Optional[StorageWriter]): 2025-09-07T08:19:26.4695673Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-09-07T08:19:26.4695906Z this is not specified, DCP will automatically infer the writer based on the 2025-09-07T08:19:26.4696115Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T08:19:26.4696224Z be raised. (Default: ``None``) 2025-09-07T08:19:26.4696366Z planner (Optional[SavePlanner]): 2025-09-07T08:19:26.4696568Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T08:19:26.4696693Z planner will be used. (Default: ``None``) 2025-09-07T08:19:26.4696825Z process_group (Optional[ProcessGroup]): 2025-09-07T08:19:26.4697002Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T08:19:26.4697096Z (Default: ``None``) 2025-09-07T08:19:26.4697263Z async_checkpointer_type (AsyncCheckpointerType): 2025-09-07T08:19:26.4697428Z whether to do checkpoint in separate thread or process 2025-09-07T08:19:26.4697580Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-09-07T08:19:26.4697687Z async_stager (AsyncStager): 2025-09-07T08:19:26.4697969Z provides staging implementation. If storage_writer implements AsyncStager 2025-09-07T08:19:26.4698191Z and async_stager is provided, async_stager will be used for staging 2025-09-07T08:19:26.4698280Z no_dist (bool): 2025-09-07T08:19:26.4698458Z If ``True``, this function will assume the intent is to save 2025-09-07T08:19:26.4698580Z a checkpoint on a single rank/process. 2025-09-07T08:19:26.4698675Z (Default: ``False``) 2025-09-07T08:19:26.4698992Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-09-07T08:19:26.4699213Z This configuration is experimental and should be used with caution. 2025-09-07T08:19:26.4699495Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T08:19:26.4699573Z 2025-09-07T08:19:26.4699655Z Returns: 2025-09-07T08:19:26.4699879Z Future: A future holding the resultant Metadata object from `save`. 2025-09-07T08:19:26.4699958Z 2025-09-07T08:19:26.4700047Z Example: 2025-09-07T08:19:26.4700139Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4700234Z >>> my_model = MyModule() 2025-09-07T08:19:26.4700319Z 2025-09-07T08:19:26.4700429Z >>> state_dict = {"model": my_model} 2025-09-07T08:19:26.4700513Z 2025-09-07T08:19:26.4700743Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T08:19:26.4700835Z ... "/checkpoint/1" 2025-09-07T08:19:26.4700918Z ... ) 2025-09-07T08:19:26.4701121Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-09-07T08:19:26.4701232Z >>> state_dict=state_dict, 2025-09-07T08:19:26.4701377Z >>> storage_writer=fs_storage_writer, 2025-09-07T08:19:26.4701483Z >>> ) 2025-09-07T08:19:26.4701570Z >>> 2025-09-07T08:19:26.4701665Z >>> # ... do some work ... 2025-09-07T08:19:26.4701744Z >>> 2025-09-07T08:19:26.4701860Z >>> checkpoint_future.result() 2025-09-07T08:19:26.4701938Z 2025-09-07T08:19:26.4702026Z 2025-09-07T08:19:26.4702278Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4702353Z 2025-09-07T08:19:26.4702455Z warnings.warn(msg) 2025-09-07T08:19:26.4702533Z 2025-09-07T08:19:26.4702730Z --- Parse Warning: 72 / 146 --- 2025-09-07T08:19:26.4703784Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=94. 2025-09-07T08:19:26.4704044Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4704128Z 2025-09-07T08:19:26.4704327Z Initialize rendezvous event object and record its operations. 2025-09-07T08:19:26.4704418Z 2025-09-07T08:19:26.4704525Z Args: 2025-09-07T08:19:26.4704652Z run_id (str): The run id of the rendezvous. 2025-09-07T08:19:26.4704806Z message (str): The message describing the event. 2025-09-07T08:19:26.4705059Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-09-07T08:19:26.4705252Z name (str): Event name. (E.g. Current action being performed). 2025-09-07T08:19:26.4705368Z hostname (str): Hostname of the node. 2025-09-07T08:19:26.4705506Z pid (Optional[int]): The process id of the node. 2025-09-07T08:19:26.4705755Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-09-07T08:19:26.4706023Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-09-07T08:19:26.4706188Z rank (Optional[int]): The rank of the node, if known. 2025-09-07T08:19:26.4706270Z Returns: 2025-09-07T08:19:26.4706377Z None 2025-09-07T08:19:26.4706467Z Example: 2025-09-07T08:19:26.4706593Z >>> # See DynamicRendezvousHandler class 2025-09-07T08:19:26.4706688Z >>> def _record( 2025-09-07T08:19:26.4706767Z ... self, 2025-09-07T08:19:26.4706856Z ... message: str, 2025-09-07T08:19:26.4706997Z ... node_state: NodeState = NodeState.RUNNING, 2025-09-07T08:19:26.4707103Z ... rank: Optional[int] = None, 2025-09-07T08:19:26.4707195Z ... ) -> None: 2025-09-07T08:19:26.4707311Z ... construct_and_record_rdzv_event( 2025-09-07T08:19:26.4707476Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-09-07T08:19:26.4707602Z ... run_id=self._settings.run_id, 2025-09-07T08:19:26.4707696Z ... message=message, 2025-09-07T08:19:26.4707804Z ... node_state=node_state, 2025-09-07T08:19:26.4707922Z ... hostname=self._this_node.addr, 2025-09-07T08:19:26.4708034Z ... pid=self._this_node.pid, 2025-09-07T08:19:26.4708170Z ... local_id=self._this_node.local_id, 2025-09-07T08:19:26.4708259Z ... rank=rank, 2025-09-07T08:19:26.4708348Z ... ) 2025-09-07T08:19:26.4708426Z 2025-09-07T08:19:26.4708675Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4708757Z 2025-09-07T08:19:26.4708852Z warnings.warn(msg) 2025-09-07T08:19:26.4708927Z 2025-09-07T08:19:26.4709121Z --- Parse Warning: 73 / 146 --- 2025-09-07T08:19:26.4710069Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=114. 2025-09-07T08:19:26.4710363Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4710438Z 2025-09-07T08:19:26.4710604Z This configures FSDP-native mixed precision training. 2025-09-07T08:19:26.4710693Z 2025-09-07T08:19:26.4710776Z Attributes: 2025-09-07T08:19:26.4711015Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-09-07T08:19:26.4711216Z parameters during forward and backward and thus the dtype for 2025-09-07T08:19:26.4711431Z forward and backward computation. Outside forward and backward, the 2025-09-07T08:19:26.4711633Z *sharded* parameters are kept in full precision (e.g. for the 2025-09-07T08:19:26.4711836Z optimizer step), and for model checkpointing, the parameters are 2025-09-07T08:19:26.4711995Z always saved in full precision. (Default: ``None``) 2025-09-07T08:19:26.4712208Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T08:19:26.4712427Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-09-07T08:19:26.4712606Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-09-07T08:19:26.4712843Z the ``param_dtype`` value, still running gradient reduction in low 2025-09-07T08:19:26.4713059Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-09-07T08:19:26.4713254Z to force gradient reduction to run in full precision. (Default: 2025-09-07T08:19:26.4713344Z ``None``) 2025-09-07T08:19:26.4713554Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T08:19:26.4713752Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-09-07T08:19:26.4713951Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-09-07T08:19:26.4714160Z dtype thereafter. For model checkpointing, the buffers are saved 2025-09-07T08:19:26.4714346Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-09-07T08:19:26.4714428Z ``None``) 2025-09-07T08:19:26.4714642Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-09-07T08:19:26.4719932Z gradients to full precision after the backward pass in preparation 2025-09-07T08:19:26.4720213Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-09-07T08:19:26.4720414Z in the dtype used for gradient reduction, which can save memory if 2025-09-07T08:19:26.4720620Z using a custom optimizer that supports running in low precision. 2025-09-07T08:19:26.4720731Z (Default: ``False``) 2025-09-07T08:19:26.4720942Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-09-07T08:19:26.4721152Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-09-07T08:19:26.4721371Z that parameter and input dtypes match for forward computation, as 2025-09-07T08:19:26.4721576Z required by many ops. This may need to be set to ``True`` when only 2025-09-07T08:19:26.4721806Z applying mixed precision to some but not all FSDP modules, in which 2025-09-07T08:19:26.4722013Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-09-07T08:19:26.4722114Z (Default: ``False``) 2025-09-07T08:19:26.4722327Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-09-07T08:19:26.4722531Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-09-07T08:19:26.4722714Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-09-07T08:19:26.4722854Z this does not do anything. (Default: ``True``) 2025-09-07T08:19:26.4723081Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-09-07T08:19:26.4723338Z module classes to ignore for mixed precision when using an 2025-09-07T08:19:26.4723561Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-09-07T08:19:26.4723770Z applied to them separately with mixed precision disabled (meaning 2025-09-07T08:19:26.4723971Z that the final FSDP construction would deviate from the specified 2025-09-07T08:19:26.4724268Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-09-07T08:19:26.4724461Z not do anything. This API is experimental and subject to change. 2025-09-07T08:19:26.4724580Z (Default: ``(_BatchNorm,)``) 2025-09-07T08:19:26.4724657Z 2025-09-07T08:19:26.4724825Z .. note:: This API is experimental and subject to change. 2025-09-07T08:19:26.4724917Z 2025-09-07T08:19:26.4725130Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-09-07T08:19:26.4725222Z 2025-09-07T08:19:26.4725402Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-09-07T08:19:26.4725512Z precision, but buffers are not. 2025-09-07T08:19:26.4725602Z 2025-09-07T08:19:26.4725805Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-09-07T08:19:26.4726057Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-09-07T08:19:26.4726285Z Disabling FSDP's mixed precision for those norm modules only means that 2025-09-07T08:19:26.4726496Z the affine parameters are kept in ``float32``. However, this incurs 2025-09-07T08:19:26.4726742Z separate all-gathers and reduce-scatters for those norm modules, which 2025-09-07T08:19:26.4726958Z may be inefficient, so if the workload permits, the user should prefer 2025-09-07T08:19:26.4727110Z to still apply mixed precision to those modules. 2025-09-07T08:19:26.4727186Z 2025-09-07T08:19:26.4727383Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-09-07T08:19:26.4727598Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-09-07T08:19:26.4727822Z modules will have FSDP applied to them separately with mixed precision 2025-09-07T08:19:26.4728033Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-09-07T08:19:26.4728109Z 2025-09-07T08:19:26.4728308Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-09-07T08:19:26.4728526Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-09-07T08:19:26.4728692Z its ``cast_root_forward_inputs`` takes precedence over its 2025-09-07T08:19:26.4728878Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-09-07T08:19:26.4729088Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-09-07T08:19:26.4729315Z sufficient for the typical case where each FSDP instance has the same 2025-09-07T08:19:26.4729536Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-09-07T08:19:26.4729710Z ``param_dtype`` at the beginning of the model's forward pass. 2025-09-07T08:19:26.4729802Z 2025-09-07T08:19:26.4730003Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-09-07T08:19:26.4730252Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-09-07T08:19:26.4730451Z values to configure casting inputs or not before each instance's 2025-09-07T08:19:26.4730640Z forward. In such a case, since the casts happen before each FSDP 2025-09-07T08:19:26.4730865Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-09-07T08:19:26.4731092Z submodules run before its FSDP submodules to avoid the activation dtype 2025-09-07T08:19:26.4731305Z being changed due to a different ``MixedPrecision`` configuration. 2025-09-07T08:19:26.4731384Z 2025-09-07T08:19:26.4731472Z Example:: 2025-09-07T08:19:26.4731558Z 2025-09-07T08:19:26.4731709Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4731909Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-09-07T08:19:26.4732003Z >>> model[1] = FSDP( 2025-09-07T08:19:26.4732090Z >>> model[1], 2025-09-07T08:19:26.4732407Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-09-07T08:19:26.4732485Z >>> ) 2025-09-07T08:19:26.4732587Z >>> model = FSDP( 2025-09-07T08:19:26.4732673Z >>> model, 2025-09-07T08:19:26.4732975Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-09-07T08:19:26.4733066Z >>> ) 2025-09-07T08:19:26.4733142Z 2025-09-07T08:19:26.4733348Z The above shows a working example. On the other hand, if ``model[1]`` 2025-09-07T08:19:26.4733558Z were replaced with ``model[0]``, meaning that the submodule using 2025-09-07T08:19:26.4733778Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-09-07T08:19:26.4734012Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-09-07T08:19:26.4734094Z ones. 2025-09-07T08:19:26.4734205Z 2025-09-07T08:19:26.4734284Z 2025-09-07T08:19:26.4734530Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4734616Z 2025-09-07T08:19:26.4734710Z warnings.warn(msg) 2025-09-07T08:19:26.4734788Z 2025-09-07T08:19:26.4735040Z --- Parse Warning: 74 / 146 --- 2025-09-07T08:19:26.4735997Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=295. 2025-09-07T08:19:26.4736269Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4736353Z 2025-09-07T08:19:26.4736557Z ``FullStateDictConfig`` is a config class meant to be used with 2025-09-07T08:19:26.4736767Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-09-07T08:19:26.4736991Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-09-07T08:19:26.4737227Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-09-07T08:19:26.4737424Z is meant to be used via the :func:`state_dict_type` context manager as 2025-09-07T08:19:26.4737519Z follows: 2025-09-07T08:19:26.4737600Z 2025-09-07T08:19:26.4737724Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4737970Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T08:19:26.4738097Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-09-07T08:19:26.4738295Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-09-07T08:19:26.4738524Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-09-07T08:19:26.4738628Z >>> state = fsdp.state_dict() 2025-09-07T08:19:26.4738850Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-09-07T08:19:26.4739086Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-09-07T08:19:26.4739327Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-09-07T08:19:26.4739428Z >>> if dist.get_rank() == 0: 2025-09-07T08:19:26.4739604Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-09-07T08:19:26.4739753Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-09-07T08:19:26.4739870Z >>> model.load_state_dict(state_dict) 2025-09-07T08:19:26.4740110Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-09-07T08:19:26.4740371Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-09-07T08:19:26.4740461Z >>> fsdp = FSDP( 2025-09-07T08:19:26.4740579Z ... model, 2025-09-07T08:19:26.4740709Z ... device_id=torch.cuda.current_device(), 2025-09-07T08:19:26.4740819Z ... auto_wrap_policy=..., 2025-09-07T08:19:26.4740924Z ... sync_module_states=True, 2025-09-07T08:19:26.4741002Z ... ) 2025-09-07T08:19:26.4741220Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-09-07T08:19:26.4741300Z 2025-09-07T08:19:26.4741383Z Attributes: 2025-09-07T08:19:26.4741591Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-09-07T08:19:26.4741791Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-09-07T08:19:26.4741954Z ranks save the full state dict. (Default: ``False``) 2025-09-07T08:19:26.4742033Z 2025-09-07T08:19:26.4742283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4742369Z 2025-09-07T08:19:26.4742462Z warnings.warn(msg) 2025-09-07T08:19:26.4742550Z 2025-09-07T08:19:26.4742739Z --- Parse Warning: 75 / 146 --- 2025-09-07T08:19:26.4743922Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=634. 2025-09-07T08:19:26.4744220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4744465Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T08:19:26.4744554Z 2025-09-07T08:19:26.4744815Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-09-07T08:19:26.4745030Z The target module does not have to be a FSDP module. If the target 2025-09-07T08:19:26.4745240Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-09-07T08:19:26.4745316Z 2025-09-07T08:19:26.4745541Z .. note:: This API should be called for only the top-level (root) 2025-09-07T08:19:26.4745632Z module. 2025-09-07T08:19:26.4745721Z 2025-09-07T08:19:26.4745928Z .. note:: This API enables users to transparently use the conventional 2025-09-07T08:19:26.4746115Z ``state_dict`` API to take model checkpoints in cases where the 2025-09-07T08:19:26.4746328Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-09-07T08:19:26.4746535Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-09-07T08:19:26.4746771Z instances, while dispatching into `sharded_state_dict` implementation 2025-09-07T08:19:26.4746860Z for FSDP: 2025-09-07T08:19:26.4746939Z 2025-09-07T08:19:26.4747037Z Example:: 2025-09-07T08:19:26.4747113Z 2025-09-07T08:19:26.4747255Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4747362Z >>> model = DDP(FSDP(...)) 2025-09-07T08:19:26.4747474Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:26.4747572Z >>> model, 2025-09-07T08:19:26.4747701Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T08:19:26.4747922Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-09-07T08:19:26.4748145Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-09-07T08:19:26.4748226Z >>> ) 2025-09-07T08:19:26.4748364Z >>> param_state_dict = model.state_dict() 2025-09-07T08:19:26.4748534Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T08:19:26.4748620Z 2025-09-07T08:19:26.4748703Z Args: 2025-09-07T08:19:26.4748855Z module (torch.nn.Module): Root module. 2025-09-07T08:19:26.4749134Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T08:19:26.4749374Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-09-07T08:19:26.4749498Z target ``state_dict_type``. 2025-09-07T08:19:26.4749747Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-09-07T08:19:26.4749864Z for the optimizer state dict. 2025-09-07T08:19:26.4749952Z 2025-09-07T08:19:26.4750033Z Returns: 2025-09-07T08:19:26.4750257Z A StateDictSettings that include the previous state_dict type and 2025-09-07T08:19:26.4750370Z configuration for the module. 2025-09-07T08:19:26.4750448Z 2025-09-07T08:19:26.4750708Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4750786Z 2025-09-07T08:19:26.4750881Z warnings.warn(msg) 2025-09-07T08:19:26.4750970Z 2025-09-07T08:19:26.4751150Z --- Parse Warning: 76 / 146 --- 2025-09-07T08:19:26.4752332Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=792. 2025-09-07T08:19:26.4752617Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4752868Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T08:19:26.4752946Z 2025-09-07T08:19:26.4753260Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-09-07T08:19:26.4753400Z :meth:`set_state_dict_type` for the detail. 2025-09-07T08:19:26.4753479Z 2025-09-07T08:19:26.4753578Z Example:: 2025-09-07T08:19:26.4753655Z 2025-09-07T08:19:26.4753785Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4753900Z >>> model = DDP(FSDP(...)) 2025-09-07T08:19:26.4754038Z >>> with FSDP.state_dict_type( 2025-09-07T08:19:26.4754137Z >>> model, 2025-09-07T08:19:26.4754265Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T08:19:26.4754344Z >>> ): 2025-09-07T08:19:26.4754473Z >>> checkpoint = model.state_dict() 2025-09-07T08:19:26.4754551Z 2025-09-07T08:19:26.4754629Z Args: 2025-09-07T08:19:26.4754766Z module (torch.nn.Module): Root module. 2025-09-07T08:19:26.4754996Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T08:19:26.4755230Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-09-07T08:19:26.4755391Z configuration for the target ``state_dict_type``. 2025-09-07T08:19:26.4755632Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-09-07T08:19:26.4755828Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-09-07T08:19:26.4755911Z 2025-09-07T08:19:26.4756169Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4756247Z 2025-09-07T08:19:26.4756342Z warnings.warn(msg) 2025-09-07T08:19:26.4756431Z 2025-09-07T08:19:26.4756608Z --- Parse Warning: 77 / 146 --- 2025-09-07T08:19:26.4757806Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1805. 2025-09-07T08:19:26.4758095Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4758209Z 2025-09-07T08:19:26.4758448Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-09-07T08:19:26.4758524Z 2025-09-07T08:19:26.4758724Z The given state-dict can be transformed to one of three types: 2025-09-07T08:19:26.4759021Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-09-07T08:19:26.4759100Z 2025-09-07T08:19:26.4759330Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-09-07T08:19:26.4759538Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-09-07T08:19:26.4759631Z avoid OOM. 2025-09-07T08:19:26.4759708Z 2025-09-07T08:19:26.4759951Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-09-07T08:19:26.4760151Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-09-07T08:19:26.4760230Z memory. 2025-09-07T08:19:26.4760319Z 2025-09-07T08:19:26.4760532Z For local state_dict, no transformation will be performed. But a state 2025-09-07T08:19:26.4760780Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-09-07T08:19:26.4760895Z nature (this is not supported yet). 2025-09-07T08:19:26.4760996Z 2025-09-07T08:19:26.4761092Z Example:: 2025-09-07T08:19:26.4761167Z 2025-09-07T08:19:26.4761291Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4761531Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T08:19:26.4761683Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T08:19:26.4761865Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T08:19:26.4762059Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T08:19:26.4762152Z >>> # Save a checkpoint 2025-09-07T08:19:26.4762252Z >>> model, optim = ... 2025-09-07T08:19:26.4762358Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:26.4762446Z >>> model, 2025-09-07T08:19:26.4762560Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T08:19:26.4762711Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T08:19:26.4762864Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T08:19:26.4762946Z >>> ) 2025-09-07T08:19:26.4763060Z >>> state_dict = model.state_dict() 2025-09-07T08:19:26.4763227Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T08:19:26.4763365Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T08:19:26.4763471Z >>> # Load a checkpoint 2025-09-07T08:19:26.4763566Z >>> model, optim = ... 2025-09-07T08:19:26.4763722Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T08:19:26.4763826Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:26.4763906Z >>> model, 2025-09-07T08:19:26.4764032Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T08:19:26.4764237Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T08:19:26.4764384Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T08:19:26.4764467Z >>> ) 2025-09-07T08:19:26.4764579Z >>> model.load_state_dict(state_dict) 2025-09-07T08:19:26.4764738Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T08:19:26.4764852Z >>> model, optim, optim_state_dict 2025-09-07T08:19:26.4764946Z >>> ) 2025-09-07T08:19:26.4765067Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T08:19:26.4765144Z 2025-09-07T08:19:26.4765237Z Args: 2025-09-07T08:19:26.4765428Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T08:19:26.4765625Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T08:19:26.4765772Z were passed into the optimizer ``optim``. 2025-09-07T08:19:26.4765983Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T08:19:26.4766097Z parameters. 2025-09-07T08:19:26.4766319Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-09-07T08:19:26.4766524Z transform. If the value is None, optim.state_dict() will be used. ( 2025-09-07T08:19:26.4766631Z Default: ``None``) 2025-09-07T08:19:26.4766866Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T08:19:26.4767047Z are sharded or ``None`` if using the default process group. ( 2025-09-07T08:19:26.4767153Z Default: ``None``) 2025-09-07T08:19:26.4767228Z 2025-09-07T08:19:26.4767309Z Returns: 2025-09-07T08:19:26.4767504Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-09-07T08:19:26.4767668Z ``model``. The sharding of the optimizer state is based on 2025-09-07T08:19:26.4767776Z ``state_dict_type``. 2025-09-07T08:19:26.4767851Z 2025-09-07T08:19:26.4768103Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4768190Z 2025-09-07T08:19:26.4768282Z warnings.warn(msg) 2025-09-07T08:19:26.4768367Z 2025-09-07T08:19:26.4768561Z --- Parse Warning: 78 / 146 --- 2025-09-07T08:19:26.4769790Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1903. 2025-09-07T08:19:26.4770064Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4770141Z 2025-09-07T08:19:26.4770504Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-09-07T08:19:26.4770579Z 2025-09-07T08:19:26.4770755Z Given a ``optim_state_dict`` that is transformed through 2025-09-07T08:19:26.4770963Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-09-07T08:19:26.4771173Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-09-07T08:19:26.4771394Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-09-07T08:19:26.4771471Z 2025-09-07T08:19:26.4771607Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4771839Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T08:19:26.4771989Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T08:19:26.4772173Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T08:19:26.4772368Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T08:19:26.4772475Z >>> # Save a checkpoint 2025-09-07T08:19:26.4772568Z >>> model, optim = ... 2025-09-07T08:19:26.4772668Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:26.4772760Z >>> model, 2025-09-07T08:19:26.4772874Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T08:19:26.4773001Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T08:19:26.4773155Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T08:19:26.4773422Z >>> ) 2025-09-07T08:19:26.4773548Z >>> state_dict = model.state_dict() 2025-09-07T08:19:26.4773663Z >>> original_osd = optim.state_dict() 2025-09-07T08:19:26.4773790Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-09-07T08:19:26.4773883Z >>> model, 2025-09-07T08:19:26.4773963Z >>> optim, 2025-09-07T08:19:26.4774088Z >>> optim_state_dict=original_osd 2025-09-07T08:19:26.4774168Z >>> ) 2025-09-07T08:19:26.4774311Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T08:19:26.4774416Z >>> # Load a checkpoint 2025-09-07T08:19:26.4774512Z >>> model, optim = ... 2025-09-07T08:19:26.4774733Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T08:19:26.4774882Z >>> FSDP.set_state_dict_type( 2025-09-07T08:19:26.4774962Z >>> model, 2025-09-07T08:19:26.4775095Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T08:19:26.4775223Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T08:19:26.4775374Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T08:19:26.4775451Z >>> ) 2025-09-07T08:19:26.4775561Z >>> model.load_state_dict(state_dict) 2025-09-07T08:19:26.4775716Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T08:19:26.4775824Z >>> model, optim, optim_state_dict 2025-09-07T08:19:26.4775915Z >>> ) 2025-09-07T08:19:26.4776034Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T08:19:26.4776110Z 2025-09-07T08:19:26.4776202Z Args: 2025-09-07T08:19:26.4776394Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T08:19:26.4776592Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T08:19:26.4776734Z were passed into the optimizer ``optim``. 2025-09-07T08:19:26.4776913Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T08:19:26.4777046Z parameters. 2025-09-07T08:19:26.4777252Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-09-07T08:19:26.4777448Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-09-07T08:19:26.4777645Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-09-07T08:19:26.4777816Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-09-07T08:19:26.4778017Z load_directly (bool): If this is set to True, this API will also 2025-09-07T08:19:26.4778211Z call optim.load_state_dict(result) before returning the result. 2025-09-07T08:19:26.4778439Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-09-07T08:19:26.4778534Z (Default: ``False``) 2025-09-07T08:19:26.4778770Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T08:19:26.4778992Z are sharded or ``None`` if using the default process group. ( 2025-09-07T08:19:26.4779085Z Default: ``None``) 2025-09-07T08:19:26.4779174Z 2025-09-07T08:19:26.4779421Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4779493Z 2025-09-07T08:19:26.4779599Z warnings.warn(msg) 2025-09-07T08:19:26.4779675Z 2025-09-07T08:19:26.4779871Z --- Parse Warning: 79 / 146 --- 2025-09-07T08:19:26.4780885Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=129. 2025-09-07T08:19:26.4781145Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4781232Z 2025-09-07T08:19:26.4781454Z RemoteModule instance can only be created after RPC initialization. 2025-09-07T08:19:26.4781545Z 2025-09-07T08:19:26.4781738Z It creates a user-specified module on a specified remote node. 2025-09-07T08:19:26.4781964Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T08:19:26.4782080Z executed on the remote node. 2025-09-07T08:19:26.4782308Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T08:19:26.4782475Z gradients back to the corresponding remote module. 2025-09-07T08:19:26.4782825Z It can be shared across processors using `RPC framework `__, 2025-09-07T08:19:26.4783023Z without incurring any overheads of copying the actual module, 2025-09-07T08:19:26.4783268Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-09-07T08:19:26.4783373Z pointing to the remote module. 2025-09-07T08:19:26.4783472Z 2025-09-07T08:19:26.4783679Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-09-07T08:19:26.4783874Z the ``forward`` method of the module returned by the ``module_cls``. 2025-09-07T08:19:26.4783963Z 2025-09-07T08:19:26.4784266Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-09-07T08:19:26.4784339Z 2025-09-07T08:19:26.4784603Z Particularly, to create a hybrid model, typically the local modules should be 2025-09-07T08:19:26.4784972Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-09-07T08:19:26.4785072Z Hybrid Example: 2025-09-07T08:19:26.4785189Z >>> class HybridModel(nn.Module): 2025-09-07T08:19:26.4785295Z >>> def __init__(self) -> None: 2025-09-07T08:19:26.4785417Z >>> nn.Module.__init__(self) 2025-09-07T08:19:26.4785553Z >>> self.remote_embedding = RemoteModule(...) 2025-09-07T08:19:26.4785684Z >>> self.local_linear = nn.Linear(...) 2025-09-07T08:19:26.4785765Z 2025-09-07T08:19:26.4785983Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T08:19:26.4786241Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T08:19:26.4786444Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-09-07T08:19:26.4786582Z ``def forward(input: Tensor) -> Tensor:`` and 2025-09-07T08:19:26.4786743Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-09-07T08:19:26.4786815Z 2025-09-07T08:19:26.4786908Z .. note:: 2025-09-07T08:19:26.4787045Z If the remote module is placed on a cuda device, 2025-09-07T08:19:26.4787288Z any input CPU tensors will be automatically moved to the same cuda device, 2025-09-07T08:19:26.4787687Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-09-07T08:19:26.4787762Z 2025-09-07T08:19:26.4787851Z Args: 2025-09-07T08:19:26.4788164Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T08:19:26.4788459Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T08:19:26.4788542Z formats: 2025-09-07T08:19:26.4788614Z 2025-09-07T08:19:26.4788760Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T08:19:26.4788904Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T08:19:26.4788986Z 2025-09-07T08:19:26.4789228Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T08:19:26.4789335Z module_cls (nn.Module): For example, 2025-09-07T08:19:26.4789449Z >>> class MyModule(nn.Module): 2025-09-07T08:19:26.4789548Z >>> def forward(input): 2025-09-07T08:19:26.4789657Z >>> return input + 1 2025-09-07T08:19:26.4789735Z >>> 2025-09-07T08:19:26.4789829Z >>> module_cls = MyModule 2025-09-07T08:19:26.4790036Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T08:19:26.4790223Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T08:19:26.4790502Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T08:19:26.4790730Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T08:19:26.4790939Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T08:19:26.4791179Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T08:19:26.4791252Z 2025-09-07T08:19:26.4791340Z Returns: 2025-09-07T08:19:26.4791604Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T08:19:26.4791849Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T08:19:26.4792126Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T08:19:26.4792267Z on the user-provided module on the remote side. 2025-09-07T08:19:26.4792346Z 2025-09-07T08:19:26.4792430Z Example:: 2025-09-07T08:19:26.4792575Z Run the following code in two different processes: 2025-09-07T08:19:26.4792659Z 2025-09-07T08:19:26.4792769Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:26.4792867Z >>> # On worker 0: 2025-09-07T08:19:26.4792956Z >>> import torch 2025-09-07T08:19:26.4793073Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4793186Z >>> from torch import nn, Tensor 2025-09-07T08:19:26.4793402Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T08:19:26.4793487Z >>> 2025-09-07T08:19:26.4793622Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T08:19:26.4793742Z >>> remote_linear_module = RemoteModule( 2025-09-07T08:19:26.4793900Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T08:19:26.4793977Z >>> ) 2025-09-07T08:19:26.4794083Z >>> input = torch.randn(128, 20) 2025-09-07T08:19:26.4794246Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T08:19:26.4794340Z >>> ret = ret_fut.wait() 2025-09-07T08:19:26.4794441Z >>> rpc.shutdown() 2025-09-07T08:19:26.4794516Z 2025-09-07T08:19:26.4794599Z >>> # On worker 1: 2025-09-07T08:19:26.4794691Z >>> import torch 2025-09-07T08:19:26.4794812Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4794895Z >>> 2025-09-07T08:19:26.4795028Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T08:19:26.4795118Z >>> rpc.shutdown() 2025-09-07T08:19:26.4795202Z 2025-09-07T08:19:26.4795451Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4795532Z 2025-09-07T08:19:26.4795650Z warnings.warn(msg) 2025-09-07T08:19:26.4795726Z 2025-09-07T08:19:26.4795927Z --- Parse Warning: 80 / 146 --- 2025-09-07T08:19:26.4796987Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=506. 2025-09-07T08:19:26.4797256Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4797332Z 2025-09-07T08:19:26.4797649Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-09-07T08:19:26.4797734Z 2025-09-07T08:19:26.4798054Z This alternate initialization method can be particularly useful if we want to create multiple 2025-09-07T08:19:26.4798379Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-09-07T08:19:26.4798459Z 2025-09-07T08:19:26.4798731Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-09-07T08:19:26.4798924Z which is not supported. The recommended way is as follows: 2025-09-07T08:19:26.4798999Z 2025-09-07T08:19:26.4799126Z 1. the sender creates a RemoteModule; 2025-09-07T08:19:26.4799265Z 2. the sender sends its ``module_rref`` over RPC; 2025-09-07T08:19:26.4799598Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-09-07T08:19:26.4799682Z 2025-09-07T08:19:26.4799764Z Example:: 2025-09-07T08:19:26.4799919Z Run the following code in two different processes: 2025-09-07T08:19:26.4799993Z 2025-09-07T08:19:26.4800128Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:26.4800227Z >>> # On worker 0: 2025-09-07T08:19:26.4800338Z >>> import torch 2025-09-07T08:19:26.4800457Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4800574Z >>> from torch import nn, Tensor 2025-09-07T08:19:26.4800787Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T08:19:26.4800871Z >>> 2025-09-07T08:19:26.4801003Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T08:19:26.4801106Z >>> remote_module = RemoteModule( 2025-09-07T08:19:26.4801236Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T08:19:26.4801309Z >>> ) 2025-09-07T08:19:26.4801392Z >>> 2025-09-07T08:19:26.4801497Z >>> remote_module1 = rpc.rpc_sync( 2025-09-07T08:19:26.4801585Z >>> "worker1/cpu", 2025-09-07T08:19:26.4801713Z >>> RemoteModule.init_from_module_rref, 2025-09-07T08:19:26.4801865Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-09-07T08:19:26.4801955Z >>> ) 2025-09-07T08:19:26.4802047Z >>> rpc.shutdown() 2025-09-07T08:19:26.4802121Z 2025-09-07T08:19:26.4802218Z >>> # On worker 1: 2025-09-07T08:19:26.4802361Z >>> import torch 2025-09-07T08:19:26.4802491Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4802567Z >>> 2025-09-07T08:19:26.4802696Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T08:19:26.4802799Z >>> rpc.shutdown() 2025-09-07T08:19:26.4802872Z 2025-09-07T08:19:26.4802949Z Args: 2025-09-07T08:19:26.4803248Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T08:19:26.4803533Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T08:19:26.4803628Z formats: 2025-09-07T08:19:26.4803702Z 2025-09-07T08:19:26.4803838Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T08:19:26.4804000Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T08:19:26.4804072Z 2025-09-07T08:19:26.4804445Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T08:19:26.4804695Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-09-07T08:19:26.4804797Z the created remote module. 2025-09-07T08:19:26.4805081Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T08:19:26.4805305Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T08:19:26.4805531Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T08:19:26.4805758Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T08:19:26.4805831Z 2025-09-07T08:19:26.4805919Z Returns: 2025-09-07T08:19:26.4806159Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T08:19:26.4806401Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-09-07T08:19:26.4806670Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T08:19:26.4806822Z on the user-provided module on the remote side. 2025-09-07T08:19:26.4806896Z 2025-09-07T08:19:26.4807142Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4807230Z 2025-09-07T08:19:26.4807321Z warnings.warn(msg) 2025-09-07T08:19:26.4807394Z 2025-09-07T08:19:26.4807602Z --- Parse Warning: 81 / 146 --- 2025-09-07T08:19:26.4808604Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=598. 2025-09-07T08:19:26.4808875Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4808974Z 2025-09-07T08:19:26.4809210Z A RemoteModule instance can only be created after RPC initialization. 2025-09-07T08:19:26.4809288Z 2025-09-07T08:19:26.4809481Z It creates a user-specified module on a specified remote node. 2025-09-07T08:19:26.4809721Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T08:19:26.4809820Z executed on the remote node. 2025-09-07T08:19:26.4810059Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T08:19:26.4810212Z gradients back to the corresponding remote module. 2025-09-07T08:19:26.4810284Z 2025-09-07T08:19:26.4810504Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-09-07T08:19:26.4810712Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-09-07T08:19:26.4810964Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-09-07T08:19:26.4811159Z and ``forward`` are the same as the ``forward`` method of the module 2025-09-07T08:19:26.4811284Z returned by the ``module_cls``. 2025-09-07T08:19:26.4811365Z 2025-09-07T08:19:26.4811557Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T08:19:26.4811807Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T08:19:26.4812026Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-09-07T08:19:26.4812098Z 2025-09-07T08:19:26.4812226Z | ``def forward(input: Tensor) -> Tensor:`` 2025-09-07T08:19:26.4812382Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-09-07T08:19:26.4812463Z 2025-09-07T08:19:26.4812542Z Args: 2025-09-07T08:19:26.4812834Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T08:19:26.4813185Z The format should be "/", where the device field can be parsed as torch.device type. 2025-09-07T08:19:26.4813362Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-09-07T08:19:26.4813610Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T08:19:26.4813854Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-09-07T08:19:26.4813928Z 2025-09-07T08:19:26.4814043Z >>> class MyModule(nn.Module): 2025-09-07T08:19:26.4814141Z >>> def forward(input): 2025-09-07T08:19:26.4814249Z >>> return input + 1 2025-09-07T08:19:26.4814329Z >>> 2025-09-07T08:19:26.4814424Z >>> module_cls = MyModule 2025-09-07T08:19:26.4814508Z 2025-09-07T08:19:26.4814705Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T08:19:26.4814887Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T08:19:26.4814978Z 2025-09-07T08:19:26.4815059Z Returns: 2025-09-07T08:19:26.4815307Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T08:19:26.4815532Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T08:19:26.4815797Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T08:19:26.4815948Z on the user-provided module on the remote side. 2025-09-07T08:19:26.4816021Z 2025-09-07T08:19:26.4816119Z Example:: 2025-09-07T08:19:26.4816271Z Run the following code in two different processes: 2025-09-07T08:19:26.4816345Z 2025-09-07T08:19:26.4816469Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:26.4816559Z >>> # On worker 0: 2025-09-07T08:19:26.4816657Z >>> import torch 2025-09-07T08:19:26.4816806Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4816930Z >>> from torch import nn, Tensor 2025-09-07T08:19:26.4817160Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T08:19:26.4817243Z >>> 2025-09-07T08:19:26.4817391Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T08:19:26.4817511Z >>> remote_linear_module = RemoteModule( 2025-09-07T08:19:26.4817633Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T08:19:26.4817724Z >>> ) 2025-09-07T08:19:26.4817825Z >>> input = torch.randn(128, 20) 2025-09-07T08:19:26.4817986Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T08:19:26.4818081Z >>> ret = ret_fut.wait() 2025-09-07T08:19:26.4818169Z >>> rpc.shutdown() 2025-09-07T08:19:26.4818257Z 2025-09-07T08:19:26.4818342Z >>> # On worker 1: 2025-09-07T08:19:26.4818430Z >>> import torch 2025-09-07T08:19:26.4818565Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4818639Z >>> 2025-09-07T08:19:26.4818781Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T08:19:26.4818869Z >>> rpc.shutdown() 2025-09-07T08:19:26.4818943Z 2025-09-07T08:19:26.4819165Z Furthermore, a more practical example that is combined with 2025-09-07T08:19:26.4819637Z `DistributedDataParallel `__ (DDP) 2025-09-07T08:19:26.4819973Z can be found in this `tutorial `__. 2025-09-07T08:19:26.4820047Z 2025-09-07T08:19:26.4820295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4820381Z 2025-09-07T08:19:26.4820468Z warnings.warn(msg) 2025-09-07T08:19:26.4820552Z 2025-09-07T08:19:26.4820734Z --- Parse Warning: 82 / 146 --- 2025-09-07T08:19:26.4821726Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-09-07T08:19:26.4822027Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4822100Z 2025-09-07T08:19:26.4822352Z DistributedOptimizer takes remote references to parameters scattered 2025-09-07T08:19:26.4822590Z across workers and applies the given optimizer locally for each parameter. 2025-09-07T08:19:26.4822664Z 2025-09-07T08:19:26.4822903Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-09-07T08:19:26.4823048Z to retrieve the gradients for specific parameters. 2025-09-07T08:19:26.4823132Z 2025-09-07T08:19:26.4823225Z Concurrent calls to 2025-09-07T08:19:26.4823424Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-09-07T08:19:26.4823578Z either from the same or different clients, will 2025-09-07T08:19:26.4823796Z be serialized on each worker -- as each worker's optimizer can only work 2025-09-07T08:19:26.4824011Z on one set of gradients at a time. However, there is no guarantee that 2025-09-07T08:19:26.4824257Z the full forward-backward-optimizer sequence will execute for one client 2025-09-07T08:19:26.4824467Z at a time. This means that the gradients being applied may not correspond 2025-09-07T08:19:26.4824690Z to the latest forward pass executed on a given worker. Also, there is no 2025-09-07T08:19:26.4824796Z guaranteed ordering across workers. 2025-09-07T08:19:26.4824879Z 2025-09-07T08:19:26.4825134Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-09-07T08:19:26.4825353Z by default, so that optimizer updates are not blocked by the Python Global 2025-09-07T08:19:26.4825599Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-09-07T08:19:26.4825856Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-09-07T08:19:26.4826135Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-09-07T08:19:26.4826241Z for your own custom optimizers. 2025-09-07T08:19:26.4826315Z 2025-09-07T08:19:26.4826405Z Args: 2025-09-07T08:19:26.4826595Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-09-07T08:19:26.4826707Z instantiate on each worker. 2025-09-07T08:19:26.4826911Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-09-07T08:19:26.4826996Z to optimize. 2025-09-07T08:19:26.4827211Z args: arguments to pass to the optimizer constructor on each worker. 2025-09-07T08:19:26.4827427Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-09-07T08:19:26.4827510Z 2025-09-07T08:19:26.4827596Z Example:: 2025-09-07T08:19:26.4827707Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:26.4827875Z >>> import torch.distributed.autograd as dist_autograd 2025-09-07T08:19:26.4827997Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4828110Z >>> from torch import optim 2025-09-07T08:19:26.4828318Z >>> from torch.distributed.optim import DistributedOptimizer 2025-09-07T08:19:26.4828393Z >>> 2025-09-07T08:19:26.4828538Z >>> with dist_autograd.context() as context_id: 2025-09-07T08:19:26.4828630Z >>> # Forward pass. 2025-09-07T08:19:26.4828833Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-09-07T08:19:26.4829024Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-09-07T08:19:26.4829143Z >>> loss = rref1.to_here() + rref2.to_here() 2025-09-07T08:19:26.4829230Z >>> 2025-09-07T08:19:26.4829322Z >>> # Backward pass. 2025-09-07T08:19:26.4829475Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-09-07T08:19:26.4829552Z >>> 2025-09-07T08:19:26.4829638Z >>> # Optimizer. 2025-09-07T08:19:26.4829764Z >>> dist_optim = DistributedOptimizer( 2025-09-07T08:19:26.4829873Z >>> optim.SGD, 2025-09-07T08:19:26.4829974Z >>> [rref1, rref2], 2025-09-07T08:19:26.4830058Z >>> lr=0.05, 2025-09-07T08:19:26.4830133Z >>> ) 2025-09-07T08:19:26.4830246Z >>> dist_optim.step(context_id) 2025-09-07T08:19:26.4830319Z 2025-09-07T08:19:26.4830465Z __ https://github.com/pytorch/tutorials/pull/1465 2025-09-07T08:19:26.4830548Z 2025-09-07T08:19:26.4830796Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4830878Z 2025-09-07T08:19:26.4830968Z warnings.warn(msg) 2025-09-07T08:19:26.4831037Z 2025-09-07T08:19:26.4831228Z --- Parse Warning: 83 / 146 --- 2025-09-07T08:19:26.4832287Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2025-09-07T08:19:26.4832560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4832635Z 2025-09-07T08:19:26.4833021Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-09-07T08:19:26.4833180Z This optimizer runs local optimizer at every step. 2025-09-07T08:19:26.4833507Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-09-07T08:19:26.4833592Z 2025-09-07T08:19:26.4833672Z Args: 2025-09-07T08:19:26.4833770Z optim: The local optimizer. 2025-09-07T08:19:26.4833991Z averager: A model averager instance to run post-localSGD algorithm. 2025-09-07T08:19:26.4834064Z 2025-09-07T08:19:26.4834186Z Example:: 2025-09-07T08:19:26.4834263Z 2025-09-07T08:19:26.4834410Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.4834511Z >>> import torch 2025-09-07T08:19:26.4834622Z >>> import torch.distributed as dist 2025-09-07T08:19:26.4834900Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T08:19:26.4834997Z >>> import torch.nn as nn 2025-09-07T08:19:26.4835183Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-09-07T08:19:26.4835463Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T08:19:26.4835560Z >>> PostLocalSGDState, 2025-09-07T08:19:26.4835669Z >>> post_localSGD_hook, 2025-09-07T08:19:26.4835745Z >>> ) 2025-09-07T08:19:26.4835817Z >>> 2025-09-07T08:19:26.4835977Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T08:19:26.4836114Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T08:19:26.4836194Z >>> ) 2025-09-07T08:19:26.4836271Z >>> 2025-09-07T08:19:26.4836411Z >>> # Register a post-localSGD communication hook. 2025-09-07T08:19:26.4836709Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T08:19:26.4836891Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T08:19:26.4836972Z >>> 2025-09-07T08:19:26.4837173Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-09-07T08:19:26.4837415Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-09-07T08:19:26.4837581Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T08:19:26.4837788Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-09-07T08:19:26.4837906Z >>> opt = PostLocalSGDOptimizer( 2025-09-07T08:19:26.4838001Z >>> optim=local_optim, 2025-09-07T08:19:26.4838243Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T08:19:26.4838335Z >>> ) 2025-09-07T08:19:26.4838413Z >>> 2025-09-07T08:19:26.4838659Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-09-07T08:19:26.4838959Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-09-07T08:19:26.4839327Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-09-07T08:19:26.4839433Z >>> for step in range(0, 200): 2025-09-07T08:19:26.4839521Z >>> opt.zero_grad() 2025-09-07T08:19:26.4839634Z >>> loss = loss_fn(output, labels) 2025-09-07T08:19:26.4839725Z >>> loss.backward() 2025-09-07T08:19:26.4839808Z >>> opt.step() 2025-09-07T08:19:26.4839891Z 2025-09-07T08:19:26.4840141Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4840213Z 2025-09-07T08:19:26.4840318Z warnings.warn(msg) 2025-09-07T08:19:26.4840389Z 2025-09-07T08:19:26.4840581Z --- Parse Warning: 84 / 146 --- 2025-09-07T08:19:26.4841676Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=284. 2025-09-07T08:19:26.4841945Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4842021Z 2025-09-07T08:19:26.4842419Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-09-07T08:19:26.4842501Z 2025-09-07T08:19:26.4842749Z The sharing is done as described by `ZeRO `_. 2025-09-07T08:19:26.4842835Z 2025-09-07T08:19:26.4843006Z The local optimizer instance in each rank is only 2025-09-07T08:19:26.4843271Z responsible for updating approximately ``1 / world_size`` parameters and 2025-09-07T08:19:26.4843477Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-09-07T08:19:26.4843721Z parameters are updated locally, each rank will broadcast its parameters to 2025-09-07T08:19:26.4843913Z all other peers to keep all model replicas in the same state. 2025-09-07T08:19:26.4844192Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-09-07T08:19:26.4844458Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-09-07T08:19:26.4844564Z memory consumption. 2025-09-07T08:19:26.4844645Z 2025-09-07T08:19:26.4844913Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-09-07T08:19:26.4845141Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-09-07T08:19:26.4845383Z not divided among ranks. The partition is arbitrary and might not match the 2025-09-07T08:19:26.4845524Z the parameter registration or usage order. 2025-09-07T08:19:26.4845603Z 2025-09-07T08:19:26.4845696Z Arguments: 2025-09-07T08:19:26.4845887Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-09-07T08:19:26.4846113Z or :class:`dict` s giving all parameters, which will be sharded 2025-09-07T08:19:26.4846210Z across ranks. 2025-09-07T08:19:26.4846287Z 2025-09-07T08:19:26.4846375Z Keyword Args: 2025-09-07T08:19:26.4846611Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-09-07T08:19:26.4846699Z optimizer. 2025-09-07T08:19:26.4846914Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-09-07T08:19:26.4847110Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-09-07T08:19:26.4847268Z :meth:`torch.distributed.init_process_group`). 2025-09-07T08:19:26.4847496Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-09-07T08:19:26.4847707Z packed into buckets to speed up communication, and ``param.data`` 2025-09-07T08:19:26.4847937Z fields point to bucket views at different offsets; if ``False``, 2025-09-07T08:19:26.4848138Z each individual parameter is communicated separately, and each 2025-09-07T08:19:26.4848298Z ``params.data`` stays intact (default: ``False``). 2025-09-07T08:19:26.4848489Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-09-07T08:19:26.4848687Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-09-07T08:19:26.4848907Z synchronization; this requires (1) either a functional optimizer 2025-09-07T08:19:26.4849086Z for the ``optimizer_class`` argument or one with a functional 2025-09-07T08:19:26.4849269Z equivalent and (2) registering a DDP communication hook 2025-09-07T08:19:26.4849468Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-09-07T08:19:26.4849633Z parameters are packed into buckets matching those in 2025-09-07T08:19:26.4849797Z :class:`DistributedDataParallel`, meaning that the 2025-09-07T08:19:26.4849947Z ``parameters_as_bucket_view`` argument is ignored. 2025-09-07T08:19:26.4850136Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-09-07T08:19:26.4850226Z (per normal). 2025-09-07T08:19:26.4850321Z (default: ``False``) 2025-09-07T08:19:26.4850541Z **defaults: any trailing arguments, which are forwarded to the local 2025-09-07T08:19:26.4850632Z optimizer. 2025-09-07T08:19:26.4850718Z 2025-09-07T08:19:26.4850803Z Example:: 2025-09-07T08:19:26.4850879Z 2025-09-07T08:19:26.4850983Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4851080Z >>> import torch.nn as nn 2025-09-07T08:19:26.4851316Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-09-07T08:19:26.4851534Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T08:19:26.4851757Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-09-07T08:19:26.4851885Z >>> ddp = DDP(model, device_ids=[rank]) 2025-09-07T08:19:26.4851999Z >>> opt = ZeroRedundancyOptimizer( 2025-09-07T08:19:26.4852104Z >>> ddp.parameters(), 2025-09-07T08:19:26.4852223Z >>> optimizer_class=torch.optim.Adam, 2025-09-07T08:19:26.4852304Z >>> lr=0.01 2025-09-07T08:19:26.4852391Z >>> ) 2025-09-07T08:19:26.4852495Z >>> ddp(inputs).sum().backward() 2025-09-07T08:19:26.4852586Z >>> opt.step() 2025-09-07T08:19:26.4852675Z 2025-09-07T08:19:26.4852760Z .. warning:: 2025-09-07T08:19:26.4852967Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-09-07T08:19:26.4853110Z passed-in parameters are the same dense type. 2025-09-07T08:19:26.4853186Z 2025-09-07T08:19:26.4853276Z .. warning:: 2025-09-07T08:19:26.4853483Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-09-07T08:19:26.4853692Z the way that overlapping :class:`DistributedDataParallel` with 2025-09-07T08:19:26.4853948Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-09-07T08:19:26.4854165Z two or three training iterations do not perform parameter updates in 2025-09-07T08:19:26.4854358Z the optimizer step, depending on if ``static_graph=False`` or 2025-09-07T08:19:26.4854538Z ``static_graph=True``, respectively. This is because it needs 2025-09-07T08:19:26.4854734Z information about the gradient bucketing strategy used by 2025-09-07T08:19:26.4854948Z :class:`DistributedDataParallel`, which is not finalized until the 2025-09-07T08:19:26.4855145Z second forward pass if ``static_graph=False`` or until the third 2025-09-07T08:19:26.4855356Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-09-07T08:19:26.4855464Z is to prepend dummy inputs. 2025-09-07T08:19:26.4855548Z 2025-09-07T08:19:26.4855823Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-09-07T08:19:26.4855901Z 2025-09-07T08:19:26.4856158Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4856236Z 2025-09-07T08:19:26.4856332Z warnings.warn(msg) 2025-09-07T08:19:26.4856409Z 2025-09-07T08:19:26.4856605Z --- Parse Warning: 85 / 146 --- 2025-09-07T08:19:26.4857601Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-09-07T08:19:26.4857865Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4857958Z 2025-09-07T08:19:26.4858188Z Custom reducer class that can be used to specify a custom operation that 2025-09-07T08:19:26.4858364Z reduces losses of multiple microbatches into one value. 2025-09-07T08:19:26.4858458Z 2025-09-07T08:19:26.4858543Z Example: 2025-09-07T08:19:26.4858646Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4858753Z >>> sum_reducer = _CustomReducer( 2025-09-07T08:19:26.4858849Z >>> torch.tensor(0.0), 2025-09-07T08:19:26.4858953Z >>> lambda a, b: a + b 2025-09-07T08:19:26.4859031Z >>> ) 2025-09-07T08:19:26.4859118Z 2025-09-07T08:19:26.4859366Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4859441Z 2025-09-07T08:19:26.4859543Z warnings.warn(msg) 2025-09-07T08:19:26.4859619Z 2025-09-07T08:19:26.4859795Z --- Parse Warning: 86 / 146 --- 2025-09-07T08:19:26.4860761Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6. 2025-09-07T08:19:26.4861046Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4861135Z 2025-09-07T08:19:26.4861374Z A decorator for a function indicating that the return value of the function 2025-09-07T08:19:26.4861593Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-09-07T08:19:26.4861832Z function can run asynchronously on the RPC callee. More specifically, the 2025-09-07T08:19:26.4862066Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-09-07T08:19:26.4862305Z function and installs subsequent processing steps as a callback to that 2025-09-07T08:19:26.4862534Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-09-07T08:19:26.4862744Z from the :class:`~torch.futures.Future` when completed and send the 2025-09-07T08:19:26.4862923Z value back as the RPC response. That also means the returned 2025-09-07T08:19:26.4863150Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-09-07T08:19:26.4863400Z sent through RPC. This decorator is useful when the wrapped function's 2025-09-07T08:19:26.4863595Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-09-07T08:19:26.4863823Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-09-07T08:19:26.4863899Z 2025-09-07T08:19:26.4864111Z .. note:: To enable asynchronous execution, applications must pass the 2025-09-07T08:19:26.4864346Z function object returned by this decorator to RPC APIs. If RPC detected 2025-09-07T08:19:26.4864562Z attributes installed by this decorator, it knows that this function 2025-09-07T08:19:26.4864751Z returns a ``Future`` object and will handle that accordingly. 2025-09-07T08:19:26.4864964Z However, this does not mean this decorator has to be outmost one when 2025-09-07T08:19:26.4865194Z defining a function. For example, when combined with ``@staticmethod`` 2025-09-07T08:19:26.4865427Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-09-07T08:19:26.4865648Z inner decorator to allow the target function be recognized as a static 2025-09-07T08:19:26.4865881Z or class function. This target function can still execute asynchronously 2025-09-07T08:19:26.4866100Z because, when accessed, the static or class method preserves attributes 2025-09-07T08:19:26.4866258Z installed by ``@rpc.functions.async_execution``. 2025-09-07T08:19:26.4866336Z 2025-09-07T08:19:26.4866412Z 2025-09-07T08:19:26.4866502Z Example:: 2025-09-07T08:19:26.4866698Z The returned :class:`~torch.futures.Future` object can come from 2025-09-07T08:19:26.4866839Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-09-07T08:19:26.4867062Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-09-07T08:19:26.4867236Z constructor. The example below shows directly using the 2025-09-07T08:19:26.4867375Z :class:`~torch.futures.Future` returned by 2025-09-07T08:19:26.4867496Z :meth:`~torch.futures.Future.then`. 2025-09-07T08:19:26.4867581Z 2025-09-07T08:19:26.4867700Z >>> from torch.distributed import rpc 2025-09-07T08:19:26.4867779Z >>> 2025-09-07T08:19:26.4867900Z >>> # omitting setup and shutdown RPC 2025-09-07T08:19:26.4867977Z >>> 2025-09-07T08:19:26.4868067Z >>> # On all workers 2025-09-07T08:19:26.4868186Z >>> @rpc.functions.async_execution 2025-09-07T08:19:26.4868297Z >>> def async_add_chained(to, x, y, z): 2025-09-07T08:19:26.4868494Z >>> # This function runs on "worker1" and returns immediately when 2025-09-07T08:19:26.4868702Z >>> # the callback is installed through the `then(cb)` API. In the 2025-09-07T08:19:26.4868879Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-09-07T08:19:26.4869070Z >>> # When the return value of that `rpc_async` arrives at 2025-09-07T08:19:26.4869253Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-09-07T08:19:26.4869444Z >>> # and set the value for the previously returned `Future`, which 2025-09-07T08:19:26.4869624Z >>> # will then trigger RPC to send the result back to "worker0". 2025-09-07T08:19:26.4869793Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T08:19:26.4869901Z >>> lambda fut: fut.wait() + z 2025-09-07T08:19:26.4869981Z >>> ) 2025-09-07T08:19:26.4870070Z >>> 2025-09-07T08:19:26.4870154Z >>> # On worker0 2025-09-07T08:19:26.4870249Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4870354Z >>> ret = rpc.rpc_sync( 2025-09-07T08:19:26.4870442Z >>> "worker1", 2025-09-07T08:19:26.4870547Z >>> async_add_chained, 2025-09-07T08:19:26.4870670Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-09-07T08:19:26.4870746Z >>> ) 2025-09-07T08:19:26.4870872Z >>> print(ret) # prints tensor([3., 3.]) 2025-09-07T08:19:26.4870971Z 2025-09-07T08:19:26.4871202Z When combined with TorchScript decorators, this decorator must be the 2025-09-07T08:19:26.4871285Z outmost one. 2025-09-07T08:19:26.4871357Z 2025-09-07T08:19:26.4871469Z >>> from torch import Tensor 2025-09-07T08:19:26.4871579Z >>> from torch.futures import Future 2025-09-07T08:19:26.4871706Z >>> from torch.distributed import rpc 2025-09-07T08:19:26.4871784Z >>> 2025-09-07T08:19:26.4871892Z >>> # omitting setup and shutdown RPC 2025-09-07T08:19:26.4871976Z >>> 2025-09-07T08:19:26.4872065Z >>> # On all workers 2025-09-07T08:19:26.4872158Z >>> @torch.jit.script 2025-09-07T08:19:26.4872310Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T08:19:26.4872402Z >>> return x + y 2025-09-07T08:19:26.4872493Z >>> 2025-09-07T08:19:26.4872628Z >>> @rpc.functions.async_execution 2025-09-07T08:19:26.4872723Z >>> @torch.jit.script 2025-09-07T08:19:26.4872917Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-09-07T08:19:26.4873055Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-09-07T08:19:26.4873146Z >>> 2025-09-07T08:19:26.4873228Z >>> # On worker0 2025-09-07T08:19:26.4873763Z >>> ret = rpc.rpc_sync( 2025-09-07T08:19:26.4873867Z >>> "worker1", 2025-09-07T08:19:26.4873951Z >>> async_add, 2025-09-07T08:19:26.4874079Z >>> args=("worker2", torch.ones(2), 1) 2025-09-07T08:19:26.4874156Z >>> ) 2025-09-07T08:19:26.4874272Z >>> print(ret) # prints tensor([2., 2.]) 2025-09-07T08:19:26.4874362Z 2025-09-07T08:19:26.4874584Z When combined with static or class method, this decorator must be the 2025-09-07T08:19:26.4874670Z inner one. 2025-09-07T08:19:26.4874755Z 2025-09-07T08:19:26.4874876Z >>> from torch.distributed import rpc 2025-09-07T08:19:26.4874971Z >>> 2025-09-07T08:19:26.4875089Z >>> # omitting setup and shutdown RPC 2025-09-07T08:19:26.4875169Z >>> 2025-09-07T08:19:26.4875274Z >>> # On all workers 2025-09-07T08:19:26.4875384Z >>> class AsyncExecutionClass: 2025-09-07T08:19:26.4875474Z >>> 2025-09-07T08:19:26.4875562Z >>> @staticmethod 2025-09-07T08:19:26.4875677Z >>> @rpc.functions.async_execution 2025-09-07T08:19:26.4875800Z >>> def static_async_add(to, x, y, z): 2025-09-07T08:19:26.4875969Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T08:19:26.4876092Z >>> lambda fut: fut.wait() + z 2025-09-07T08:19:26.4876171Z >>> ) 2025-09-07T08:19:26.4876318Z >>> 2025-09-07T08:19:26.4876420Z >>> @classmethod 2025-09-07T08:19:26.4876587Z >>> @rpc.functions.async_execution 2025-09-07T08:19:26.4876711Z >>> def class_async_add(cls, to, x, y, z): 2025-09-07T08:19:26.4876842Z >>> ret_fut = torch.futures.Future() 2025-09-07T08:19:26.4876987Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T08:19:26.4877145Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-09-07T08:19:26.4877227Z >>> ) 2025-09-07T08:19:26.4877320Z >>> return ret_fut 2025-09-07T08:19:26.4877408Z >>> 2025-09-07T08:19:26.4877521Z >>> @rpc.functions.async_execution 2025-09-07T08:19:26.4877655Z >>> def bound_async_add(self, to, x, y, z): 2025-09-07T08:19:26.4877819Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T08:19:26.4877930Z >>> lambda fut: fut.wait() + z 2025-09-07T08:19:26.4878022Z >>> ) 2025-09-07T08:19:26.4878102Z >>> 2025-09-07T08:19:26.4878194Z >>> # On worker0 2025-09-07T08:19:26.4878287Z >>> ret = rpc.rpc_sync( 2025-09-07T08:19:26.4878374Z >>> "worker1", 2025-09-07T08:19:26.4878558Z >>> AsyncExecutionClass.static_async_add, 2025-09-07T08:19:26.4878672Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T08:19:26.4878758Z >>> ) 2025-09-07T08:19:26.4878873Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:26.4878950Z >>> 2025-09-07T08:19:26.4879051Z >>> ret = rpc.rpc_sync( 2025-09-07T08:19:26.4879137Z >>> "worker1", 2025-09-07T08:19:26.4879275Z >>> AsyncExecutionClass.class_async_add, 2025-09-07T08:19:26.4879386Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T08:19:26.4879460Z >>> ) 2025-09-07T08:19:26.4879586Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:26.4879662Z 2025-09-07T08:19:26.4879821Z This decorator also works with RRef helpers, i.e., . 2025-09-07T08:19:26.4879969Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-09-07T08:19:26.4880150Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-09-07T08:19:26.4880300Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-09-07T08:19:26.4880375Z 2025-09-07T08:19:26.4880491Z >>> from torch.distributed import rpc 2025-09-07T08:19:26.4880581Z >>> 2025-09-07T08:19:26.4880715Z >>> # reuse the AsyncExecutionClass class above 2025-09-07T08:19:26.4880871Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T08:19:26.4881071Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-09-07T08:19:26.4881183Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:26.4881264Z >>> 2025-09-07T08:19:26.4881410Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T08:19:26.4881649Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-09-07T08:19:26.4881764Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:26.4881844Z >>> 2025-09-07T08:19:26.4882004Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T08:19:26.4882233Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-09-07T08:19:26.4882357Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T08:19:26.4882435Z 2025-09-07T08:19:26.4882683Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4882768Z 2025-09-07T08:19:26.4882857Z warnings.warn(msg) 2025-09-07T08:19:26.4882941Z 2025-09-07T08:19:26.4883159Z --- Parse Warning: 87 / 146 --- 2025-09-07T08:19:26.4884352Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=113. 2025-09-07T08:19:26.4884651Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4884731Z 2025-09-07T08:19:26.4884945Z Set device mapping between each RPC caller and callee pair. This 2025-09-07T08:19:26.4885125Z function can be called multiple times to incrementally add 2025-09-07T08:19:26.4885234Z device placement configurations. 2025-09-07T08:19:26.4885320Z 2025-09-07T08:19:26.4885400Z Args: 2025-09-07T08:19:26.4885505Z to (str): Callee name. 2025-09-07T08:19:26.4885700Z device_map (Dict of int, str, or torch.device): Device placement 2025-09-07T08:19:26.4885878Z mappings from this worker to the callee. This map must be 2025-09-07T08:19:26.4885975Z invertible. 2025-09-07T08:19:26.4886054Z 2025-09-07T08:19:26.4886146Z Example: 2025-09-07T08:19:26.4886259Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:26.4886347Z >>> # both workers 2025-09-07T08:19:26.4886445Z >>> def add(x, y): 2025-09-07T08:19:26.4886579Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-09-07T08:19:26.4886684Z >>> return x + y, (x + y).to(2) 2025-09-07T08:19:26.4886793Z >>> 2025-09-07T08:19:26.4886879Z >>> # on worker 0 2025-09-07T08:19:26.4887022Z >>> options = TensorPipeRpcBackendOptions( 2025-09-07T08:19:26.4887123Z >>> num_worker_threads=8, 2025-09-07T08:19:26.4887235Z >>> device_maps={"worker1": {0: 1}} 2025-09-07T08:19:26.4887369Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-09-07T08:19:26.4887448Z >>> ) 2025-09-07T08:19:26.4887584Z >>> options.set_device_map("worker1", {1: 2}) 2025-09-07T08:19:26.4887705Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-09-07T08:19:26.4887781Z >>> 2025-09-07T08:19:26.4887875Z >>> rpc.init_rpc( 2025-09-07T08:19:26.4887960Z >>> "worker0", 2025-09-07T08:19:26.4888052Z >>> rank=0, 2025-09-07T08:19:26.4888140Z >>> world_size=2, 2025-09-07T08:19:26.4888288Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-09-07T08:19:26.4888405Z >>> rpc_backend_options=options 2025-09-07T08:19:26.4888481Z >>> ) 2025-09-07T08:19:26.4888571Z >>> 2025-09-07T08:19:26.4888663Z >>> x = torch.ones(2) 2025-09-07T08:19:26.4888821Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-09-07T08:19:26.4889012Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-09-07T08:19:26.4889193Z >>> # sending the return value back, it will follow the invert of 2025-09-07T08:19:26.4889375Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-09-07T08:19:26.4889466Z >>> # cuda:1 on worker0 2025-09-07T08:19:26.4889611Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-09-07T08:19:26.4889762Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-09-07T08:19:26.4889840Z 2025-09-07T08:19:26.4890100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4890174Z 2025-09-07T08:19:26.4890269Z warnings.warn(msg) 2025-09-07T08:19:26.4890350Z 2025-09-07T08:19:26.4890545Z --- Parse Warning: 88 / 146 --- 2025-09-07T08:19:26.4891644Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19. 2025-09-07T08:19:26.4891917Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4891996Z 2025-09-07T08:19:26.4892203Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-09-07T08:19:26.4892504Z except that it enables profiling on all threads running RPC server request callbacks. 2025-09-07T08:19:26.4892616Z 2025-09-07T08:19:26.4892888Z Context manager that manages autograd profiler state and holds a summary of results. 2025-09-07T08:19:26.4893122Z Under the hood it just records events of functions being executed in C++ and 2025-09-07T08:19:26.4893358Z exposes those events to Python. You can wrap any code into it and it will 2025-09-07T08:19:26.4893478Z only report runtime of PyTorch functions. 2025-09-07T08:19:26.4893755Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-09-07T08:19:26.4893832Z 2025-09-07T08:19:26.4893910Z Args: 2025-09-07T08:19:26.4894185Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-09-07T08:19:26.4894281Z Default: ``True``. 2025-09-07T08:19:26.4894366Z 2025-09-07T08:19:26.4894647Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-09-07T08:19:26.4894841Z Adds approximately 4us of overhead to each tensor operation. 2025-09-07T08:19:26.4894943Z Default: ``False`` 2025-09-07T08:19:26.4895019Z 2025-09-07T08:19:26.4895255Z record_shapes (bool, optional): If shapes recording is set, information 2025-09-07T08:19:26.4895505Z about input dimensions will be collected. This allows one to see which 2025-09-07T08:19:26.4895716Z dimensions have been used under the hood and further group by them 2025-09-07T08:19:26.4895937Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-09-07T08:19:26.4896159Z shape recording might skew your profiling data. It is recommended to 2025-09-07T08:19:26.4896404Z use separate runs with and without shape recording to validate the timing. 2025-09-07T08:19:26.4896625Z Most likely the skew will be negligible for bottom most events (in a case 2025-09-07T08:19:26.4896837Z of nested function calls). But for higher level functions the total 2025-09-07T08:19:26.4897047Z self cpu time might be artificially increased because of the shape 2025-09-07T08:19:26.4897159Z collection. 2025-09-07T08:19:26.4897252Z 2025-09-07T08:19:26.4897517Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-09-07T08:19:26.4897594Z 2025-09-07T08:19:26.4897693Z .. warning:: 2025-09-07T08:19:26.4897892Z Enabling memory profiling incurs additional profiler overhead 2025-09-07T08:19:26.4897980Z 2025-09-07T08:19:26.4898061Z .. warning:: 2025-09-07T08:19:26.4898343Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-09-07T08:19:26.4898538Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-09-07T08:19:26.4898768Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-09-07T08:19:26.4898941Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-09-07T08:19:26.4899016Z 2025-09-07T08:19:26.4899096Z Example: 2025-09-07T08:19:26.4899199Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.4899288Z >>> # On worker 0: 2025-09-07T08:19:26.4899376Z >>> import torch 2025-09-07T08:19:26.4899517Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4899651Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T08:19:26.4899782Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-09-07T08:19:26.4899895Z >>> outer_profile_rref = rpc.remote( 2025-09-07T08:19:26.4900055Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T08:19:26.4900149Z ... ) 2025-09-07T08:19:26.4900275Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-09-07T08:19:26.4900435Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-09-07T08:19:26.4900545Z >>> inner_profile_rref = rpc.remote( 2025-09-07T08:19:26.4900728Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T08:19:26.4900847Z ... ) 2025-09-07T08:19:26.4900975Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-09-07T08:19:26.4901133Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-09-07T08:19:26.4901307Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T08:19:26.4901476Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T08:19:26.4901649Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-09-07T08:19:26.4901885Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4902210Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T08:19:26.4902438Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4902637Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-09-07T08:19:26.4902831Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-09-07T08:19:26.4903081Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4903203Z Self CPU time total: 89.667us 2025-09-07T08:19:26.4903364Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-09-07T08:19:26.4903596Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4903903Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T08:19:26.4904138Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4904318Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-09-07T08:19:26.4904530Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-09-07T08:19:26.4904722Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-09-07T08:19:26.4904947Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T08:19:26.4905066Z Self CPU time total: 213.926us 2025-09-07T08:19:26.4905166Z >>> rpc.shutdown() 2025-09-07T08:19:26.4905249Z 2025-09-07T08:19:26.4905352Z >>> # On worker 1: 2025-09-07T08:19:26.4905478Z >>> import torch.distributed.rpc as rpc 2025-09-07T08:19:26.4905625Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T08:19:26.4905788Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-09-07T08:19:26.4905883Z >>> rpc.shutdown() 2025-09-07T08:19:26.4905969Z 2025-09-07T08:19:26.4906221Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4906311Z 2025-09-07T08:19:26.4906410Z warnings.warn(msg) 2025-09-07T08:19:26.4906488Z 2025-09-07T08:19:26.4906696Z --- Parse Warning: 89 / 146 --- 2025-09-07T08:19:26.4907695Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=35. 2025-09-07T08:19:26.4907966Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4908046Z 2025-09-07T08:19:26.4908308Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-09-07T08:19:26.4908636Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-09-07T08:19:26.4908924Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-09-07T08:19:26.4909094Z :class:`DTensor` according to the ``out_placements``. 2025-09-07T08:19:26.4909173Z 2025-09-07T08:19:26.4909252Z Args: 2025-09-07T08:19:26.4909466Z func (Callable): the function to be applied on each local shard of 2025-09-07T08:19:26.4909563Z :class:`DTensor` s. 2025-09-07T08:19:26.4909797Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-09-07T08:19:26.4910048Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-09-07T08:19:26.4910287Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-09-07T08:19:26.4910539Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-09-07T08:19:26.4910790Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-09-07T08:19:26.4910924Z mapping to the flattened ``output``. 2025-09-07T08:19:26.4911129Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-09-07T08:19:26.4911429Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-09-07T08:19:26.4911537Z should be `None`. 2025-09-07T08:19:26.4911771Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-09-07T08:19:26.4911998Z in. In this case, even if `out_placements` is not `None`, the result function 2025-09-07T08:19:26.4912252Z should ignore the desired placements because the function is not running with 2025-09-07T08:19:26.4912357Z :class:`DTensor` s. 2025-09-07T08:19:26.4912521Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T08:19:26.4912803Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-09-07T08:19:26.4913047Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-09-07T08:19:26.4913297Z placements of each :class:`DTensor` argument is the same as the required 2025-09-07T08:19:26.4913486Z placements or not. If the placements are not the same and 2025-09-07T08:19:26.4913727Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-09-07T08:19:26.4913968Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-09-07T08:19:26.4914233Z the required sharding placements before passing its local tensor to ``func``. 2025-09-07T08:19:26.4914455Z The only exception is when required placements are not ``None`` and the 2025-09-07T08:19:26.4914701Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-09-07T08:19:26.4914917Z will be skipped and the argument will be directly passed to ``func``. 2025-09-07T08:19:26.4915138Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-09-07T08:19:26.4915239Z Default: None 2025-09-07T08:19:26.4915422Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T08:19:26.4915639Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-09-07T08:19:26.4915849Z to the flattened input DTensor. This argument is the hint that user 2025-09-07T08:19:26.4916041Z can give to :meth:`to_local` in case the gradient layout of the 2025-09-07T08:19:26.4916253Z local tensor input does not match its :class:`DTensor` input layout. 2025-09-07T08:19:26.4916449Z If not specified, we will assume the gradient layout of the local 2025-09-07T08:19:26.4916675Z tensor input remains the same as the original :class:`DTensor` input 2025-09-07T08:19:26.4916865Z and use that for gradient computation. Default: None. 2025-09-07T08:19:26.4917039Z device_mesh (:class:`DeviceMesh`, optional): 2025-09-07T08:19:26.4917260Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-09-07T08:19:26.4917514Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-09-07T08:19:26.4917624Z mesh. Default: None. 2025-09-07T08:19:26.4917702Z 2025-09-07T08:19:26.4917798Z Keyword Args: 2025-09-07T08:19:26.4917917Z redistribute_inputs (bool, optional): 2025-09-07T08:19:26.4918168Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-09-07T08:19:26.4918423Z their placements are different from the required input placements. If this 2025-09-07T08:19:26.4918649Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-09-07T08:19:26.4918799Z an exception will be raised. Default: False. 2025-09-07T08:19:26.4918873Z 2025-09-07T08:19:26.4918958Z Returns: 2025-09-07T08:19:26.4919222Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-09-07T08:19:26.4919459Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-09-07T08:19:26.4919574Z 2025-09-07T08:19:26.4919655Z Raises: 2025-09-07T08:19:26.4919894Z AssertionError: For any non-DTensor output, we require its corresponding 2025-09-07T08:19:26.4920163Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-09-07T08:19:26.4920261Z if this is not the case. 2025-09-07T08:19:26.4920347Z 2025-09-07T08:19:26.4920607Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-09-07T08:19:26.4920756Z a redistribution according to ``in_placements``. 2025-09-07T08:19:26.4920843Z 2025-09-07T08:19:26.4920923Z Example: 2025-09-07T08:19:26.4921039Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:26.4921186Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-09-07T08:19:26.4921330Z >>> partial_sum_tensor = torch.mm(W, X) 2025-09-07T08:19:26.4921582Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-09-07T08:19:26.4921683Z >>> return reduced_tensor 2025-09-07T08:19:26.4921762Z >>> 2025-09-07T08:19:26.4921901Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-09-07T08:19:26.4922027Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-09-07T08:19:26.4922132Z >>> Y = torch.mm(W, X) 2025-09-07T08:19:26.4922313Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-09-07T08:19:26.4922491Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-09-07T08:19:26.4922576Z >>> 2025-09-07T08:19:26.4922851Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-09-07T08:19:26.4922987Z >>> local_mm_allreduce_forward = local_map( 2025-09-07T08:19:26.4923089Z >>> mm_allreduce_forward, 2025-09-07T08:19:26.4923206Z >>> out_placements=[Replicate()], 2025-09-07T08:19:26.4923335Z >>> in_placements=[col_wise, row_wise], 2025-09-07T08:19:26.4923437Z >>> device_mesh=device_mesh, 2025-09-07T08:19:26.4923529Z >>> ) 2025-09-07T08:19:26.4923608Z >>> 2025-09-07T08:19:26.4923708Z >>> W_dt = distribute_tensor( 2025-09-07T08:19:26.4923818Z ... W, device_mesh, (col_wise) 2025-09-07T08:19:26.4923925Z ... ) # col-wisely sharded W tensor 2025-09-07T08:19:26.4924035Z >>> X_dt = distribute_tensor( 2025-09-07T08:19:26.4924228Z ... X, device_mesh, (row_wise) 2025-09-07T08:19:26.4924340Z ... ) # row-wisely sharded X tensor 2025-09-07T08:19:26.4924467Z >>> Y_dt = local_mm_allreduce_forward( 2025-09-07T08:19:26.4924595Z ... device_mesh, W_dt, X_dt 2025-09-07T08:19:26.4924775Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-09-07T08:19:26.4924849Z 2025-09-07T08:19:26.4925049Z .. note:: This API is currently experimental and subject to change 2025-09-07T08:19:26.4925136Z 2025-09-07T08:19:26.4925385Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4925481Z 2025-09-07T08:19:26.4925573Z warnings.warn(msg) 2025-09-07T08:19:26.4925651Z 2025-09-07T08:19:26.4925866Z --- Parse Warning: 90 / 146 --- 2025-09-07T08:19:26.4926946Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2025-09-07T08:19:26.4927223Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4927300Z 2025-09-07T08:19:26.4927575Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-09-07T08:19:26.4927824Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-09-07T08:19:26.4928102Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-09-07T08:19:26.4928354Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-09-07T08:19:26.4928628Z when users would like to overwrite default sharding strategies of existing operators. 2025-09-07T08:19:26.4928706Z 2025-09-07T08:19:26.4928796Z Args: 2025-09-07T08:19:26.4928923Z op (Union[OpOverload, List[OpOverload]]): 2025-09-07T08:19:26.4929128Z An op or a list of ops to register the customized sharding function. 2025-09-07T08:19:26.4929206Z 2025-09-07T08:19:26.4929288Z Returns: 2025-09-07T08:19:26.4929567Z A function decorator which can be used to wrap a function that defines the sharding 2025-09-07T08:19:26.4929839Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-09-07T08:19:26.4930147Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-09-07T08:19:26.4930452Z already implemented the operator. The customized sharding function takes the same inputs 2025-09-07T08:19:26.4930694Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-09-07T08:19:26.4930974Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-09-07T08:19:26.4931243Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-09-07T08:19:26.4931368Z corresponding input placements. 2025-09-07T08:19:26.4931444Z 2025-09-07T08:19:26.4931526Z Example: 2025-09-07T08:19:26.4931651Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:26.4931783Z >>> @register_sharding(aten._softmax.default) 2025-09-07T08:19:26.4931947Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-09-07T08:19:26.4932087Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-09-07T08:19:26.4932197Z >>> acceptable_shardings = [] 2025-09-07T08:19:26.4932290Z >>> 2025-09-07T08:19:26.4932465Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-09-07T08:19:26.4932618Z >>> acceptable_shardings.append(all_replicate) 2025-09-07T08:19:26.4932695Z >>> 2025-09-07T08:19:26.4932812Z >>> for sharding_dim in range(x.ndim): 2025-09-07T08:19:26.4932938Z >>> if sharding_dim != softmax_dim: 2025-09-07T08:19:26.4933035Z >>> all_sharded = ( 2025-09-07T08:19:26.4933160Z >>> [Shard(sharding_dim)], 2025-09-07T08:19:26.4933282Z >>> [Shard(sharding_dim), None, None], 2025-09-07T08:19:26.4933393Z >>> ) 2025-09-07T08:19:26.4933603Z >>> acceptable_shardings.append(all_sharded) 2025-09-07T08:19:26.4933685Z >>> 2025-09-07T08:19:26.4933810Z >>> return acceptable_shardings 2025-09-07T08:19:26.4933891Z 2025-09-07T08:19:26.4934085Z .. note:: This API is currently experimental and subject to change 2025-09-07T08:19:26.4934177Z 2025-09-07T08:19:26.4934429Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4934523Z 2025-09-07T08:19:26.4934619Z warnings.warn(msg) 2025-09-07T08:19:26.4934698Z 2025-09-07T08:19:26.4934897Z --- Parse Warning: 91 / 146 --- 2025-09-07T08:19:26.4935919Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=428. 2025-09-07T08:19:26.4936197Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4936281Z 2025-09-07T08:19:26.4936665Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-09-07T08:19:26.4937027Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-09-07T08:19:26.4937108Z 2025-09-07T08:19:26.4937208Z Keyword Args: 2025-09-07T08:19:26.4937409Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T08:19:26.4937737Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T08:19:26.4938108Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T08:19:26.4938224Z as a placeholder. default: None. 2025-09-07T08:19:26.4938462Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T08:19:26.4938842Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T08:19:26.4939269Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T08:19:26.4939420Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T08:19:26.4939790Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T08:19:26.4939900Z default: None 2025-09-07T08:19:26.4940060Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T08:19:26.4940440Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T08:19:26.4940585Z have the desired DTensor layouts. default: None. 2025-09-07T08:19:26.4940700Z use_local_output (bool, optional): 2025-09-07T08:19:26.4941071Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T08:19:26.4941154Z Returns: 2025-09-07T08:19:26.4941482Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-09-07T08:19:26.4941562Z 2025-09-07T08:19:26.4941647Z Example:: 2025-09-07T08:19:26.4941763Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:26.4942068Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-09-07T08:19:26.4942266Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T08:19:26.4942348Z >>> ... 2025-09-07T08:19:26.4942646Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T08:19:26.4942783Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T08:19:26.4942862Z >>> 2025-09-07T08:19:26.4943231Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-09-07T08:19:26.4943403Z >>> # and then redistributed to Replicated DTensor. 2025-09-07T08:19:26.4943505Z >>> parallelize_module( 2025-09-07T08:19:26.4943647Z >>> block, # this can be a submodule or module 2025-09-07T08:19:26.4943734Z >>> tp_mesh, 2025-09-07T08:19:26.4943844Z >>> parallelize_plan={ 2025-09-07T08:19:26.4943962Z >>> "attn": PrepareModuleInput( 2025-09-07T08:19:26.4944093Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T08:19:26.4944267Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-09-07T08:19:26.4944351Z >>> ), 2025-09-07T08:19:26.4944440Z >>> } 2025-09-07T08:19:26.4944520Z >>> ) 2025-09-07T08:19:26.4944596Z 2025-09-07T08:19:26.4944859Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4944938Z 2025-09-07T08:19:26.4945031Z warnings.warn(msg) 2025-09-07T08:19:26.4945120Z 2025-09-07T08:19:26.4945310Z --- Parse Warning: 92 / 146 --- 2025-09-07T08:19:26.4946479Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=597. 2025-09-07T08:19:26.4946851Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4946968Z 2025-09-07T08:19:26.4947668Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-09-07T08:19:26.4948231Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-09-07T08:19:26.4948377Z 2025-09-07T08:19:26.4948525Z Keyword Args: 2025-09-07T08:19:26.4948813Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T08:19:26.4949475Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T08:19:26.4950126Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T08:19:26.4950362Z ``None`` need to be specified as a placeholder. 2025-09-07T08:19:26.4950683Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T08:19:26.4951339Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T08:19:26.4951523Z have the desired DTensor layouts. 2025-09-07T08:19:26.4951703Z use_local_output (bool, optional): 2025-09-07T08:19:26.4952357Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T08:19:26.4952496Z Returns: 2025-09-07T08:19:26.4952994Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-09-07T08:19:26.4953123Z 2025-09-07T08:19:26.4953275Z Example:: 2025-09-07T08:19:26.4953469Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:26.4953998Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-09-07T08:19:26.4954309Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T08:19:26.4954444Z >>> ... 2025-09-07T08:19:26.4954961Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T08:19:26.4955199Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T08:19:26.4955366Z >>> 2025-09-07T08:19:26.4956235Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-09-07T08:19:26.4956605Z >>> # and then redistributed to Sharded DTensor. 2025-09-07T08:19:26.4956850Z >>> parallelize_module( 2025-09-07T08:19:26.4957107Z >>> block, # this can be a submodule or module 2025-09-07T08:19:26.4957262Z >>> tp_mesh, 2025-09-07T08:19:26.4957495Z >>> parallelize_plan = PrepareModuleOutput( 2025-09-07T08:19:26.4957681Z >>> output_layouts=Replicate(), 2025-09-07T08:19:26.4957867Z >>> desired_output_layouts=Shard(0) 2025-09-07T08:19:26.4958010Z >>> ) 2025-09-07T08:19:26.4958137Z >>> ) 2025-09-07T08:19:26.4958282Z 2025-09-07T08:19:26.4958771Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4958911Z 2025-09-07T08:19:26.4959109Z warnings.warn(msg) 2025-09-07T08:19:26.4959264Z 2025-09-07T08:19:26.4959708Z --- Parse Warning: 93 / 146 --- 2025-09-07T08:19:26.4962093Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-09-07T08:19:26.4962673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4962934Z 2025-09-07T08:19:26.4963890Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-09-07T08:19:26.4964916Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-09-07T08:19:26.4965754Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-09-07T08:19:26.4966197Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-09-07T08:19:26.4966360Z 2025-09-07T08:19:26.4966539Z Keyword Args: 2025-09-07T08:19:26.4966976Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T08:19:26.4967694Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T08:19:26.4968550Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T08:19:26.4968802Z as a placeholder. default: None. 2025-09-07T08:19:26.4969259Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T08:19:26.4969722Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T08:19:26.4970177Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T08:19:26.4970342Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T08:19:26.4970776Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T08:19:26.4970885Z default: None 2025-09-07T08:19:26.4971079Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T08:19:26.4971504Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T08:19:26.4971685Z have the desired DTensor layouts. default: None. 2025-09-07T08:19:26.4971812Z use_local_input (bool, optional): 2025-09-07T08:19:26.4972224Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T08:19:26.4972420Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T08:19:26.4972806Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T08:19:26.4973546Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T08:19:26.4973756Z ``None`` need to be specified as a placeholder. 2025-09-07T08:19:26.4973998Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T08:19:26.4974445Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T08:19:26.4974583Z have the desired DTensor layouts. 2025-09-07T08:19:26.4974723Z use_local_output (bool, optional): 2025-09-07T08:19:26.4975136Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T08:19:26.4975249Z Returns: 2025-09-07T08:19:26.4975672Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-09-07T08:19:26.4975762Z 2025-09-07T08:19:26.4975975Z Example:: 2025-09-07T08:19:26.4976090Z >>> # xdoctest: +SKIP(failing) 2025-09-07T08:19:26.4976438Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-09-07T08:19:26.4976631Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T08:19:26.4976753Z >>> ... 2025-09-07T08:19:26.4977065Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T08:19:26.4977190Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T08:19:26.4977281Z >>> 2025-09-07T08:19:26.4977614Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-09-07T08:19:26.4977968Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-09-07T08:19:26.4978188Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-09-07T08:19:26.4978286Z >>> parallelize_module( 2025-09-07T08:19:26.4978431Z >>> block, # this can be a submodule or module 2025-09-07T08:19:26.4978521Z >>> tp_mesh, 2025-09-07T08:19:26.4978624Z >>> parallelize_plan={ 2025-09-07T08:19:26.4978814Z >>> "attn": PrepareModuleInputOutput( 2025-09-07T08:19:26.4978956Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T08:19:26.4979138Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-09-07T08:19:26.4979260Z >>> output_layouts=Replicate(), 2025-09-07T08:19:26.4979380Z >>> desired_output_layouts=Shard(0), 2025-09-07T08:19:26.4979473Z >>> ), 2025-09-07T08:19:26.4979553Z >>> } 2025-09-07T08:19:26.4979643Z >>> ) 2025-09-07T08:19:26.4979721Z 2025-09-07T08:19:26.4979971Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4980061Z 2025-09-07T08:19:26.4980156Z warnings.warn(msg) 2025-09-07T08:19:26.4980247Z 2025-09-07T08:19:26.4980479Z --- Parse Warning: 94 / 146 --- 2025-09-07T08:19:26.4981573Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-09-07T08:19:26.4981853Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4981932Z 2025-09-07T08:19:26.4982259Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-09-07T08:19:26.4982446Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-09-07T08:19:26.4982523Z 2025-09-07T08:19:26.4982702Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-09-07T08:19:26.4982779Z 2025-09-07T08:19:26.4982871Z Example: 2025-09-07T08:19:26.4983044Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T08:19:26.4983188Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:26.4983337Z >>> m = LowRankMultivariateNormal( 2025-09-07T08:19:26.4983517Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-09-07T08:19:26.4983611Z ... ) 2025-09-07T08:19:26.4983898Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-09-07T08:19:26.4983997Z tensor([-0.2102, -0.5429]) 2025-09-07T08:19:26.4984082Z 2025-09-07T08:19:26.4984164Z Args: 2025-09-07T08:19:26.4984406Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-09-07T08:19:26.4984662Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-09-07T08:19:26.4984781Z `batch_shape + event_shape + (rank,)` 2025-09-07T08:19:26.4985046Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-09-07T08:19:26.4985149Z `batch_shape + event_shape` 2025-09-07T08:19:26.4985238Z 2025-09-07T08:19:26.4985316Z Note: 2025-09-07T08:19:26.4985584Z The computation for determinant and inverse of covariance matrix is avoided when 2025-09-07T08:19:26.4985872Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-09-07T08:19:26.4986082Z `_ and 2025-09-07T08:19:26.4986377Z `matrix determinant lemma `_. 2025-09-07T08:19:26.4986624Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-09-07T08:19:26.4986744Z the small size "capacitance" matrix:: 2025-09-07T08:19:26.4986830Z 2025-09-07T08:19:26.4987009Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-09-07T08:19:26.4987095Z 2025-09-07T08:19:26.4987348Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4987428Z 2025-09-07T08:19:26.4987537Z warnings.warn(msg) 2025-09-07T08:19:26.4987614Z 2025-09-07T08:19:26.4987842Z --- Parse Warning: 95 / 146 --- 2025-09-07T08:19:26.4988853Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=15. 2025-09-07T08:19:26.4989117Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4989205Z 2025-09-07T08:19:26.4989428Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-09-07T08:19:26.4989681Z distribution where all component are from different parameterizations of 2025-09-07T08:19:26.4989889Z the same distribution type. It is parameterized by a `Categorical` 2025-09-07T08:19:26.4990084Z "selecting distribution" (over `k` component) and a component 2025-09-07T08:19:26.4990295Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-09-07T08:19:26.4990452Z (equal to `[k]`) which indexes each (batch of) component. 2025-09-07T08:19:26.4990542Z 2025-09-07T08:19:26.4990629Z Examples:: 2025-09-07T08:19:26.4990706Z 2025-09-07T08:19:26.4990827Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.4991027Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-09-07T08:19:26.4991148Z >>> # weighted normal distributions 2025-09-07T08:19:26.4991262Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T08:19:26.4991403Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-09-07T08:19:26.4991525Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T08:19:26.4991601Z 2025-09-07T08:19:26.4991807Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-09-07T08:19:26.4991956Z >>> # weighted bivariate normal distributions 2025-09-07T08:19:26.4992094Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T08:19:26.4992207Z >>> comp = D.Independent(D.Normal( 2025-09-07T08:19:26.4992333Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-09-07T08:19:26.4992456Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T08:19:26.4992528Z 2025-09-07T08:19:26.4992705Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-09-07T08:19:26.4992909Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-09-07T08:19:26.4993026Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-09-07T08:19:26.4993143Z >>> comp = D.Independent(D.Normal( 2025-09-07T08:19:26.4993274Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-09-07T08:19:26.4993391Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T08:19:26.4993472Z 2025-09-07T08:19:26.4993552Z Args: 2025-09-07T08:19:26.4993760Z mixture_distribution: `torch.distributions.Categorical`-like 2025-09-07T08:19:26.4993952Z instance. Manages the probability of selecting component. 2025-09-07T08:19:26.4994119Z The number of categories must match the rightmost batch 2025-09-07T08:19:26.4994337Z dimension of the `component_distribution`. Must have either 2025-09-07T08:19:26.4994476Z scalar `batch_shape` or `batch_shape` matching 2025-09-07T08:19:26.4994609Z `component_distribution.batch_shape[:-1]` 2025-09-07T08:19:26.4994835Z component_distribution: `torch.distributions.Distribution`-like 2025-09-07T08:19:26.4995008Z instance. Right-most batch dimension indexes component. 2025-09-07T08:19:26.4995089Z 2025-09-07T08:19:26.4995338Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4995413Z 2025-09-07T08:19:26.4995508Z warnings.warn(msg) 2025-09-07T08:19:26.4995584Z 2025-09-07T08:19:26.4995780Z --- Parse Warning: 96 / 146 --- 2025-09-07T08:19:26.4996804Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-09-07T08:19:26.4997076Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.4997151Z 2025-09-07T08:19:26.4997332Z Creates a RelaxedBernoulli distribution, parametrized by 2025-09-07T08:19:26.4997525Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-09-07T08:19:26.4997737Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-09-07T08:19:26.4997925Z so the values are in (0, 1), and has reparametrizable samples. 2025-09-07T08:19:26.4997998Z 2025-09-07T08:19:26.4998082Z Example:: 2025-09-07T08:19:26.4998167Z 2025-09-07T08:19:26.4998307Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:26.4998439Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-09-07T08:19:26.4998571Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-09-07T08:19:26.4998659Z >>> m.sample() 2025-09-07T08:19:26.4998782Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-09-07T08:19:26.4998857Z 2025-09-07T08:19:26.4998937Z Args: 2025-09-07T08:19:26.4999083Z temperature (Tensor): relaxation temperature 2025-09-07T08:19:26.4999255Z probs (Number, Tensor): the probability of sampling `1` 2025-09-07T08:19:26.4999423Z logits (Number, Tensor): the log-odds of sampling `1` 2025-09-07T08:19:26.4999498Z 2025-09-07T08:19:26.4999743Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.4999825Z 2025-09-07T08:19:26.4999917Z warnings.warn(msg) 2025-09-07T08:19:26.5000000Z 2025-09-07T08:19:26.5000217Z --- Parse Warning: 97 / 146 --- 2025-09-07T08:19:26.5001288Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-09-07T08:19:26.5001556Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5001633Z 2025-09-07T08:19:26.5001852Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-09-07T08:19:26.5002043Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-09-07T08:19:26.5002277Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-09-07T08:19:26.5002444Z its samples are on simplex, and are reparametrizable. 2025-09-07T08:19:26.5002521Z 2025-09-07T08:19:26.5002614Z Example:: 2025-09-07T08:19:26.5002689Z 2025-09-07T08:19:26.5002823Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:26.5002980Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-09-07T08:19:26.5003103Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-09-07T08:19:26.5003196Z >>> m.sample() 2025-09-07T08:19:26.5003332Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-09-07T08:19:26.5003410Z 2025-09-07T08:19:26.5003496Z Args: 2025-09-07T08:19:26.5003633Z temperature (Tensor): relaxation temperature 2025-09-07T08:19:26.5003757Z probs (Tensor): event probabilities 2025-09-07T08:19:26.5003941Z logits (Tensor): unnormalized log probability for each event 2025-09-07T08:19:26.5004016Z 2025-09-07T08:19:26.5004367Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5004445Z 2025-09-07T08:19:26.5004534Z warnings.warn(msg) 2025-09-07T08:19:26.5004617Z 2025-09-07T08:19:26.5004806Z --- Parse Warning: 98 / 146 --- 2025-09-07T08:19:26.5005861Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2025-09-07T08:19:26.5006128Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5006325Z Return a new dict with new, potentially nested, key value pair 2025-09-07T08:19:26.5006399Z 2025-09-07T08:19:26.5006490Z >>> purchase = { 2025-09-07T08:19:26.5006590Z ... "name": "Alice", 2025-09-07T08:19:26.5006760Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T08:19:26.5006886Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T08:19:26.5006965Z ... } 2025-09-07T08:19:26.5007170Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-09-07T08:19:26.5007287Z {'credit card': '5555-1234-1234-1234', 2025-09-07T08:19:26.5007376Z 'name': 'Alice', 2025-09-07T08:19:26.5007548Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-09-07T08:19:26.5007630Z 2025-09-07T08:19:26.5007885Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5007970Z 2025-09-07T08:19:26.5008066Z warnings.warn(msg) 2025-09-07T08:19:26.5008144Z 2025-09-07T08:19:26.5008330Z --- Parse Warning: 99 / 146 --- 2025-09-07T08:19:26.5009350Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2025-09-07T08:19:26.5009614Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5009789Z Update value in a (potentially) nested dictionary 2025-09-07T08:19:26.5009875Z 2025-09-07T08:19:26.5009981Z inputs: 2025-09-07T08:19:26.5010095Z d - dictionary on which to operate 2025-09-07T08:19:26.5010322Z keys - list or tuple giving the location of the value to be changed in d 2025-09-07T08:19:26.5010447Z func - function to operate on that value 2025-09-07T08:19:26.5010526Z 2025-09-07T08:19:26.5010721Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-09-07T08:19:26.5010954Z original dictionary with v replaced by func(v), but does not mutate the 2025-09-07T08:19:26.5011061Z original dictionary. 2025-09-07T08:19:26.5011138Z 2025-09-07T08:19:26.5011342Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-09-07T08:19:26.5011564Z specified by the keys, with the innermost value set to func(default). 2025-09-07T08:19:26.5011640Z 2025-09-07T08:19:26.5015622Z >>> inc = lambda x: x + 1 2025-09-07T08:19:26.5015775Z >>> update_in({"a": 0}, ["a"], inc) 2025-09-07T08:19:26.5015871Z {'a': 1} 2025-09-07T08:19:26.5015949Z 2025-09-07T08:19:26.5016058Z >>> transaction = { 2025-09-07T08:19:26.5016154Z ... "name": "Alice", 2025-09-07T08:19:26.5016419Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T08:19:26.5016549Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T08:19:26.5016630Z ... } 2025-09-07T08:19:26.5016858Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-09-07T08:19:26.5016963Z {'credit card': '5555-1234-1234-1234', 2025-09-07T08:19:26.5017052Z 'name': 'Alice', 2025-09-07T08:19:26.5017224Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-09-07T08:19:26.5017302Z 2025-09-07T08:19:26.5017432Z >>> # updating a value when k0 is not in d 2025-09-07T08:19:26.5017563Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-09-07T08:19:26.5017651Z {1: {2: {3: 'bar'}}} 2025-09-07T08:19:26.5017780Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-09-07T08:19:26.5017874Z {1: 'foo', 2: {3: {4: 1}}} 2025-09-07T08:19:26.5017982Z 2025-09-07T08:19:26.5018252Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5018332Z 2025-09-07T08:19:26.5018436Z warnings.warn(msg) 2025-09-07T08:19:26.5018511Z 2025-09-07T08:19:26.5018739Z --- Parse Warning: 100 / 146 --- 2025-09-07T08:19:26.5019761Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2025-09-07T08:19:26.5020021Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5020196Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-09-07T08:19:26.5020275Z 2025-09-07T08:19:26.5020462Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-09-07T08:19:26.5020664Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-09-07T08:19:26.5020745Z 2025-09-07T08:19:26.5020960Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-09-07T08:19:26.5021087Z structures such as dictionaries and lists. 2025-09-07T08:19:26.5021176Z 2025-09-07T08:19:26.5021269Z >>> transaction = { 2025-09-07T08:19:26.5021358Z ... "name": "Alice", 2025-09-07T08:19:26.5021558Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T08:19:26.5021675Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T08:19:26.5021752Z ... } 2025-09-07T08:19:26.5021894Z >>> get_in(["purchase", "items", 0], transaction) 2025-09-07T08:19:26.5021973Z 'Apple' 2025-09-07T08:19:26.5022124Z >>> get_in(["name"], transaction) 2025-09-07T08:19:26.5022244Z 'Alice' 2025-09-07T08:19:26.5022366Z >>> get_in(["purchase", "total"], transaction) 2025-09-07T08:19:26.5022522Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-09-07T08:19:26.5022655Z >>> get_in(["purchase", "items", 10], transaction) 2025-09-07T08:19:26.5022801Z >>> get_in(["purchase", "total"], transaction, 0) 2025-09-07T08:19:26.5022881Z 0 2025-09-07T08:19:26.5022989Z >>> get_in(["y"], {}, no_default=True) 2025-09-07T08:19:26.5023113Z Traceback (most recent call last): 2025-09-07T08:19:26.5023189Z ... 2025-09-07T08:19:26.5023287Z KeyError: 'y' 2025-09-07T08:19:26.5023363Z 2025-09-07T08:19:26.5023442Z See Also: 2025-09-07T08:19:26.5023542Z itertoolz.get 2025-09-07T08:19:26.5023637Z operator.getitem 2025-09-07T08:19:26.5023716Z 2025-09-07T08:19:26.5023977Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5024054Z 2025-09-07T08:19:26.5024155Z warnings.warn(msg) 2025-09-07T08:19:26.5024231Z 2025-09-07T08:19:26.5024423Z --- Parse Warning: 101 / 146 --- 2025-09-07T08:19:26.5025483Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2025-09-07T08:19:26.5025741Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5025864Z Group a collection by a key function 2025-09-07T08:19:26.5025940Z 2025-09-07T08:19:26.5026103Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-09-07T08:19:26.5026234Z >>> groupby(len, names) # doctest: +SKIP 2025-09-07T08:19:26.5026391Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-09-07T08:19:26.5026477Z 2025-09-07T08:19:26.5026581Z >>> iseven = lambda x: x % 2 == 0 2025-09-07T08:19:26.5026747Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-09-07T08:19:26.5026893Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-09-07T08:19:26.5026972Z 2025-09-07T08:19:26.5027126Z Non-callable keys imply grouping on a member. 2025-09-07T08:19:26.5027203Z 2025-09-07T08:19:26.5027285Z >>> groupby( 2025-09-07T08:19:26.5027386Z ... "gender", 2025-09-07T08:19:26.5027468Z ... [ 2025-09-07T08:19:26.5027600Z ... {"name": "Alice", "gender": "F"}, 2025-09-07T08:19:26.5027710Z ... {"name": "Bob", "gender": "M"}, 2025-09-07T08:19:26.5027829Z ... {"name": "Charlie", "gender": "M"}, 2025-09-07T08:19:26.5027921Z ... ], 2025-09-07T08:19:26.5028016Z ... ) # doctest:+SKIP 2025-09-07T08:19:26.5028143Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-09-07T08:19:26.5028249Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-09-07T08:19:26.5028361Z {'gender': 'M', 'name': 'Charlie'}]} 2025-09-07T08:19:26.5028450Z 2025-09-07T08:19:26.5028587Z Not to be confused with ``itertools.groupby`` 2025-09-07T08:19:26.5028667Z 2025-09-07T08:19:26.5028760Z See Also: 2025-09-07T08:19:26.5028840Z countby 2025-09-07T08:19:26.5028931Z 2025-09-07T08:19:26.5029182Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5029259Z 2025-09-07T08:19:26.5029361Z warnings.warn(msg) 2025-09-07T08:19:26.5029437Z 2025-09-07T08:19:26.5029633Z --- Parse Warning: 102 / 146 --- 2025-09-07T08:19:26.5030470Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py line=142. 2025-09-07T08:19:26.5030759Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5031025Z Return the recommended gain value for the given nonlinearity function. 2025-09-07T08:19:26.5031104Z 2025-09-07T08:19:26.5031215Z The values are as follows: 2025-09-07T08:19:26.5031290Z 2025-09-07T08:19:26.5031400Z ================= ==================================================== 2025-09-07T08:19:26.5031510Z nonlinearity gain 2025-09-07T08:19:26.5031621Z ================= ==================================================== 2025-09-07T08:19:26.5031729Z Linear / Identity :math:`1` 2025-09-07T08:19:26.5031820Z Conv{1,2,3}D :math:`1` 2025-09-07T08:19:26.5031911Z Sigmoid :math:`1` 2025-09-07T08:19:26.5032022Z Tanh :math:`\frac{5}{3}` 2025-09-07T08:19:26.5032118Z ReLU :math:`\sqrt{2}` 2025-09-07T08:19:26.5032319Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-09-07T08:19:26.5032423Z SELU :math:`\frac{3}{4}` 2025-09-07T08:19:26.5032530Z ================= ==================================================== 2025-09-07T08:19:26.5032619Z 2025-09-07T08:19:26.5032740Z .. warning:: 2025-09-07T08:19:26.5032935Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-09-07T08:19:26.5033172Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-09-07T08:19:26.5033337Z This gives the initial weights a variance of ``1 / N``, 2025-09-07T08:19:26.5033570Z which is necessary to induce a stable fixed point in the forward pass. 2025-09-07T08:19:26.5033790Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-09-07T08:19:26.5033984Z effect for more stable gradient flow in rectangular layers. 2025-09-07T08:19:26.5034062Z 2025-09-07T08:19:26.5034146Z Args: 2025-09-07T08:19:26.5034353Z nonlinearity: the non-linear function (`nn.functional` name) 2025-09-07T08:19:26.5034521Z param: optional parameter for the non-linear function 2025-09-07T08:19:26.5034631Z 2025-09-07T08:19:26.5034716Z Examples: 2025-09-07T08:19:26.5034831Z >>> gain = nn.init.calculate_gain( 2025-09-07T08:19:26.5034934Z ... "leaky_relu", 0.2 2025-09-07T08:19:26.5035056Z ... ) # leaky_relu with negative_slope=0.2 2025-09-07T08:19:26.5035143Z 2025-09-07T08:19:26.5035623Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-09-07T08:19:26.5035704Z 2025-09-07T08:19:26.5035964Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5036043Z 2025-09-07T08:19:26.5036143Z warnings.warn(msg) 2025-09-07T08:19:26.5036218Z 2025-09-07T08:19:26.5036406Z --- Parse Warning: 103 / 146 --- 2025-09-07T08:19:26.5037327Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=603. 2025-09-07T08:19:26.5037590Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5037777Z Applies Batch Normalization over a N-Dimensional input. 2025-09-07T08:19:26.5037852Z 2025-09-07T08:19:26.5038192Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-09-07T08:19:26.5038427Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-09-07T08:19:26.5038633Z Internal Covariate Shift `__ . 2025-09-07T08:19:26.5038725Z 2025-09-07T08:19:26.5038808Z .. math:: 2025-09-07T08:19:26.5038886Z 2025-09-07T08:19:26.5039135Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-09-07T08:19:26.5039237Z 2025-09-07T08:19:26.5039476Z The mean and standard-deviation are calculated per-dimension over all 2025-09-07T08:19:26.5039707Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-09-07T08:19:26.5039946Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-09-07T08:19:26.5040128Z By default, the elements of :math:`\gamma` are sampled from 2025-09-07T08:19:26.5040322Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-09-07T08:19:26.5040588Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-09-07T08:19:26.5040701Z `torch.var(input, unbiased=False)`. 2025-09-07T08:19:26.5040777Z 2025-09-07T08:19:26.5041019Z Also by default, during training this layer keeps running estimates of its 2025-09-07T08:19:26.5041251Z computed mean and variance, which are then used for normalization during 2025-09-07T08:19:26.5041507Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-09-07T08:19:26.5041591Z of 0.1. 2025-09-07T08:19:26.5041696Z 2025-09-07T08:19:26.5041932Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-09-07T08:19:26.5042148Z keep running estimates, and batch statistics are instead used during 2025-09-07T08:19:26.5042265Z evaluation time as well. 2025-09-07T08:19:26.5042341Z 2025-09-07T08:19:26.5042421Z .. note:: 2025-09-07T08:19:26.5042647Z This :attr:`momentum` argument is different from one used in optimizer 2025-09-07T08:19:26.5042863Z classes and the conventional notion of momentum. Mathematically, the 2025-09-07T08:19:26.5043007Z update rule for running statistics here is 2025-09-07T08:19:26.5043270Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-09-07T08:19:26.5043474Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-09-07T08:19:26.5043580Z new observed value. 2025-09-07T08:19:26.5043682Z 2025-09-07T08:19:26.5043991Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-09-07T08:19:26.5044334Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-09-07T08:19:26.5044510Z Normalization or Spatio-temporal Batch Normalization. 2025-09-07T08:19:26.5044601Z 2025-09-07T08:19:26.5044743Z Currently :class:`SyncBatchNorm` only supports 2025-09-07T08:19:26.5045035Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-09-07T08:19:26.5045240Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-09-07T08:19:26.5045448Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-09-07T08:19:26.5045554Z Network with DDP. 2025-09-07T08:19:26.5045633Z 2025-09-07T08:19:26.5045726Z Args: 2025-09-07T08:19:26.5045890Z num_features: :math:`C` from an expected input of size 2025-09-07T08:19:26.5045985Z :math:`(N, C, +)` 2025-09-07T08:19:26.5046177Z eps: a value added to the denominator for numerical stability. 2025-09-07T08:19:26.5046272Z Default: ``1e-5`` 2025-09-07T08:19:26.5046470Z momentum: the value used for the running_mean and running_var 2025-09-07T08:19:26.5046672Z computation. Can be set to ``None`` for cumulative moving average 2025-09-07T08:19:26.5046792Z (i.e. simple average). Default: 0.1 2025-09-07T08:19:26.5047002Z affine: a boolean value that when set to ``True``, this module has 2025-09-07T08:19:26.5047150Z learnable affine parameters. Default: ``True`` 2025-09-07T08:19:26.5047404Z track_running_stats: a boolean value that when set to ``True``, this 2025-09-07T08:19:26.5047652Z module tracks the running mean and variance, and when set to ``False``, 2025-09-07T08:19:26.5047875Z this module does not track such statistics, and initializes statistics 2025-09-07T08:19:26.5048084Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-09-07T08:19:26.5048308Z When these buffers are ``None``, this module always uses batch statistics. 2025-09-07T08:19:26.5048468Z in both training and eval modes. Default: ``True`` 2025-09-07T08:19:26.5048711Z process_group: synchronization of stats happen within each process group 2025-09-07T08:19:26.5048934Z individually. Default behavior is synchronization across the whole 2025-09-07T08:19:26.5049030Z world 2025-09-07T08:19:26.5049108Z 2025-09-07T08:19:26.5049203Z Shape: 2025-09-07T08:19:26.5049309Z - Input: :math:`(N, C, +)` 2025-09-07T08:19:26.5049456Z - Output: :math:`(N, C, +)` (same shape as input) 2025-09-07T08:19:26.5049544Z 2025-09-07T08:19:26.5049626Z .. note:: 2025-09-07T08:19:26.5049881Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-09-07T08:19:26.5050103Z synchronization is disabled when ``model.eval()`` is set or if 2025-09-07T08:19:26.5050228Z ``self.training`` is otherwise ``False``. 2025-09-07T08:19:26.5050310Z 2025-09-07T08:19:26.5050397Z Examples:: 2025-09-07T08:19:26.5050484Z 2025-09-07T08:19:26.5050577Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5050689Z >>> # With Learnable Parameters 2025-09-07T08:19:26.5050801Z >>> m = nn.SyncBatchNorm(100) 2025-09-07T08:19:26.5050922Z >>> # creating process group (optional) 2025-09-07T08:19:26.5051070Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T08:19:26.5051170Z >>> ranks = list(range(8)) 2025-09-07T08:19:26.5051274Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T08:19:26.5051431Z >>> # Note: every rank calls into new_group for every 2025-09-07T08:19:26.5051602Z >>> # process group created, even if that rank is not 2025-09-07T08:19:26.5051718Z >>> # part of the group. 2025-09-07T08:19:26.5051961Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T08:19:26.5052159Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T08:19:26.5052284Z >>> # Without Learnable Parameters 2025-09-07T08:19:26.5052482Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-09-07T08:19:26.5052612Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-09-07T08:19:26.5052706Z >>> output = m(input) 2025-09-07T08:19:26.5052780Z 2025-09-07T08:19:26.5052905Z >>> # network is nn.BatchNorm layer 2025-09-07T08:19:26.5053176Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-09-07T08:19:26.5053350Z >>> # only single gpu per process is currently supported 2025-09-07T08:19:26.5053562Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-09-07T08:19:26.5053670Z >>> sync_bn_network, 2025-09-07T08:19:26.5053802Z >>> device_ids=[args.local_rank], 2025-09-07T08:19:26.5053928Z >>> output_device=args.local_rank) 2025-09-07T08:19:26.5054018Z 2025-09-07T08:19:26.5054268Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5054344Z 2025-09-07T08:19:26.5054448Z warnings.warn(msg) 2025-09-07T08:19:26.5054524Z 2025-09-07T08:19:26.5054740Z --- Parse Warning: 104 / 146 --- 2025-09-07T08:19:26.5055806Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=830. 2025-09-07T08:19:26.5056091Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5056405Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-09-07T08:19:26.5056482Z 2025-09-07T08:19:26.5056575Z Args: 2025-09-07T08:19:26.5056811Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-09-07T08:19:26.5057022Z process_group (optional): process group to scope synchronization, 2025-09-07T08:19:26.5057146Z default is the whole world 2025-09-07T08:19:26.5057223Z 2025-09-07T08:19:26.5057318Z Returns: 2025-09-07T08:19:26.5057568Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-09-07T08:19:26.5057782Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-09-07T08:19:26.5058003Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-09-07T08:19:26.5058114Z instead. 2025-09-07T08:19:26.5058201Z 2025-09-07T08:19:26.5058285Z Example:: 2025-09-07T08:19:26.5058360Z 2025-09-07T08:19:26.5058490Z >>> # Network with nn.BatchNorm layer 2025-09-07T08:19:26.5058634Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:26.5058749Z >>> module = torch.nn.Sequential( 2025-09-07T08:19:26.5058879Z >>> torch.nn.Linear(20, 100), 2025-09-07T08:19:26.5058997Z >>> torch.nn.BatchNorm1d(100), 2025-09-07T08:19:26.5059088Z >>> ).cuda() 2025-09-07T08:19:26.5059228Z >>> # creating process group (optional) 2025-09-07T08:19:26.5059368Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T08:19:26.5059487Z >>> ranks = list(range(8)) 2025-09-07T08:19:26.5059619Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T08:19:26.5059767Z >>> # Note: every rank calls into new_group for every 2025-09-07T08:19:26.5059931Z >>> # process group created, even if that rank is not 2025-09-07T08:19:26.5060029Z >>> # part of the group. 2025-09-07T08:19:26.5060161Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T08:19:26.5060407Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T08:19:26.5060612Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T08:19:26.5060911Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-09-07T08:19:26.5060992Z 2025-09-07T08:19:26.5061082Z 2025-09-07T08:19:26.5061334Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5061414Z 2025-09-07T08:19:26.5061519Z warnings.warn(msg) 2025-09-07T08:19:26.5061597Z 2025-09-07T08:19:26.5061796Z --- Parse Warning: 105 / 146 --- 2025-09-07T08:19:26.5062665Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=66. 2025-09-07T08:19:26.5062925Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5063011Z 2025-09-07T08:19:26.5063316Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-09-07T08:19:26.5063402Z 2025-09-07T08:19:26.5063700Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-09-07T08:19:26.5063922Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-09-07T08:19:26.5064034Z 2025-09-07T08:19:26.5064346Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-09-07T08:19:26.5064603Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-09-07T08:19:26.5064768Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-09-07T08:19:26.5064844Z 2025-09-07T08:19:26.5064935Z Shape: 2025-09-07T08:19:26.5065143Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-09-07T08:19:26.5065396Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-09-07T08:19:26.5065608Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-09-07T08:19:26.5065733Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-09-07T08:19:26.5065823Z 2025-09-07T08:19:26.5065902Z Args: 2025-09-07T08:19:26.5066059Z dim (Union[int, str]): Dimension to be unflattened 2025-09-07T08:19:26.5066401Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-09-07T08:19:26.5066502Z 2025-09-07T08:19:26.5066592Z Examples: 2025-09-07T08:19:26.5066697Z >>> input = torch.randn(2, 50) 2025-09-07T08:19:26.5066808Z >>> # With tuple of ints 2025-09-07T08:19:26.5066901Z >>> m = nn.Sequential( 2025-09-07T08:19:26.5066992Z >>> nn.Linear(50, 50), 2025-09-07T08:19:26.5067110Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-09-07T08:19:26.5067187Z >>> ) 2025-09-07T08:19:26.5067290Z >>> output = m(input) 2025-09-07T08:19:26.5067382Z >>> output.size() 2025-09-07T08:19:26.5067474Z torch.Size([2, 2, 5, 5]) 2025-09-07T08:19:26.5067575Z >>> # With torch.Size 2025-09-07T08:19:26.5067672Z >>> m = nn.Sequential( 2025-09-07T08:19:26.5067766Z >>> nn.Linear(50, 50), 2025-09-07T08:19:26.5067897Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-09-07T08:19:26.5067972Z >>> ) 2025-09-07T08:19:26.5068100Z >>> output = m(input) 2025-09-07T08:19:26.5068192Z >>> output.size() 2025-09-07T08:19:26.5068283Z torch.Size([2, 2, 5, 5]) 2025-09-07T08:19:26.5068409Z >>> # With namedshape (tuple of tuples) 2025-09-07T08:19:26.5068553Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-09-07T08:19:26.5068765Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-09-07T08:19:26.5068869Z >>> output = unflatten(input) 2025-09-07T08:19:26.5068960Z >>> output.size() 2025-09-07T08:19:26.5069063Z torch.Size([2, 2, 5, 5]) 2025-09-07T08:19:26.5069135Z 2025-09-07T08:19:26.5069396Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5069473Z 2025-09-07T08:19:26.5069562Z warnings.warn(msg) 2025-09-07T08:19:26.5069654Z 2025-09-07T08:19:26.5069840Z --- Parse Warning: 106 / 146 --- 2025-09-07T08:19:26.5070835Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1798. 2025-09-07T08:19:26.5071097Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5071295Z Creates a criterion that measures the triplet loss given input 2025-09-07T08:19:26.5071495Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-09-07T08:19:26.5071704Z positive, and negative examples, respectively), and a nonnegative, 2025-09-07T08:19:26.5071961Z real-valued function ("distance function") used to compute the relationship 2025-09-07T08:19:26.5072202Z between the anchor and positive example ("positive distance") and the 2025-09-07T08:19:26.5072375Z anchor and negative example ("negative distance"). 2025-09-07T08:19:26.5072464Z 2025-09-07T08:19:26.5072669Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-09-07T08:19:26.5072781Z can be described as: 2025-09-07T08:19:26.5072858Z 2025-09-07T08:19:26.5072942Z .. math:: 2025-09-07T08:19:26.5073089Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-09-07T08:19:26.5073383Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-09-07T08:19:26.5073479Z 2025-09-07T08:19:26.5073722Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-09-07T08:19:26.5074013Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-09-07T08:19:26.5074264Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-09-07T08:19:26.5074505Z between the positive and negative distances that is required for the loss to 2025-09-07T08:19:26.5074745Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-09-07T08:19:26.5074924Z that the distance function can handle. 2025-09-07T08:19:26.5075000Z 2025-09-07T08:19:26.5075132Z If :attr:`reduction` is not ``'none'`` 2025-09-07T08:19:26.5075230Z (default ``'mean'``), then: 2025-09-07T08:19:26.5075304Z 2025-09-07T08:19:26.5075400Z .. math:: 2025-09-07T08:19:26.5075488Z \ell(x, y) = 2025-09-07T08:19:26.5075583Z \begin{cases} 2025-09-07T08:19:26.5075782Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-09-07T08:19:26.5075965Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-09-07T08:19:26.5076059Z \end{cases} 2025-09-07T08:19:26.5076136Z 2025-09-07T08:19:26.5076376Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-09-07T08:19:26.5076624Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-09-07T08:19:26.5076699Z 2025-09-07T08:19:26.5076827Z Args: 2025-09-07T08:19:26.5077099Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-09-07T08:19:26.5077294Z quantifies the closeness of two tensors. If not specified, 2025-09-07T08:19:26.5077462Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-09-07T08:19:26.5077723Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-09-07T08:19:26.5078001Z between the positive and negative distances required for the loss to be 0. Larger 2025-09-07T08:19:26.5078274Z margins penalize cases where the negative examples are not distant enough from the 2025-09-07T08:19:26.5078460Z anchors, relative to the positives. Default: :math:`1`. 2025-09-07T08:19:26.5078702Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-09-07T08:19:26.5078972Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-09-07T08:19:26.5079203Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-09-07T08:19:26.5079463Z negative example than the anchor is, swaps the positive example and the anchor in 2025-09-07T08:19:26.5079606Z the loss computation. Default: ``False``. 2025-09-07T08:19:26.5079878Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-09-07T08:19:26.5080068Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T08:19:26.5080243Z ``'mean'``: the sum of the output will be divided by the number of 2025-09-07T08:19:26.5080508Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-09-07T08:19:26.5080626Z 2025-09-07T08:19:26.5080700Z 2025-09-07T08:19:26.5080795Z Shape: 2025-09-07T08:19:26.5081031Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-09-07T08:19:26.5081157Z as supported by the distance function. 2025-09-07T08:19:26.5081414Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-09-07T08:19:26.5081497Z otherwise. 2025-09-07T08:19:26.5081583Z 2025-09-07T08:19:26.5081669Z Examples: 2025-09-07T08:19:26.5081741Z 2025-09-07T08:19:26.5081849Z >>> # Initialize embeddings 2025-09-07T08:19:26.5081959Z >>> embedding = nn.Embedding(1000, 128) 2025-09-07T08:19:26.5082095Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-09-07T08:19:26.5082221Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-09-07T08:19:26.5082342Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-09-07T08:19:26.5082460Z >>> anchor = embedding(anchor_ids) 2025-09-07T08:19:26.5082573Z >>> positive = embedding(positive_ids) 2025-09-07T08:19:26.5082700Z >>> negative = embedding(negative_ids) 2025-09-07T08:19:26.5082801Z >>> 2025-09-07T08:19:26.5082905Z >>> # Built-in Distance Function 2025-09-07T08:19:26.5083007Z >>> triplet_loss = \ 2025-09-07T08:19:26.5083279Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-09-07T08:19:26.5083427Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T08:19:26.5083530Z >>> output.backward() 2025-09-07T08:19:26.5083610Z >>> 2025-09-07T08:19:26.5083728Z >>> # Custom Distance Function 2025-09-07T08:19:26.5083822Z >>> def l_infinity(x1, x2): 2025-09-07T08:19:26.5083970Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-09-07T08:19:26.5084056Z >>> 2025-09-07T08:19:26.5084308Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-09-07T08:19:26.5084419Z >>> triplet_loss = ( 2025-09-07T08:19:26.5084735Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-09-07T08:19:26.5084885Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T08:19:26.5084988Z >>> output.backward() 2025-09-07T08:19:26.5085064Z >>> 2025-09-07T08:19:26.5085189Z >>> # Custom Distance Function (Lambda) 2025-09-07T08:19:26.5085278Z >>> triplet_loss = ( 2025-09-07T08:19:26.5085406Z >>> nn.TripletMarginWithDistanceLoss( 2025-09-07T08:19:26.5085629Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-09-07T08:19:26.5085776Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T08:19:26.5085881Z >>> output.backward() 2025-09-07T08:19:26.5085954Z 2025-09-07T08:19:26.5086038Z Reference: 2025-09-07T08:19:26.5086350Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-09-07T08:19:26.5086566Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-09-07T08:19:26.5086655Z 2025-09-07T08:19:26.5086902Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-09-07T08:19:26.5086978Z 2025-09-07T08:19:26.5087079Z warnings.warn(msg) 2025-09-07T08:19:26.5087155Z 2025-09-07T08:19:26.5087369Z --- Parse Warning: 107 / 146 --- 2025-09-07T08:19:26.5088233Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1933. 2025-09-07T08:19:26.5088493Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5088707Z The Connectionist Temporal Classification loss. 2025-09-07T08:19:26.5088806Z 2025-09-07T08:19:26.5089196Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-09-07T08:19:26.5089549Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-09-07T08:19:26.5089887Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-09-07T08:19:26.5090193Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-09-07T08:19:26.5090268Z 2025-09-07T08:19:26.5090359Z Args: 2025-09-07T08:19:26.5090519Z blank (int, optional): blank label. Default :math:`0`. 2025-09-07T08:19:26.5090757Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-09-07T08:19:26.5090950Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T08:19:26.5091152Z ``'mean'``: the output losses will be divided by the target lengths and 2025-09-07T08:19:26.5091406Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-09-07T08:19:26.5091524Z Default: ``'mean'`` 2025-09-07T08:19:26.5091644Z zero_infinity (bool, optional): 2025-09-07T08:19:26.5091837Z Whether to zero infinite losses and the associated gradients. 2025-09-07T08:19:26.5091933Z Default: ``False`` 2025-09-07T08:19:26.5092123Z Infinite losses mainly occur when the inputs are too short 2025-09-07T08:19:26.5092227Z to be aligned to the targets. 2025-09-07T08:19:26.5092305Z 2025-09-07T08:19:26.5092393Z Shape: 2025-09-07T08:19:26.5092576Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-09-07T08:19:26.5092706Z where :math:`T = \text{input length}`, 2025-09-07T08:19:26.5092815Z :math:`N = \text{batch size}`, and 2025-09-07T08:19:26.5092969Z :math:`C = \text{number of classes (including blank)}`. 2025-09-07T08:19:26.5093223Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-09-07T08:19:26.5093358Z :func:`torch.nn.functional.log_softmax`). 2025-09-07T08:19:26.5093490Z - Targets: Tensor of size :math:`(N, S)` or 2025-09-07T08:19:26.5093643Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-09-07T08:19:26.5093762Z where :math:`N = \text{batch size}` and 2025-09-07T08:19:26.5093927Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-09-07T08:19:26.5094120Z It represents the target sequences. Each element in the target 2025-09-07T08:19:26.5094362Z sequence is a class index. And the target index cannot be blank (default=0). 2025-09-07T08:19:26.5094511Z In the :math:`(N, S)` form, targets are padded to the 2025-09-07T08:19:26.5094662Z length of the longest sequence, and stacked. 2025-09-07T08:19:26.5094847Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-09-07T08:19:26.5094981Z the targets are assumed to be un-padded and 2025-09-07T08:19:26.5095102Z concatenated within 1 dimension. 2025-09-07T08:19:26.5095304Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T08:19:26.5095504Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-09-07T08:19:26.5095697Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-09-07T08:19:26.5095919Z for each sequence to achieve masking under the assumption that sequences 2025-09-07T08:19:26.5096037Z are padded to equal lengths. 2025-09-07T08:19:26.5096258Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T08:19:26.5096476Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-09-07T08:19:26.5096724Z Lengths are specified for each sequence to achieve masking under the 2025-09-07T08:19:26.5096971Z assumption that sequences are padded to equal lengths. If target shape is 2025-09-07T08:19:26.5097145Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-09-07T08:19:26.5097394Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-09-07T08:19:26.5097578Z each target in a batch. Lengths must each be :math:`\leq S` 2025-09-07T08:19:26.5097817Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-09-07T08:19:26.5098066Z targets, the target_lengths must add up to the total length of the tensor. 2025-09-07T08:19:26.5098253Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-09-07T08:19:26.5098483Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-09-07T08:19:26.5098690Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-09-07T08:19:26.5098789Z 2025-09-07T08:19:26.5098885Z Examples: 2025-09-07T08:19:26.5098960Z 2025-09-07T08:19:26.5099066Z >>> # Target are to be padded 2025-09-07T08:19:26.5099190Z >>> T = 50 # Input sequence length 2025-09-07T08:19:26.5099317Z >>> C = 20 # Number of classes (including blank) 2025-09-07T08:19:26.5099408Z >>> N = 16 # Batch size 2025-09-07T08:19:26.5099644Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-09-07T08:19:26.5099821Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-09-07T08:19:26.5099913Z >>> 2025-09-07T08:19:26.5100105Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T08:19:26.5100323Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T08:19:26.5100405Z >>> 2025-09-07T08:19:26.5100623Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T08:19:26.5100848Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-09-07T08:19:26.5100924Z >>> 2025-09-07T08:19:26.5101148Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T08:19:26.5101262Z >>> target_lengths = torch.randint( 2025-09-07T08:19:26.5101352Z ... low=S_min, 2025-09-07T08:19:26.5101448Z ... high=S, 2025-09-07T08:19:26.5101532Z ... size=(N,), 2025-09-07T08:19:26.5101629Z ... dtype=torch.long, 2025-09-07T08:19:26.5101719Z ... ) 2025-09-07T08:19:26.5101816Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T08:19:26.5102009Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T08:19:26.5102104Z >>> loss.backward() 2025-09-07T08:19:26.5102181Z >>> 2025-09-07T08:19:26.5102272Z >>> 2025-09-07T08:19:26.5102381Z >>> # Target are to be un-padded 2025-09-07T08:19:26.5102501Z >>> T = 50 # Input sequence length 2025-09-07T08:19:26.5102630Z >>> C = 20 # Number of classes (including blank) 2025-09-07T08:19:26.5102719Z >>> N = 16 # Batch size 2025-09-07T08:19:26.5102806Z >>> 2025-09-07T08:19:26.5103002Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T08:19:26.5103210Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T08:19:26.5103422Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T08:19:26.5103501Z >>> 2025-09-07T08:19:26.5103721Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T08:19:26.5103980Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-09-07T08:19:26.5104089Z >>> target = torch.randint( 2025-09-07T08:19:26.5104173Z ... low=1, 2025-09-07T08:19:26.5104257Z ... high=C, 2025-09-07T08:19:26.5104373Z ... size=(sum(target_lengths),), 2025-09-07T08:19:26.5104469Z ... dtype=torch.long, 2025-09-07T08:19:26.5104557Z ... ) 2025-09-07T08:19:26.5104650Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T08:19:26.5104827Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T08:19:26.5104929Z >>> loss.backward() 2025-09-07T08:19:26.5105009Z >>> 2025-09-07T08:19:26.5105098Z >>> 2025-09-07T08:19:26.5105275Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-09-07T08:19:26.5105387Z >>> T = 50 # Input sequence length 2025-09-07T08:19:26.5105526Z >>> C = 20 # Number of classes (including blank) 2025-09-07T08:19:26.5105607Z >>> 2025-09-07T08:19:26.5105807Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-09-07T08:19:26.5105962Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-09-07T08:19:26.5106158Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-09-07T08:19:26.5106320Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-09-07T08:19:26.5106398Z >>> 2025-09-07T08:19:26.5106597Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T08:19:26.5106826Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-09-07T08:19:26.5106925Z >>> target = torch.randint( 2025-09-07T08:19:26.5107021Z ... low=1, 2025-09-07T08:19:26.5107105Z ... high=C, 2025-09-07T08:19:26.5107221Z ... size=(target_lengths,), 2025-09-07T08:19:26.5107317Z ... dtype=torch.long, 2025-09-07T08:19:26.5107393Z ... ) 2025-09-07T08:19:26.5107528Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T08:19:26.5107709Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T08:19:26.5107804Z >>> loss.backward() 2025-09-07T08:19:26.5107893Z 2025-09-07T08:19:26.5107975Z Reference: 2025-09-07T08:19:26.5108155Z A. Graves et al.: Connectionist Temporal Classification: 2025-09-07T08:19:26.5108380Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-09-07T08:19:26.5108534Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-09-07T08:19:26.5108615Z 2025-09-07T08:19:26.5108695Z Note: 2025-09-07T08:19:26.5108944Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-09-07T08:19:26.5109195Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-09-07T08:19:26.5109411Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-09-07T08:19:26.5109524Z dtype :attr:`torch.int32`. 2025-09-07T08:19:26.5109599Z 2025-09-07T08:19:26.5109874Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-09-07T08:19:26.5109950Z 2025-09-07T08:19:26.5110023Z 2025-09-07T08:19:26.5110111Z Note: 2025-09-07T08:19:26.5110351Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-09-07T08:19:26.5110610Z may select a nondeterministic algorithm to increase performance. If this is 2025-09-07T08:19:26.5110849Z undesirable, you can try to make the operation deterministic (potentially at 2025-09-07T08:19:26.5111104Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-09-07T08:19:26.5111191Z True``. 2025-09-07T08:19:26.5111407Z Please see the notes on :doc:`/notes/randomness` for background. 2025-09-07T08:19:26.5111489Z 2025-09-07T08:19:26.5111741Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5111815Z 2025-09-07T08:19:26.5111916Z warnings.warn(msg) 2025-09-07T08:19:26.5111990Z 2025-09-07T08:19:26.5112201Z --- Parse Warning: 108 / 146 --- 2025-09-07T08:19:26.5113104Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=410. 2025-09-07T08:19:26.5113364Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5113523Z Computes a partial inverse of :class:`MaxPool2d`. 2025-09-07T08:19:26.5113601Z 2025-09-07T08:19:26.5113869Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-09-07T08:19:26.5113946Z 2025-09-07T08:19:26.5114169Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-09-07T08:19:26.5114455Z including the indices of the maximal values and computes a partial inverse 2025-09-07T08:19:26.5114599Z in which all non-maximal values are set to zero. 2025-09-07T08:19:26.5114682Z 2025-09-07T08:19:26.5114760Z Note: 2025-09-07T08:19:26.5115073Z This operation may behave nondeterministically when the input indices has repeat values. 2025-09-07T08:19:26.5115459Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-09-07T08:19:26.5115534Z 2025-09-07T08:19:26.5115769Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-09-07T08:19:26.5115938Z sizes. Hence, the inversion process can get ambiguous. 2025-09-07T08:19:26.5116120Z To accommodate this, you can provide the needed output size 2025-09-07T08:19:26.5116354Z as an additional argument :attr:`output_size` in the forward call. 2025-09-07T08:19:26.5116473Z See the Inputs and Example below. 2025-09-07T08:19:26.5116554Z 2025-09-07T08:19:26.5116629Z Args: 2025-09-07T08:19:26.5116804Z kernel_size (int or tuple): Size of the max pooling window. 2025-09-07T08:19:26.5116977Z stride (int or tuple): Stride of the max pooling window. 2025-09-07T08:19:26.5117108Z It is set to :attr:`kernel_size` by default. 2025-09-07T08:19:26.5117290Z padding (int or tuple): Padding that was added to the input 2025-09-07T08:19:26.5117362Z 2025-09-07T08:19:26.5117439Z Inputs: 2025-09-07T08:19:26.5117557Z - `input`: the input Tensor to invert 2025-09-07T08:19:26.5117759Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-09-07T08:19:26.5117923Z - `output_size` (optional): the targeted output size 2025-09-07T08:19:26.5118001Z 2025-09-07T08:19:26.5118081Z Shape: 2025-09-07T08:19:26.5118264Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-09-07T08:19:26.5118472Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-09-07T08:19:26.5118553Z 2025-09-07T08:19:26.5118638Z .. math:: 2025-09-07T08:19:26.5118906Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-09-07T08:19:26.5118987Z 2025-09-07T08:19:26.5119069Z .. math:: 2025-09-07T08:19:26.5119330Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-09-07T08:19:26.5119402Z 2025-09-07T08:19:26.5119585Z or as given by :attr:`output_size` in the call operator 2025-09-07T08:19:26.5119665Z 2025-09-07T08:19:26.5119773Z Example:: 2025-09-07T08:19:26.5119853Z 2025-09-07T08:19:26.5120005Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-09-07T08:19:26.5120130Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-09-07T08:19:26.5120263Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-09-07T08:19:26.5120366Z [ 5., 6., 7., 8.], 2025-09-07T08:19:26.5120474Z [ 9., 10., 11., 12.], 2025-09-07T08:19:26.5120578Z [13., 14., 15., 16.]]]]) 2025-09-07T08:19:26.5120688Z >>> output, indices = pool(input) 2025-09-07T08:19:26.5120796Z >>> unpool(output, indices) 2025-09-07T08:19:26.5120896Z tensor([[[[ 0., 0., 0., 0.], 2025-09-07T08:19:26.5120995Z [ 0., 6., 0., 8.], 2025-09-07T08:19:26.5121090Z [ 0., 0., 0., 0.], 2025-09-07T08:19:26.5121186Z [ 0., 14., 0., 16.]]]]) 2025-09-07T08:19:26.5121396Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-09-07T08:19:26.5121535Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-09-07T08:19:26.5121674Z [ 6., 7., 8., 9., 10.], 2025-09-07T08:19:26.5121775Z [11., 12., 13., 14., 15.], 2025-09-07T08:19:26.5121880Z [16., 17., 18., 19., 20.]]]]) 2025-09-07T08:19:26.5121993Z >>> output, indices = pool(input) 2025-09-07T08:19:26.5122157Z >>> # This call will not work without specifying output_size 2025-09-07T08:19:26.5122312Z >>> unpool(output, indices, output_size=input.size()) 2025-09-07T08:19:26.5122412Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-09-07T08:19:26.5122507Z [ 0., 7., 0., 9., 0.], 2025-09-07T08:19:26.5122601Z [ 0., 0., 0., 0., 0.], 2025-09-07T08:19:26.5122697Z [ 0., 17., 0., 19., 0.]]]]) 2025-09-07T08:19:26.5122774Z 2025-09-07T08:19:26.5122869Z 2025-09-07T08:19:26.5122952Z 2025-09-07T08:19:26.5123203Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5123279Z 2025-09-07T08:19:26.5123371Z warnings.warn(msg) 2025-09-07T08:19:26.5123456Z 2025-09-07T08:19:26.5123645Z --- Parse Warning: 109 / 146 --- 2025-09-07T08:19:26.5124629Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=272. 2025-09-07T08:19:26.5124891Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5125219Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-09-07T08:19:26.5125297Z 2025-09-07T08:19:26.5125617Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-09-07T08:19:26.5125737Z and with 2D inputs, this class 2025-09-07T08:19:26.5125811Z 2025-09-07T08:19:26.5126119Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-09-07T08:19:26.5126427Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-09-07T08:19:26.5126718Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-09-07T08:19:26.5126800Z 2025-09-07T08:19:26.5127150Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-09-07T08:19:26.5127240Z operations. 2025-09-07T08:19:26.5127346Z 2025-09-07T08:19:26.5127604Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-09-07T08:19:26.5127870Z pass. This scales the output of the Embedding before performing a weighted 2025-09-07T08:19:26.5128115Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-09-07T08:19:26.5128363Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-09-07T08:19:26.5128467Z :attr:`per_sample_weights`. 2025-09-07T08:19:26.5128541Z 2025-09-07T08:19:26.5128628Z Args: 2025-09-07T08:19:26.5128807Z num_embeddings (int): size of the dictionary of embeddings 2025-09-07T08:19:26.5128978Z embedding_dim (int): the size of each embedding vector 2025-09-07T08:19:26.5129288Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-09-07T08:19:26.5129436Z is renormalized to have norm :attr:`max_norm`. 2025-09-07T08:19:26.5129774Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-09-07T08:19:26.5130100Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-09-07T08:19:26.5130289Z the words in the mini-batch. Default ``False``. 2025-09-07T08:19:26.5130467Z Note: this option is not supported when ``mode="max"``. 2025-09-07T08:19:26.5130718Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-09-07T08:19:26.5130936Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-09-07T08:19:26.5131151Z into consideration. ``"mean"`` computes the average of the values 2025-09-07T08:19:26.5131333Z in the bag, ``"max"`` computes the max value over each bag. 2025-09-07T08:19:26.5131447Z Default: ``"mean"`` 2025-09-07T08:19:26.5131789Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-09-07T08:19:26.5132045Z Notes for more details regarding sparse gradients. Note: this option is not 2025-09-07T08:19:26.5132171Z supported when ``mode="max"``. 2025-09-07T08:19:26.5132545Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-09-07T08:19:26.5132765Z is equivalent to the size of `indices`. This matches the CSR format. 2025-09-07T08:19:26.5133097Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-09-07T08:19:26.5133375Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-09-07T08:19:26.5133623Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-09-07T08:19:26.5133891Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-09-07T08:19:26.5134130Z zeros, but can be updated to another value to be used as the padding vector. 2025-09-07T08:19:26.5134381Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-09-07T08:19:26.5134487Z reduction. 2025-09-07T08:19:26.5134570Z 2025-09-07T08:19:26.5134653Z Attributes: 2025-09-07T08:19:26.5134987Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-09-07T08:19:26.5135132Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-09-07T08:19:26.5135231Z 2025-09-07T08:19:26.5135339Z Examples:: 2025-09-07T08:19:26.5135414Z 2025-09-07T08:19:26.5135581Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-09-07T08:19:26.5135753Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-09-07T08:19:26.5135880Z >>> # a batch of 2 samples of 4 indices each 2025-09-07T08:19:26.5136078Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T08:19:26.5136220Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T08:19:26.5136359Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:26.5136484Z >>> embedding_sum(input, offsets) 2025-09-07T08:19:26.5136591Z tensor([[-0.8861, -5.4350, -0.0523], 2025-09-07T08:19:26.5136707Z [ 1.1306, -2.5798, -1.0044]]) 2025-09-07T08:19:26.5136781Z 2025-09-07T08:19:26.5136890Z >>> # Example with padding_idx 2025-09-07T08:19:26.5137111Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-09-07T08:19:26.5137291Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T08:19:26.5137476Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T08:19:26.5137585Z >>> embedding_sum(input, offsets) 2025-09-07T08:19:26.5137694Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-09-07T08:19:26.5137806Z [-0.7082, 3.2145, -2.6251]]) 2025-09-07T08:19:26.5137884Z 2025-09-07T08:19:26.5138070Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-09-07T08:19:26.5138220Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-09-07T08:19:26.5138373Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-09-07T08:19:26.5138491Z embedding.weight, 2025-09-07T08:19:26.5138619Z padding_idx=embedding.padding_idx, 2025-09-07T08:19:26.5138728Z mode='sum') 2025-09-07T08:19:26.5138809Z 2025-09-07T08:19:26.5139086Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5139181Z 2025-09-07T08:19:26.5139274Z warnings.warn(msg) 2025-09-07T08:19:26.5139359Z 2025-09-07T08:19:26.5139555Z --- Parse Warning: 110 / 146 --- 2025-09-07T08:19:26.5140508Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py line=186. 2025-09-07T08:19:26.5140777Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5140925Z Take in and process masked source/target sequences. 2025-09-07T08:19:26.5141018Z 2025-09-07T08:19:26.5141104Z .. note:: 2025-09-07T08:19:26.5141183Z 2025-09-07T08:19:26.5141581Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-09-07T08:19:26.5141728Z not allowed to participate in the attention, 2025-09-07T08:19:26.5141928Z which is the opposite of the definition for :attr:`attn_mask` 2025-09-07T08:19:26.5142116Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-09-07T08:19:26.5142193Z 2025-09-07T08:19:26.5142286Z Args: 2025-09-07T08:19:26.5142419Z src: the sequence to the encoder (required). 2025-09-07T08:19:26.5142564Z tgt: the sequence to the decoder (required). 2025-09-07T08:19:26.5142741Z src_mask: the additive mask for the src sequence (optional). 2025-09-07T08:19:26.5142918Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-09-07T08:19:26.5143158Z memory_mask: the additive mask for the encoder output (optional). 2025-09-07T08:19:26.5143419Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-09-07T08:19:26.5143653Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-09-07T08:19:26.5143901Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-09-07T08:19:26.5144115Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-09-07T08:19:26.5144258Z Default: ``None``; try to detect a causal mask. 2025-09-07T08:19:26.5144344Z Warning: 2025-09-07T08:19:26.5144510Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-09-07T08:19:26.5144680Z the causal mask. Providing incorrect hints can result in 2025-09-07T08:19:26.5144851Z incorrect execution, including forward and backward 2025-09-07T08:19:26.5144948Z compatibility. 2025-09-07T08:19:26.5145148Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-09-07T08:19:26.5145308Z Default: ``None``; try to detect a causal mask. 2025-09-07T08:19:26.5145423Z Warning: 2025-09-07T08:19:26.5145586Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-09-07T08:19:26.5145758Z the causal mask. Providing incorrect hints can result in 2025-09-07T08:19:26.5145913Z incorrect execution, including forward and backward 2025-09-07T08:19:26.5146020Z compatibility. 2025-09-07T08:19:26.5146191Z memory_is_causal: If specified, applies a causal mask as 2025-09-07T08:19:26.5146294Z ``memory_mask``. 2025-09-07T08:19:26.5146396Z Default: ``False``. 2025-09-07T08:19:26.5146482Z Warning: 2025-09-07T08:19:26.5146627Z ``memory_is_causal`` provides a hint that 2025-09-07T08:19:26.5146791Z ``memory_mask`` is the causal mask. Providing incorrect 2025-09-07T08:19:26.5146980Z hints can result in incorrect execution, including 2025-09-07T08:19:26.5147111Z forward and backward compatibility. 2025-09-07T08:19:26.5147185Z 2025-09-07T08:19:26.5147280Z Shape: 2025-09-07T08:19:26.5147524Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-09-07T08:19:26.5147649Z `(N, S, E)` if `batch_first=True`. 2025-09-07T08:19:26.5147887Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T08:19:26.5147998Z `(N, T, E)` if `batch_first=True`. 2025-09-07T08:19:26.5148194Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-09-07T08:19:26.5148374Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-09-07T08:19:26.5148495Z - memory_mask: :math:`(T, S)`. 2025-09-07T08:19:26.5148737Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T08:19:26.5148972Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-09-07T08:19:26.5149237Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T08:19:26.5149314Z 2025-09-07T08:19:26.5149631Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-09-07T08:19:26.5149838Z positions. If a BoolTensor is provided, positions with ``True`` 2025-09-07T08:19:26.5150098Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-09-07T08:19:26.5150299Z is provided, it will be added to the attention weight. 2025-09-07T08:19:26.5150627Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-09-07T08:19:26.5150853Z the attention. If a BoolTensor is provided, the positions with the 2025-09-07T08:19:26.5151165Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-09-07T08:19:26.5151253Z 2025-09-07T08:19:26.5151506Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T08:19:26.5151621Z `(N, T, E)` if `batch_first=True`. 2025-09-07T08:19:26.5151706Z 2025-09-07T08:19:26.5151946Z Note: Due to the multi-head attention architecture in the transformer model, 2025-09-07T08:19:26.5152189Z the output sequence length of a transformer is same as the input sequence 2025-09-07T08:19:26.5152309Z (i.e. target) length of the decoder. 2025-09-07T08:19:26.5152386Z 2025-09-07T08:19:26.5152718Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-09-07T08:19:26.5152851Z batch size, :math:`E` is the feature number 2025-09-07T08:19:26.5152960Z 2025-09-07T08:19:26.5153048Z Examples: 2025-09-07T08:19:26.5153148Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5153274Z >>> output = transformer_model( 2025-09-07T08:19:26.5153418Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-09-07T08:19:26.5153508Z ... ) 2025-09-07T08:19:26.5153587Z 2025-09-07T08:19:26.5153837Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5153927Z 2025-09-07T08:19:26.5154019Z warnings.warn(msg) 2025-09-07T08:19:26.5154093Z 2025-09-07T08:19:26.5154301Z --- Parse Warning: 111 / 146 --- 2025-09-07T08:19:26.5155339Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1766. 2025-09-07T08:19:26.5155615Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5155691Z 2025-09-07T08:19:26.5155937Z Context manager for training with uneven inputs across processes in DDP. 2025-09-07T08:19:26.5156013Z 2025-09-07T08:19:26.5156233Z This context manager will keep track of already-joined DDP processes, 2025-09-07T08:19:26.5156452Z and "shadow" the forward and backward passes by inserting collective 2025-09-07T08:19:26.5156682Z communication operations to match with the ones created by non-joined 2025-09-07T08:19:26.5156919Z DDP processes. This will ensure each collective call has a corresponding 2025-09-07T08:19:26.5157139Z call by already-joined DDP processes, preventing hangs or errors that 2025-09-07T08:19:26.5157339Z would otherwise happen when training with uneven inputs across 2025-09-07T08:19:26.5157586Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-09-07T08:19:26.5157795Z specified to be ``True``, all trainers will throw an error once one rank 2025-09-07T08:19:26.5157998Z runs out of inputs, allowing these errors to be caught and handled 2025-09-07T08:19:26.5158104Z according to application logic. 2025-09-07T08:19:26.5158178Z 2025-09-07T08:19:26.5158400Z Once all DDP processes have joined, the context manager will broadcast 2025-09-07T08:19:26.5158621Z the model corresponding to the last joined process to all processes to 2025-09-07T08:19:26.5158773Z ensure the model is the same across all processes 2025-09-07T08:19:26.5158878Z (which is guaranteed by DDP). 2025-09-07T08:19:26.5158955Z 2025-09-07T08:19:26.5159187Z To use this to enable training with uneven inputs across processes, 2025-09-07T08:19:26.5159430Z simply wrap this context manager around your training loop. No further 2025-09-07T08:19:26.5159610Z modifications to the model or data loading is required. 2025-09-07T08:19:26.5159691Z 2025-09-07T08:19:26.5159776Z .. warning:: 2025-09-07T08:19:26.5159994Z If the model or training loop this context manager is wrapped around 2025-09-07T08:19:26.5160175Z has additional distributed collective operations, such as 2025-09-07T08:19:26.5160368Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-09-07T08:19:26.5160571Z ``throw_on_early_termination`` must be enabled. This is because this 2025-09-07T08:19:26.5160781Z context manager is not aware of non-DDP collective communication. 2025-09-07T08:19:26.5160959Z This flag will cause all ranks to throw when any one rank 2025-09-07T08:19:26.5161166Z exhausts inputs, allowing these errors to be caught and recovered 2025-09-07T08:19:26.5161277Z from across all ranks. 2025-09-07T08:19:26.5161358Z 2025-09-07T08:19:26.5161439Z Args: 2025-09-07T08:19:26.5161630Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-09-07T08:19:26.5161834Z gradients by the initial ``world_size`` DDP training was launched 2025-09-07T08:19:26.5162033Z with. If ``False``, will compute the effective world size 2025-09-07T08:19:26.5162216Z (number of ranks that have not depleted their inputs yet) and 2025-09-07T08:19:26.5162360Z divide gradients by that during allreduce. Set 2025-09-07T08:19:26.5162547Z ``divide_by_initial_world_size=True`` to ensure every input 2025-09-07T08:19:26.5162753Z sample including the uneven inputs have equal weight in terms of 2025-09-07T08:19:26.5162934Z how much they contribute to the global gradient. This is 2025-09-07T08:19:26.5163104Z achieved by always dividing the gradient by the initial 2025-09-07T08:19:26.5163289Z ``world_size`` even when we encounter uneven inputs. If you set 2025-09-07T08:19:26.5163466Z this to ``False``, we divide the gradient by the remaining 2025-09-07T08:19:26.5163684Z number of nodes. This ensures parity with training on a smaller 2025-09-07T08:19:26.5163879Z ``world_size`` although it also means the uneven inputs would 2025-09-07T08:19:26.5164071Z contribute more towards the global gradient. Typically, you 2025-09-07T08:19:26.5164368Z would want to set this to ``True`` for cases where the last few 2025-09-07T08:19:26.5164574Z inputs of your training job are uneven. In extreme cases, where 2025-09-07T08:19:26.5164757Z there is a large discrepancy in the number of inputs, setting 2025-09-07T08:19:26.5164908Z this to ``False`` might provide better results. 2025-09-07T08:19:26.5165116Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-09-07T08:19:26.5165288Z in ``enable=False`` to disable in cases where you know that 2025-09-07T08:19:26.5165486Z inputs are even across participating processes. Default is 2025-09-07T08:19:26.5165572Z ``True``. 2025-09-07T08:19:26.5165767Z throw_on_early_termination (bool): Whether to throw an error 2025-09-07T08:19:26.5165937Z or continue training when at least one rank has exhausted 2025-09-07T08:19:26.5166118Z inputs. If ``True``, will throw upon the first rank reaching end 2025-09-07T08:19:26.5166293Z of data. If ``False``, will continue training with a smaller 2025-09-07T08:19:26.5166486Z effective world size until all ranks are joined. Note that if 2025-09-07T08:19:26.5166614Z this flag is specified, then the flag 2025-09-07T08:19:26.5166785Z ``divide_by_initial_world_size`` would be ignored. Default 2025-09-07T08:19:26.5166886Z is ``False``. 2025-09-07T08:19:26.5166960Z 2025-09-07T08:19:26.5167069Z 2025-09-07T08:19:26.5167191Z Example:: 2025-09-07T08:19:26.5167268Z 2025-09-07T08:19:26.5167383Z >>> # xdoctest: +SKIP("Distributed") 2025-09-07T08:19:26.5167483Z >>> import torch 2025-09-07T08:19:26.5167602Z >>> import torch.distributed as dist 2025-09-07T08:19:26.5167700Z >>> import os 2025-09-07T08:19:26.5167820Z >>> import torch.multiprocessing as mp 2025-09-07T08:19:26.5167921Z >>> import torch.nn as nn 2025-09-07T08:19:26.5168028Z >>> # On each spawned worker 2025-09-07T08:19:26.5168123Z >>> def worker(rank): 2025-09-07T08:19:26.5168309Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-09-07T08:19:26.5168420Z >>> torch.cuda.set_device(rank) 2025-09-07T08:19:26.5168554Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T08:19:26.5168738Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-09-07T08:19:26.5168880Z >>> model, device_ids=[rank], output_device=rank 2025-09-07T08:19:26.5168976Z >>> ) 2025-09-07T08:19:26.5169105Z >>> # Rank 1 gets one more input than rank 0. 2025-09-07T08:19:26.5169292Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-09-07T08:19:26.5169428Z >>> with model.join(): 2025-09-07T08:19:26.5169526Z >>> for _ in range(5): 2025-09-07T08:19:26.5169643Z >>> for inp in inputs: 2025-09-07T08:19:26.5169754Z >>> loss = model(inp).sum() 2025-09-07T08:19:26.5169856Z >>> loss.backward() 2025-09-07T08:19:26.5170055Z >>> # Without the join() API, the below synchronization will hang 2025-09-07T08:19:26.5170196Z >>> # blocking for rank 1's allreduce to complete. 2025-09-07T08:19:26.5170335Z >>> torch.cuda.synchronize(device=rank) 2025-09-07T08:19:26.5170413Z 2025-09-07T08:19:26.5170668Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5170761Z 2025-09-07T08:19:26.5170855Z warnings.warn(msg) 2025-09-07T08:19:26.5170933Z 2025-09-07T08:19:26.5171177Z --- Parse Warning: 112 / 146 --- 2025-09-07T08:19:26.5172270Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2057. 2025-09-07T08:19:26.5172541Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5172623Z 2025-09-07T08:19:26.5172944Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-09-07T08:19:26.5173021Z 2025-09-07T08:19:26.5173224Z Registers an optimizer with DDP such that the optimization for a 2025-09-07T08:19:26.5173607Z parameter will run immediately when that parameter's gradient is 2025-09-07T08:19:26.5173811Z finished with reduction, instead of waiting for all parameters' 2025-09-07T08:19:26.5174041Z gradients to finish reduction. This can result in a training speedup 2025-09-07T08:19:26.5174260Z depending on your workload since the optimizer can run while gradient 2025-09-07T08:19:26.5174487Z reduction for other parameters are still ongoing. In addition, this has 2025-09-07T08:19:26.5174718Z the potential to reduce peak memory consumption during training, as it 2025-09-07T08:19:26.5174914Z only needs to load the per-parameter optimizer states of a single 2025-09-07T08:19:26.5175134Z parameter at a time, instead of loading all per-parameter optimizer 2025-09-07T08:19:26.5175219Z states at once. 2025-09-07T08:19:26.5175294Z 2025-09-07T08:19:26.5175386Z Args: 2025-09-07T08:19:26.5175581Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-09-07T08:19:26.5175762Z as a fused optimizer. 2025-09-07T08:19:26.5175928Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-09-07T08:19:26.5176179Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-09-07T08:19:26.5176413Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-09-07T08:19:26.5176612Z Optimizers. If this is omitted, all DDP model parameters will be 2025-09-07T08:19:26.5176708Z optimized. 2025-09-07T08:19:26.5176901Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-09-07T08:19:26.5176978Z 2025-09-07T08:19:26.5177077Z .. warning :: 2025-09-07T08:19:26.5177284Z _register_fused_optim should only be called once on a DDP instance, 2025-09-07T08:19:26.5177506Z and registering multiple fused optimizers for the same DDP model 2025-09-07T08:19:26.5177631Z is not currently supported. Please ping 2025-09-07T08:19:26.5177865Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T08:19:26.5177970Z for your use case. 2025-09-07T08:19:26.5178052Z 2025-09-07T08:19:26.5178137Z .. warning :: 2025-09-07T08:19:26.5178339Z _register_fused_optim and register_comm_hook currently do not 2025-09-07T08:19:26.5178586Z compose together, meaning that custom DDP communication hooks are 2025-09-07T08:19:26.5178763Z not supported with overlapped optimizers. Please ping 2025-09-07T08:19:26.5178985Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T08:19:26.5179091Z for your use case. 2025-09-07T08:19:26.5179167Z 2025-09-07T08:19:26.5179251Z .. warning :: 2025-09-07T08:19:26.5179487Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-09-07T08:19:26.5179611Z with overlapped optimizer. Please ping 2025-09-07T08:19:26.5179835Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T08:19:26.5179940Z for your use case. 2025-09-07T08:19:26.5180018Z 2025-09-07T08:19:26.5180118Z Example:: 2025-09-07T08:19:26.5180195Z 2025-09-07T08:19:26.5180327Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-09-07T08:19:26.5180668Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-09-07T08:19:26.5180867Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-09-07T08:19:26.5180961Z >>> lr = 1e-2 2025-09-07T08:19:26.5181051Z >>> betas = (0.9, 0.99) 2025-09-07T08:19:26.5181139Z >>> eps = 1e-6 2025-09-07T08:19:26.5181370Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-09-07T08:19:26.5181490Z >>> # Example with subset of parameters 2025-09-07T08:19:26.5181633Z >>> params_to_opt = [list(net.parameters())[0]] 2025-09-07T08:19:26.5181742Z >>> net._register_fused_optim( 2025-09-07T08:19:26.5181971Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-09-07T08:19:26.5182063Z ... ) 2025-09-07T08:19:26.5182144Z 2025-09-07T08:19:26.5182406Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5182486Z 2025-09-07T08:19:26.5182582Z warnings.warn(msg) 2025-09-07T08:19:26.5182668Z 2025-09-07T08:19:26.5182869Z --- Parse Warning: 113 / 146 --- 2025-09-07T08:19:26.5183884Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=14. 2025-09-07T08:19:26.5184149Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5184370Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-09-07T08:19:26.5184460Z 2025-09-07T08:19:26.5184763Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T08:19:26.5185073Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T08:19:26.5185328Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T08:19:26.5185634Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T08:19:26.5185722Z 2025-09-07T08:19:26.5185808Z .. note:: 2025-09-07T08:19:26.5186050Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-09-07T08:19:26.5186267Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-09-07T08:19:26.5186472Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T08:19:26.5186706Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T08:19:26.5186923Z One place we are confident in is that NHWC(channels_last) conversion for 2025-09-07T08:19:26.5187148Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-09-07T08:19:26.5187350Z even in cases where we have to apply permutation to input tensors. 2025-09-07T08:19:26.5187465Z 2025-09-07T08:19:26.5187685Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T08:19:26.5187799Z channels_last. This ensures that; 2025-09-07T08:19:26.5188023Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T08:19:26.5188252Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T08:19:26.5188495Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T08:19:26.5188608Z from memory_format conversion. 2025-09-07T08:19:26.5188688Z 2025-09-07T08:19:26.5188925Z The optimal case is that, layers between convolution layers are channels 2025-09-07T08:19:26.5189156Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T08:19:26.5189395Z encounters the first convolution layer and stay in that memory format. 2025-09-07T08:19:26.5189654Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T08:19:26.5189732Z 2025-09-07T08:19:26.5189962Z In case where a channels last incompatible layer is between convolution 2025-09-07T08:19:26.5190168Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T08:19:26.5190396Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T08:19:26.5190619Z contiguous format and be permuted to channels last when it encounters 2025-09-07T08:19:26.5190825Z another convolution layer. There's no point in propagating that 2025-09-07T08:19:26.5191056Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T08:19:26.5191150Z ``memory_format``. 2025-09-07T08:19:26.5191238Z 2025-09-07T08:19:26.5191468Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T08:19:26.5191685Z there might have been a better spot to fuse the permutation other than 2025-09-07T08:19:26.5191814Z immediately before a convolution. 2025-09-07T08:19:26.5191890Z 2025-09-07T08:19:26.5191980Z Args: 2025-09-07T08:19:26.5192189Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-09-07T08:19:26.5192285Z ``nn.Module`` 2025-09-07T08:19:26.5192444Z memory_format: user specified ``memory_format``, 2025-09-07T08:19:26.5192617Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T08:19:26.5192701Z 2025-09-07T08:19:26.5192782Z Returns: 2025-09-07T08:19:26.5192947Z The original module with updated ``nn.Conv2d`` 2025-09-07T08:19:26.5193038Z 2025-09-07T08:19:26.5193145Z Example: 2025-09-07T08:19:26.5193293Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:26.5193445Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T08:19:26.5193552Z >>> input = torch.randint( 2025-09-07T08:19:26.5193709Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T08:19:26.5193787Z ... ) 2025-09-07T08:19:26.5193889Z >>> model = nn.Sequential( 2025-09-07T08:19:26.5194018Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-09-07T08:19:26.5194113Z >>> # This is identical to: 2025-09-07T08:19:26.5194368Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-09-07T08:19:26.5194530Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-09-07T08:19:26.5194652Z ... model, torch.channels_last 2025-09-07T08:19:26.5194732Z ... ) 2025-09-07T08:19:26.5194830Z >>> out = model(input) 2025-09-07T08:19:26.5194921Z 2025-09-07T08:19:26.5195173Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5195279Z 2025-09-07T08:19:26.5195383Z warnings.warn(msg) 2025-09-07T08:19:26.5195459Z 2025-09-07T08:19:26.5195666Z --- Parse Warning: 114 / 146 --- 2025-09-07T08:19:26.5196662Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=93. 2025-09-07T08:19:26.5196921Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5197142Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-09-07T08:19:26.5197417Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T08:19:26.5197699Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T08:19:26.5197982Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T08:19:26.5198298Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T08:19:26.5198380Z 2025-09-07T08:19:26.5198464Z .. note:: 2025-09-07T08:19:26.5198719Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-09-07T08:19:26.5198941Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-09-07T08:19:26.5199175Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T08:19:26.5199398Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T08:19:26.5199632Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-09-07T08:19:26.5199863Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-09-07T08:19:26.5200066Z even in cases where we have to apply permutation to input tensors. 2025-09-07T08:19:26.5200162Z 2025-09-07T08:19:26.5200387Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T08:19:26.5200505Z channels_last_3d. This ensures that; 2025-09-07T08:19:26.5200732Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T08:19:26.5200964Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T08:19:26.5201214Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T08:19:26.5201328Z from memory_format conversion. 2025-09-07T08:19:26.5201407Z 2025-09-07T08:19:26.5201674Z The optimal case is that, layers between convolution layers are channels 2025-09-07T08:19:26.5201909Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T08:19:26.5202177Z encounters the first convolution layer and stay in that memory format. 2025-09-07T08:19:26.5202419Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T08:19:26.5202499Z 2025-09-07T08:19:26.5202734Z In case where a channels last incompatible layer is between convolution 2025-09-07T08:19:26.5202946Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T08:19:26.5203180Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T08:19:26.5203403Z contiguous format and be permuted to channels last when it encounters 2025-09-07T08:19:26.5203627Z another convolution layer. There's no point in propagating that 2025-09-07T08:19:26.5203844Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T08:19:26.5203943Z ``memory_format``. 2025-09-07T08:19:26.5204036Z 2025-09-07T08:19:26.5204353Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T08:19:26.5204620Z there might have been a better spot to fuse the permutation other than 2025-09-07T08:19:26.5204737Z immediately before a convolution. 2025-09-07T08:19:26.5204816Z 2025-09-07T08:19:26.5204912Z Args: 2025-09-07T08:19:26.5205124Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-09-07T08:19:26.5205242Z ``nn.Module`` 2025-09-07T08:19:26.5205393Z memory_format: user specified ``memory_format``, 2025-09-07T08:19:26.5205569Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T08:19:26.5205662Z 2025-09-07T08:19:26.5205747Z Returns: 2025-09-07T08:19:26.5205887Z The original module with updated ``nn.Conv3d`` 2025-09-07T08:19:26.5205978Z 2025-09-07T08:19:26.5206059Z Example: 2025-09-07T08:19:26.5206208Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T08:19:26.5206398Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T08:19:26.5206505Z >>> input = torch.randint( 2025-09-07T08:19:26.5206664Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T08:19:26.5206746Z ... ) 2025-09-07T08:19:26.5206859Z >>> model = nn.Sequential( 2025-09-07T08:19:26.5206974Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-09-07T08:19:26.5207073Z >>> # This is identical to: 2025-09-07T08:19:26.5207339Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-09-07T08:19:26.5207499Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-09-07T08:19:26.5207629Z ... model, torch.channels_last_3d 2025-09-07T08:19:26.5207708Z ... ) 2025-09-07T08:19:26.5207806Z >>> out = model(input) 2025-09-07T08:19:26.5207895Z 2025-09-07T08:19:26.5208147Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5208239Z 2025-09-07T08:19:26.5208333Z warnings.warn(msg) 2025-09-07T08:19:26.5208410Z 2025-09-07T08:19:26.5208622Z --- Parse Warning: 115 / 146 --- 2025-09-07T08:19:26.5209583Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py line=424. 2025-09-07T08:19:26.5209859Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5210014Z Register a parametrization to a tensor in a module. 2025-09-07T08:19:26.5210091Z 2025-09-07T08:19:26.5210406Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-09-07T08:19:26.5210706Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-09-07T08:19:26.5210979Z If the original tensor requires a gradient, the backward pass will differentiate 2025-09-07T08:19:26.5211269Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-09-07T08:19:26.5211347Z 2025-09-07T08:19:26.5211671Z The first time that a module registers a parametrization, this function will add an attribute 2025-09-07T08:19:26.5211913Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-09-07T08:19:26.5212004Z 2025-09-07T08:19:26.5212253Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-09-07T08:19:26.5212380Z ``module.parametrizations.weight``. 2025-09-07T08:19:26.5212470Z 2025-09-07T08:19:26.5212607Z The original tensor will be accessible under 2025-09-07T08:19:26.5212773Z ``module.parametrizations.weight.original``. 2025-09-07T08:19:26.5212853Z 2025-09-07T08:19:26.5213118Z Parametrizations may be concatenated by registering several parametrizations 2025-09-07T08:19:26.5213257Z on the same attribute. 2025-09-07T08:19:26.5213333Z 2025-09-07T08:19:26.5213586Z The training mode of a registered parametrization is updated on registration 2025-09-07T08:19:26.5213721Z to match the training mode of the host module 2025-09-07T08:19:26.5213798Z 2025-09-07T08:19:26.5214112Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-09-07T08:19:26.5214237Z using the context manager :func:`cached`. 2025-09-07T08:19:26.5214324Z 2025-09-07T08:19:26.5214565Z A :attr:`parametrization` may optionally implement a method with signature 2025-09-07T08:19:26.5214640Z 2025-09-07T08:19:26.5214760Z .. code-block:: python 2025-09-07T08:19:26.5214837Z 2025-09-07T08:19:26.5215062Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-09-07T08:19:26.5215139Z 2025-09-07T08:19:26.5215454Z This method is called on the unparametrized tensor when the first parametrization 2025-09-07T08:19:26.5215678Z is registered to compute the initial value of the original tensor. 2025-09-07T08:19:26.5215986Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-09-07T08:19:26.5216078Z 2025-09-07T08:19:26.5216386Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-09-07T08:19:26.5216673Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-09-07T08:19:26.5216765Z 2025-09-07T08:19:26.5216989Z It is possible for the first parametrization to depend on several inputs. 2025-09-07T08:19:26.5217246Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-09-07T08:19:26.5217482Z (see the example implementation of a ``RankOne`` parametrization below). 2025-09-07T08:19:26.5217561Z 2025-09-07T08:19:26.5217912Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-09-07T08:19:26.5218043Z with names ``original0``, ``original1``,... 2025-09-07T08:19:26.5218135Z 2025-09-07T08:19:26.5218221Z .. note:: 2025-09-07T08:19:26.5218298Z 2025-09-07T08:19:26.5218576Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-09-07T08:19:26.5218723Z once to perform a number of consistency checks. 2025-09-07T08:19:26.5218999Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-09-07T08:19:26.5219121Z and nothing will be called otherwise. 2025-09-07T08:19:26.5219198Z 2025-09-07T08:19:26.5219326Z .. note:: 2025-09-07T08:19:26.5219401Z 2025-09-07T08:19:26.5219639Z In most situations, ``right_inverse`` will be a function such that 2025-09-07T08:19:26.5219759Z ``forward(right_inverse(X)) == X`` (see 2025-09-07T08:19:26.5220041Z `right inverse `_). 2025-09-07T08:19:26.5220301Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-09-07T08:19:26.5220394Z to relax this. 2025-09-07T08:19:26.5220482Z 2025-09-07T08:19:26.5220569Z .. warning:: 2025-09-07T08:19:26.5220644Z 2025-09-07T08:19:26.5220930Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-09-07T08:19:26.5221188Z will register a number of new parameters. If such parametrization is registered 2025-09-07T08:19:26.5221468Z after the optimizer is created, these new parameters will need to be added manually 2025-09-07T08:19:26.5221663Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-09-07T08:19:26.5221745Z 2025-09-07T08:19:26.5221838Z Args: 2025-09-07T08:19:26.5222051Z module (nn.Module): module on which to register the parametrization 2025-09-07T08:19:26.5222301Z tensor_name (str): name of the parameter or buffer on which to register 2025-09-07T08:19:26.5222403Z the parametrization 2025-09-07T08:19:26.5222602Z parametrization (nn.Module): the parametrization to register 2025-09-07T08:19:26.5222700Z Keyword args: 2025-09-07T08:19:26.5222914Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-09-07T08:19:26.5223116Z may change the dtype and shape of the tensor. Default: `False` 2025-09-07T08:19:26.5223381Z Warning: the parametrization is not checked for consistency upon registration. 2025-09-07T08:19:26.5223501Z Enable this flag at your own risk. 2025-09-07T08:19:26.5223590Z 2025-09-07T08:19:26.5223673Z Raises: 2025-09-07T08:19:26.5223991Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-09-07T08:19:26.5224076Z 2025-09-07T08:19:26.5224163Z Examples: 2025-09-07T08:19:26.5224319Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T08:19:26.5224414Z >>> import torch 2025-09-07T08:19:26.5224515Z >>> import torch.nn as nn 2025-09-07T08:19:26.5224664Z >>> import torch.nn.utils.parametrize as P 2025-09-07T08:19:26.5224743Z >>> 2025-09-07T08:19:26.5224864Z >>> class Symmetric(nn.Module): 2025-09-07T08:19:26.5224965Z >>> def forward(self, X): 2025-09-07T08:19:26.5225140Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-09-07T08:19:26.5225232Z >>> 2025-09-07T08:19:26.5225345Z >>> def right_inverse(self, A): 2025-09-07T08:19:26.5225457Z >>> return A.triu() 2025-09-07T08:19:26.5225538Z >>> 2025-09-07T08:19:26.5225633Z >>> m = nn.Linear(5, 5) 2025-09-07T08:19:26.5225814Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-09-07T08:19:26.5226057Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-09-07T08:19:26.5226150Z True 2025-09-07T08:19:26.5226250Z >>> A = torch.rand(5, 5) 2025-09-07T08:19:26.5226359Z >>> A = A + A.T # A is now symmetric 2025-09-07T08:19:26.5226566Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-09-07T08:19:26.5226688Z >>> print(torch.allclose(m.weight, A)) 2025-09-07T08:19:26.5226780Z True 2025-09-07T08:19:26.5226857Z 2025-09-07T08:19:26.5226962Z >>> class RankOne(nn.Module): 2025-09-07T08:19:26.5227081Z >>> def forward(self, x, y): 2025-09-07T08:19:26.5227246Z >>> # Form a rank 1 matrix multiplying two vectors 2025-09-07T08:19:26.5227417Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-09-07T08:19:26.5227496Z >>> 2025-09-07T08:19:26.5227608Z >>> def right_inverse(self, Z): 2025-09-07T08:19:26.5227741Z >>> # Project Z onto the rank 1 matrices 2025-09-07T08:19:26.5227889Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-09-07T08:19:26.5228019Z >>> # Return rescaled singular vectors 2025-09-07T08:19:26.5228141Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-09-07T08:19:26.5228286Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-09-07T08:19:26.5228381Z >>> 2025-09-07T08:19:26.5228522Z >>> linear_rank_one = P.register_parametrization( 2025-09-07T08:19:26.5228657Z ... nn.Linear(4, 4), "weight", RankOne() 2025-09-07T08:19:26.5228737Z ... ) 2025-09-07T08:19:26.5228939Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-09-07T08:19:26.5229032Z 1 2025-09-07T08:19:26.5229108Z 2025-09-07T08:19:26.5229190Z 2025-09-07T08:19:26.5229476Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5229552Z 2025-09-07T08:19:26.5229662Z warnings.warn(msg) 2025-09-07T08:19:26.5229739Z 2025-09-07T08:19:26.5229942Z --- Parse Warning: 116 / 146 --- 2025-09-07T08:19:26.5230834Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=979. 2025-09-07T08:19:26.5231095Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5231418Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-09-07T08:19:26.5231498Z 2025-09-07T08:19:26.5231734Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-09-07T08:19:26.5231995Z by removing the specified ``amount`` of (currently unpruned) channels 2025-09-07T08:19:26.5232175Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-09-07T08:19:26.5232386Z Modifies module in place (and also return the modified module) 2025-09-07T08:19:26.5232472Z by: 2025-09-07T08:19:26.5232552Z 2025-09-07T08:19:26.5232774Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T08:19:26.5232992Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T08:19:26.5233213Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T08:19:26.5233418Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T08:19:26.5233527Z ``name+'_orig'``. 2025-09-07T08:19:26.5233612Z 2025-09-07T08:19:26.5233696Z Args: 2025-09-07T08:19:26.5233888Z module (nn.Module): module containing the tensor to prune 2025-09-07T08:19:26.5234071Z name (str): parameter name within ``module`` on which pruning 2025-09-07T08:19:26.5234167Z will act. 2025-09-07T08:19:26.5234351Z amount (int or float): quantity of parameters to prune. 2025-09-07T08:19:26.5234527Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T08:19:26.5234740Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T08:19:26.5234874Z absolute number of parameters to prune. 2025-09-07T08:19:26.5235078Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-09-07T08:19:26.5235233Z entries for argument ``p`` in :func:`torch.norm`. 2025-09-07T08:19:26.5235455Z dim (int): index of the dim along which we define channels to prune. 2025-09-07T08:19:26.5235703Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-09-07T08:19:26.5235917Z shape as module parameter) used to compute mask for pruning. 2025-09-07T08:19:26.5236152Z The values in this tensor indicate the importance of the corresponding 2025-09-07T08:19:26.5236282Z elements in the parameter being pruned. 2025-09-07T08:19:26.5236506Z If unspecified or None, the module parameter will be used in its place. 2025-09-07T08:19:26.5236596Z 2025-09-07T08:19:26.5236679Z Returns: 2025-09-07T08:19:26.5236905Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T08:19:26.5236983Z 2025-09-07T08:19:26.5237065Z Examples: 2025-09-07T08:19:26.5237196Z >>> from torch.nn.utils import prune 2025-09-07T08:19:26.5237301Z >>> m = prune.ln_structured( 2025-09-07T08:19:26.5237493Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-09-07T08:19:26.5237575Z ... ) 2025-09-07T08:19:26.5237650Z 2025-09-07T08:19:26.5237914Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5238030Z 2025-09-07T08:19:26.5238123Z warnings.warn(msg) 2025-09-07T08:19:26.5238213Z 2025-09-07T08:19:26.5238402Z --- Parse Warning: 117 / 146 --- 2025-09-07T08:19:26.5239325Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1026. 2025-09-07T08:19:26.5239585Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5239660Z 2025-09-07T08:19:26.5240103Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-09-07T08:19:26.5240181Z 2025-09-07T08:19:26.5240301Z Modifies modules in place by: 2025-09-07T08:19:26.5240379Z 2025-09-07T08:19:26.5240612Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T08:19:26.5240846Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T08:19:26.5241056Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T08:19:26.5241271Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T08:19:26.5241361Z ``name+'_orig'``. 2025-09-07T08:19:26.5241437Z 2025-09-07T08:19:26.5241531Z Args: 2025-09-07T08:19:26.5241730Z parameters (Iterable of (module, name) tuples): parameters of 2025-09-07T08:19:26.5241931Z the model to prune in a global fashion, i.e. by aggregating all 2025-09-07T08:19:26.5242139Z weights prior to deciding which ones to prune. module must be of 2025-09-07T08:19:26.5242293Z type :class:`nn.Module`, and name must be a string. 2025-09-07T08:19:26.5242529Z pruning_method (function): a valid pruning function from this module, 2025-09-07T08:19:26.5242704Z or a custom one implemented by the user that satisfies the 2025-09-07T08:19:26.5242944Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-09-07T08:19:26.5243172Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-09-07T08:19:26.5243402Z the corresponding parameter's importance scores tensor. The tensor 2025-09-07T08:19:26.5243607Z should be the same shape as the parameter, and is used for computing 2025-09-07T08:19:26.5243704Z mask for pruning. 2025-09-07T08:19:26.5243919Z If unspecified or None, the parameter will be used in place of its 2025-09-07T08:19:26.5244018Z importance scores. 2025-09-07T08:19:26.5244242Z kwargs: other keyword arguments such as: 2025-09-07T08:19:26.5244471Z amount (int or float): quantity of parameters to prune across the 2025-09-07T08:19:26.5244602Z specified parameters. 2025-09-07T08:19:26.5244789Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T08:19:26.5244988Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T08:19:26.5245126Z absolute number of parameters to prune. 2025-09-07T08:19:26.5245204Z 2025-09-07T08:19:26.5245284Z Raises: 2025-09-07T08:19:26.5245442Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-09-07T08:19:26.5245519Z 2025-09-07T08:19:26.5245598Z Note: 2025-09-07T08:19:26.5245828Z Since global structured pruning doesn't make much sense unless the 2025-09-07T08:19:26.5246025Z norm is normalized by the size of the parameter, we now limit the 2025-09-07T08:19:26.5246184Z scope of global pruning to unstructured methods. 2025-09-07T08:19:26.5246266Z 2025-09-07T08:19:26.5246352Z Examples: 2025-09-07T08:19:26.5246489Z >>> from torch.nn.utils import prune 2025-09-07T08:19:26.5246611Z >>> from collections import OrderedDict 2025-09-07T08:19:26.5246722Z >>> net = nn.Sequential( 2025-09-07T08:19:26.5246816Z ... OrderedDict( 2025-09-07T08:19:26.5246937Z ... [ 2025-09-07T08:19:26.5247075Z ... ("first", nn.Linear(10, 4)), 2025-09-07T08:19:26.5247191Z ... ("second", nn.Linear(4, 1)), 2025-09-07T08:19:26.5247284Z ... ] 2025-09-07T08:19:26.5247364Z ... ) 2025-09-07T08:19:26.5247445Z ... ) 2025-09-07T08:19:26.5247558Z >>> parameters_to_prune = ( 2025-09-07T08:19:26.5247661Z ... (net.first, "weight"), 2025-09-07T08:19:26.5247761Z ... (net.second, "weight"), 2025-09-07T08:19:26.5247851Z ... ) 2025-09-07T08:19:26.5247957Z >>> prune.global_unstructured( 2025-09-07T08:19:26.5248071Z ... parameters_to_prune, 2025-09-07T08:19:26.5248203Z ... pruning_method=prune.L1Unstructured, 2025-09-07T08:19:26.5248295Z ... amount=10, 2025-09-07T08:19:26.5248392Z ... ) 2025-09-07T08:19:26.5248636Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-09-07T08:19:26.5248735Z tensor(10) 2025-09-07T08:19:26.5248815Z 2025-09-07T08:19:26.5248892Z 2025-09-07T08:19:26.5249154Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5249232Z 2025-09-07T08:19:26.5249339Z warnings.warn(msg) 2025-09-07T08:19:26.5249416Z 2025-09-07T08:19:26.5249616Z --- Parse Warning: 118 / 146 --- 2025-09-07T08:19:26.5250506Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1149. 2025-09-07T08:19:26.5250768Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5251177Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-09-07T08:19:26.5251258Z 2025-09-07T08:19:26.5251471Z Modifies module in place (and also return the modified module) by: 2025-09-07T08:19:26.5251564Z 2025-09-07T08:19:26.5251768Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T08:19:26.5251996Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T08:19:26.5252202Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T08:19:26.5252409Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T08:19:26.5252515Z ``name+'_orig'``. 2025-09-07T08:19:26.5252590Z 2025-09-07T08:19:26.5252682Z Args: 2025-09-07T08:19:26.5252881Z module (nn.Module): module containing the tensor to prune 2025-09-07T08:19:26.5253061Z name (str): parameter name within ``module`` on which pruning 2025-09-07T08:19:26.5253186Z will act. 2025-09-07T08:19:26.5253359Z mask (Tensor): binary mask to be applied to the parameter. 2025-09-07T08:19:26.5253453Z 2025-09-07T08:19:26.5253535Z Returns: 2025-09-07T08:19:26.5253751Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T08:19:26.5253840Z 2025-09-07T08:19:26.5253924Z Examples: 2025-09-07T08:19:26.5254055Z >>> from torch.nn.utils import prune 2025-09-07T08:19:26.5254164Z >>> m = prune.custom_from_mask( 2025-09-07T08:19:26.5254335Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-09-07T08:19:26.5254427Z ... ) 2025-09-07T08:19:26.5254523Z >>> print(m.bias_mask) 2025-09-07T08:19:26.5254616Z tensor([0., 1., 0.]) 2025-09-07T08:19:26.5254704Z 2025-09-07T08:19:26.5254786Z 2025-09-07T08:19:26.5255051Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5255132Z 2025-09-07T08:19:26.5255225Z warnings.warn(msg) 2025-09-07T08:19:26.5255320Z 2025-09-07T08:19:26.5255537Z --- Parse Warning: 119 / 146 --- 2025-09-07T08:19:26.5256441Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py line=350. 2025-09-07T08:19:26.5256705Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5256852Z Pad a packed batch of variable length sequences. 2025-09-07T08:19:26.5256942Z 2025-09-07T08:19:26.5257118Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-09-07T08:19:26.5257208Z 2025-09-07T08:19:26.5257488Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-09-07T08:19:26.5257725Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-09-07T08:19:26.5257881Z sequence and ``B`` is the batch size. 2025-09-07T08:19:26.5257964Z 2025-09-07T08:19:26.5258058Z Example: 2025-09-07T08:19:26.5258296Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-09-07T08:19:26.5258440Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-09-07T08:19:26.5258546Z >>> lens = [2, 1, 3] 2025-09-07T08:19:26.5258661Z >>> packed = pack_padded_sequence( 2025-09-07T08:19:26.5258821Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-09-07T08:19:26.5258901Z ... ) 2025-09-07T08:19:26.5258986Z >>> packed 2025-09-07T08:19:26.5259238Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-09-07T08:19:26.5259455Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-09-07T08:19:26.5259715Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-09-07T08:19:26.5259808Z >>> seq_unpacked 2025-09-07T08:19:26.5259901Z tensor([[1, 2, 0], 2025-09-07T08:19:26.5260005Z [3, 0, 0], 2025-09-07T08:19:26.5260094Z [4, 5, 6]]) 2025-09-07T08:19:26.5260197Z >>> lens_unpacked 2025-09-07T08:19:26.5260288Z tensor([2, 1, 3]) 2025-09-07T08:19:26.5260366Z 2025-09-07T08:19:26.5260468Z .. note:: 2025-09-07T08:19:26.5260613Z :attr:`total_length` is useful to implement the 2025-09-07T08:19:26.5260843Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-09-07T08:19:26.5261058Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-09-07T08:19:26.5261316Z See :ref:`this FAQ section ` for 2025-09-07T08:19:26.5261442Z details. 2025-09-07T08:19:26.5261522Z 2025-09-07T08:19:26.5261620Z Args: 2025-09-07T08:19:26.5261754Z sequence (PackedSequence): batch to pad 2025-09-07T08:19:26.5261990Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-09-07T08:19:26.5262115Z format, ``T x B x *`` otherwise. 2025-09-07T08:19:26.5262304Z padding_value (float, optional): values for padded elements. 2025-09-07T08:19:26.5262541Z total_length (int, optional): if not ``None``, the output will be padded to 2025-09-07T08:19:26.5262776Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-09-07T08:19:26.5262963Z if :attr:`total_length` is less than the max sequence length in 2025-09-07T08:19:26.5263079Z :attr:`sequence`. 2025-09-07T08:19:26.5263157Z 2025-09-07T08:19:26.5263253Z Returns: 2025-09-07T08:19:26.5263441Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-09-07T08:19:26.5263636Z containing the list of lengths of each sequence in the batch. 2025-09-07T08:19:26.5263899Z Batch elements will be re-ordered as they were ordered originally when 2025-09-07T08:19:26.5264117Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-09-07T08:19:26.5264211Z 2025-09-07T08:19:26.5264461Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5264538Z 2025-09-07T08:19:26.5264648Z warnings.warn(msg) 2025-09-07T08:19:26.5264726Z 2025-09-07T08:19:26.5264912Z --- Parse Warning: 120 / 146 --- 2025-09-07T08:19:26.5265827Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=811. 2025-09-07T08:19:26.5266090Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5266455Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-09-07T08:19:26.5266536Z 2025-09-07T08:19:26.5266918Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-09-07T08:19:26.5267133Z intervals by which each scheduler should be called at a given epoch. 2025-09-07T08:19:26.5267211Z 2025-09-07T08:19:26.5267304Z Args: 2025-09-07T08:19:26.5267438Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:26.5267591Z schedulers (list): List of chained schedulers. 2025-09-07T08:19:26.5267797Z milestones (list): List of integers that reflects milestone points. 2025-09-07T08:19:26.5267960Z last_epoch (int): The index of last epoch. Default: -1. 2025-09-07T08:19:26.5268050Z 2025-09-07T08:19:26.5268131Z Example: 2025-09-07T08:19:26.5268239Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5268388Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-09-07T08:19:26.5268497Z >>> # lr = 0.005 if epoch == 0 2025-09-07T08:19:26.5268608Z >>> # lr = 0.005 if epoch == 1 2025-09-07T08:19:26.5268708Z >>> # lr = 0.005 if epoch == 2 2025-09-07T08:19:26.5268798Z >>> # ... 2025-09-07T08:19:26.5268899Z >>> # lr = 0.05 if epoch == 20 2025-09-07T08:19:26.5269001Z >>> # lr = 0.045 if epoch == 21 2025-09-07T08:19:26.5269113Z >>> # lr = 0.0405 if epoch == 22 2025-09-07T08:19:26.5269311Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-09-07T08:19:26.5269485Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-09-07T08:19:26.5269619Z >>> scheduler = SequentialLR( 2025-09-07T08:19:26.5269713Z ... optimizer, 2025-09-07T08:19:26.5269888Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T08:19:26.5269985Z ... milestones=[20], 2025-09-07T08:19:26.5270070Z ... ) 2025-09-07T08:19:26.5270181Z >>> for epoch in range(100): 2025-09-07T08:19:26.5270270Z >>> train(...) 2025-09-07T08:19:26.5270375Z >>> validate(...) 2025-09-07T08:19:26.5270474Z >>> scheduler.step() 2025-09-07T08:19:26.5270551Z 2025-09-07T08:19:26.5270734Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-09-07T08:19:26.5270812Z 2025-09-07T08:19:26.5271077Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5271152Z 2025-09-07T08:19:26.5271248Z warnings.warn(msg) 2025-09-07T08:19:26.5271335Z 2025-09-07T08:19:26.5271524Z --- Parse Warning: 121 / 146 --- 2025-09-07T08:19:26.5272464Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1236. 2025-09-07T08:19:26.5272739Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T08:19:26.5272919Z Reduce learning rate when a metric has stopped improving. 2025-09-07T08:19:26.5273006Z 2025-09-07T08:19:26.5273207Z Models often benefit from reducing the learning rate by a factor 2025-09-07T08:19:26.5273870Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-09-07T08:19:26.5274061Z quantity and if no improvement is seen for a 'patience' number 2025-09-07T08:19:26.5274183Z of epochs, the learning rate is reduced. 2025-09-07T08:19:26.5274275Z 2025-09-07T08:19:26.5274355Z Args: 2025-09-07T08:19:26.5274504Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:26.5274656Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-09-07T08:19:26.5274814Z be reduced when the quantity monitored has stopped 2025-09-07T08:19:26.5275053Z decreasing; in `max` mode it will be reduced when the 2025-09-07T08:19:26.5275248Z quantity monitored has stopped increasing. Default: 'min'. 2025-09-07T08:19:26.5275431Z factor (float): Factor by which the learning rate will be 2025-09-07T08:19:26.5275572Z reduced. new_lr = lr * factor. Default: 0.1. 2025-09-07T08:19:26.5275789Z patience (int): The number of allowed epochs with no improvement after 2025-09-07T08:19:26.5275935Z which the learning rate will be reduced. 2025-09-07T08:19:26.5276165Z For example, consider the case of having no patience (`patience = 0`). 2025-09-07T08:19:26.5276541Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-09-07T08:19:26.5276752Z In the second epoch, if the performance is worse than the baseline, 2025-09-07T08:19:26.5276921Z we have what is considered an intolerable epoch. 2025-09-07T08:19:26.5277189Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-09-07T08:19:26.5277356Z the learning rate is reduced at the end of this epoch. 2025-09-07T08:19:26.5277690Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-09-07T08:19:26.5278005Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-09-07T08:19:26.5278139Z the learning rate is not adjusted. 2025-09-07T08:19:26.5278234Z Default: 10. 2025-09-07T08:19:26.5278422Z threshold (float): Threshold for measuring the new optimum, 2025-09-07T08:19:26.5278626Z to only focus on significant changes. Default: 1e-4. 2025-09-07T08:19:26.5278829Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-09-07T08:19:26.5278998Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-09-07T08:19:26.5279139Z mode or best * ( 1 - threshold ) in `min` mode. 2025-09-07T08:19:26.5279292Z In `abs` mode, dynamic_threshold = best + threshold in 2025-09-07T08:19:26.5279477Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-09-07T08:19:26.5279645Z cooldown (int): Number of epochs to wait before resuming 2025-09-07T08:19:26.5279833Z normal operation after lr has been reduced. Default: 0. 2025-09-07T08:19:26.5279992Z min_lr (float or list): A scalar or a list of scalars. A 2025-09-07T08:19:26.5280151Z lower bound on the learning rate of all param groups 2025-09-07T08:19:26.5280296Z or each group respectively. Default: 0. 2025-09-07T08:19:26.5280469Z eps (float): Minimal decay applied to lr. If the difference 2025-09-07T08:19:26.5280659Z between new and old lr is smaller than eps, the update is 2025-09-07T08:19:26.5280795Z ignored. Default: 1e-8. 2025-09-07T08:19:26.5280871Z 2025-09-07T08:19:26.5280963Z Example: 2025-09-07T08:19:26.5281058Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5281295Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T08:19:26.5281450Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-09-07T08:19:26.5281550Z >>> for epoch in range(10): 2025-09-07T08:19:26.5281652Z >>> train(...) 2025-09-07T08:19:26.5281759Z >>> val_loss = validate(...) 2025-09-07T08:19:26.5281920Z >>> # Note that step should be called after validate() 2025-09-07T08:19:26.5282033Z >>> scheduler.step(val_loss) 2025-09-07T08:19:26.5282110Z 2025-09-07T08:19:26.5282316Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-09-07T08:19:26.5282396Z 2025-09-07T08:19:26.5282843Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-09-07T08:19:26.5282923Z 2025-09-07T08:19:26.5283028Z scheduler.step(val_loss) 2025-09-07T08:19:26.5283118Z ^ 2025-09-07T08:19:26.5283212Z warnings.warn(msg) 2025-09-07T08:19:26.5283302Z 2025-09-07T08:19:26.5283500Z --- Parse Warning: 122 / 146 --- 2025-09-07T08:19:26.5284455Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1433. 2025-09-07T08:19:26.5284733Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5285057Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-09-07T08:19:26.5285148Z 2025-09-07T08:19:26.5285431Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-09-07T08:19:26.5285689Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-09-07T08:19:26.5285934Z The distance between the two boundaries can be scaled on a per-iteration 2025-09-07T08:19:26.5286029Z or per-cycle basis. 2025-09-07T08:19:26.5286121Z 2025-09-07T08:19:26.5286363Z Cyclical learning rate policy changes the learning rate after every batch. 2025-09-07T08:19:26.5286556Z `step` should be called after a batch has been used for training. 2025-09-07T08:19:26.5286647Z 2025-09-07T08:19:26.5286853Z This class has three built-in policies, as put forth in the paper: 2025-09-07T08:19:26.5286944Z 2025-09-07T08:19:26.5287179Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-09-07T08:19:26.5287500Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-09-07T08:19:26.5287831Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-09-07T08:19:26.5287935Z at each cycle iteration. 2025-09-07T08:19:26.5288026Z 2025-09-07T08:19:26.5288261Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-09-07T08:19:26.5288341Z 2025-09-07T08:19:26.5288439Z Args: 2025-09-07T08:19:26.5288573Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:26.5288760Z base_lr (float or list): Initial learning rate which is the 2025-09-07T08:19:26.5288926Z lower boundary in the cycle for each parameter group. 2025-09-07T08:19:26.5289121Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T08:19:26.5289268Z for each parameter group. Functionally, 2025-09-07T08:19:26.5289426Z it defines the cycle amplitude (max_lr - base_lr). 2025-09-07T08:19:26.5289566Z The lr at any cycle is the sum of base_lr 2025-09-07T08:19:26.5289736Z and some scaling of the amplitude; therefore 2025-09-07T08:19:26.5289879Z max_lr may not actually be reached depending on 2025-09-07T08:19:26.5289984Z scaling function. 2025-09-07T08:19:26.5293822Z step_size_up (int): Number of training iterations in the 2025-09-07T08:19:26.5293978Z increasing half of a cycle. Default: 2000 2025-09-07T08:19:26.5294169Z step_size_down (int): Number of training iterations in the 2025-09-07T08:19:26.5294356Z decreasing half of a cycle. If step_size_down is None, 2025-09-07T08:19:26.5294482Z it is set to step_size_up. Default: None 2025-09-07T08:19:26.5294664Z mode (str): One of {triangular, triangular2, exp_range}. 2025-09-07T08:19:26.5294811Z Values correspond to policies detailed above. 2025-09-07T08:19:26.5294969Z If scale_fn is not None, this argument is ignored. 2025-09-07T08:19:26.5295150Z Default: 'triangular' 2025-09-07T08:19:26.5295318Z gamma (float): Constant in 'exp_range' scaling function: 2025-09-07T08:19:26.5295436Z gamma**(cycle iterations) 2025-09-07T08:19:26.5295534Z Default: 1.0 2025-09-07T08:19:26.5295724Z scale_fn (function): Custom scaling policy defined by a single 2025-09-07T08:19:26.5295855Z argument lambda function, where 2025-09-07T08:19:26.5295973Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-09-07T08:19:26.5296108Z If specified, then 'mode' is ignored. 2025-09-07T08:19:26.5296203Z Default: None 2025-09-07T08:19:26.5296331Z scale_mode (str): {'cycle', 'iterations'}. 2025-09-07T08:19:26.5296481Z Defines whether scale_fn is evaluated on 2025-09-07T08:19:26.5296618Z cycle number or cycle iterations (training 2025-09-07T08:19:26.5296750Z iterations since start of cycle). 2025-09-07T08:19:26.5296847Z Default: 'cycle' 2025-09-07T08:19:26.5297047Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T08:19:26.5297244Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T08:19:26.5297336Z Default: True 2025-09-07T08:19:26.5297562Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T08:19:26.5297770Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T08:19:26.5297928Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T08:19:26.5298080Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T08:19:26.5298201Z Default: 0.8 2025-09-07T08:19:26.5298432Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T08:19:26.5298591Z for each parameter group. Functionally, 2025-09-07T08:19:26.5298790Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T08:19:26.5298989Z The momentum at any cycle is the difference of max_momentum 2025-09-07T08:19:26.5299127Z and some scaling of the amplitude; therefore 2025-09-07T08:19:26.5299309Z base_momentum may not actually be reached depending on 2025-09-07T08:19:26.5299488Z scaling function. Note that momentum is cycled inversely 2025-09-07T08:19:26.5299712Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T08:19:26.5299826Z and learning rate is 'base_lr' 2025-09-07T08:19:26.5299918Z Default: 0.9 2025-09-07T08:19:26.5300153Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T08:19:26.5300371Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T08:19:26.5300598Z batch instead of after each epoch, this number represents the total 2025-09-07T08:19:26.5300851Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T08:19:26.5301039Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T08:19:26.5301143Z Default: -1 2025-09-07T08:19:26.5301220Z 2025-09-07T08:19:26.5301323Z Example: 2025-09-07T08:19:26.5301423Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5301650Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T08:19:26.5301815Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-09-07T08:19:26.5301908Z ... optimizer, 2025-09-07T08:19:26.5302016Z ... base_lr=0.01, 2025-09-07T08:19:26.5302106Z ... max_lr=0.1, 2025-09-07T08:19:26.5302206Z ... step_size_up=10, 2025-09-07T08:19:26.5302302Z ... ) 2025-09-07T08:19:26.5302479Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T08:19:26.5302586Z >>> for epoch in range(10): 2025-09-07T08:19:26.5302705Z >>> for batch in data_loader: 2025-09-07T08:19:26.5302808Z >>> train_batch(...) 2025-09-07T08:19:26.5302925Z >>> scheduler.step() 2025-09-07T08:19:26.5303004Z 2025-09-07T08:19:26.5303167Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-09-07T08:19:26.5303256Z 2025-09-07T08:19:26.5303566Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-09-07T08:19:26.5303741Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-09-07T08:19:26.5303821Z 2025-09-07T08:19:26.5304074Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5304167Z 2025-09-07T08:19:26.5304263Z warnings.warn(msg) 2025-09-07T08:19:26.5304355Z 2025-09-07T08:19:26.5304601Z --- Parse Warning: 123 / 146 --- 2025-09-07T08:19:26.5305595Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1725. 2025-09-07T08:19:26.5305871Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5306124Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-09-07T08:19:26.5306217Z 2025-09-07T08:19:26.5306396Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-09-07T08:19:26.5306631Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-09-07T08:19:26.5306824Z of epochs between two warm restarts in SGDR: 2025-09-07T08:19:26.5306932Z 2025-09-07T08:19:26.5307028Z .. math:: 2025-09-07T08:19:26.5307209Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-09-07T08:19:26.5307352Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-09-07T08:19:26.5307442Z 2025-09-07T08:19:26.5307607Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-09-07T08:19:26.5307804Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-09-07T08:19:26.5307883Z 2025-09-07T08:19:26.5307986Z It has been proposed in 2025-09-07T08:19:26.5308174Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-09-07T08:19:26.5308256Z 2025-09-07T08:19:26.5308354Z Args: 2025-09-07T08:19:26.5308488Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:26.5308648Z T_0 (int): Number of iterations until the first restart. 2025-09-07T08:19:26.5308941Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-09-07T08:19:26.5309131Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-09-07T08:19:26.5309382Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-09-07T08:19:26.5309459Z 2025-09-07T08:19:26.5309625Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-09-07T08:19:26.5309758Z https://arxiv.org/abs/1608.03983 2025-09-07T08:19:26.5309840Z 2025-09-07T08:19:26.5309939Z Example: 2025-09-07T08:19:26.5310037Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5310223Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-09-07T08:19:26.5310459Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-09-07T08:19:26.5310558Z ... optimizer, T_0=20 2025-09-07T08:19:26.5310651Z ... ) 2025-09-07T08:19:26.5310757Z >>> for epoch in range(100): 2025-09-07T08:19:26.5310847Z >>> train(...) 2025-09-07T08:19:26.5310950Z >>> validate(...) 2025-09-07T08:19:26.5311074Z >>> scheduler.step() 2025-09-07T08:19:26.5311166Z 2025-09-07T08:19:26.5311399Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-09-07T08:19:26.5311480Z 2025-09-07T08:19:26.5311742Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5311819Z 2025-09-07T08:19:26.5311915Z warnings.warn(msg) 2025-09-07T08:19:26.5312005Z 2025-09-07T08:19:26.5312199Z --- Parse Warning: 124 / 146 --- 2025-09-07T08:19:26.5313104Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1875. 2025-09-07T08:19:26.5313370Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5313694Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-09-07T08:19:26.5313775Z 2025-09-07T08:19:26.5314072Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-09-07T08:19:26.5314373Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-09-07T08:19:26.5314491Z lower than the initial learning rate. 2025-09-07T08:19:26.5314730Z This policy was initially described in the paper `Super-Convergence: 2025-09-07T08:19:26.5314941Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-09-07T08:19:26.5315018Z 2025-09-07T08:19:26.5315273Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-09-07T08:19:26.5315500Z `step` should be called after a batch has been used for training. 2025-09-07T08:19:26.5315617Z 2025-09-07T08:19:26.5315728Z This scheduler is not chainable. 2025-09-07T08:19:26.5315805Z 2025-09-07T08:19:26.5316061Z Note also that the total number of steps in the cycle can be determined in one 2025-09-07T08:19:26.5316198Z of two ways (listed in order of precedence): 2025-09-07T08:19:26.5316289Z 2025-09-07T08:19:26.5316438Z #. A value for total_steps is explicitly provided. 2025-09-07T08:19:26.5316619Z #. A number of epochs (epochs) and a number of steps per epoch 2025-09-07T08:19:26.5316737Z (steps_per_epoch) are provided. 2025-09-07T08:19:26.5316895Z In this case, the number of total steps is inferred by 2025-09-07T08:19:26.5317028Z total_steps = epochs * steps_per_epoch 2025-09-07T08:19:26.5317105Z 2025-09-07T08:19:26.5317342Z You must either provide a value for total_steps or provide a value for both 2025-09-07T08:19:26.5317460Z epochs and steps_per_epoch. 2025-09-07T08:19:26.5317539Z 2025-09-07T08:19:26.5317843Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-09-07T08:19:26.5318148Z claims that "unpublished work has shown even better results by using only two phases". To 2025-09-07T08:19:26.5318414Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-09-07T08:19:26.5318503Z 2025-09-07T08:19:26.5318585Z Args: 2025-09-07T08:19:26.5318724Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T08:19:26.5318934Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T08:19:26.5319045Z for each parameter group. 2025-09-07T08:19:26.5319260Z total_steps (int): The total number of steps in the cycle. Note that 2025-09-07T08:19:26.5319472Z if a value is not provided here, then it must be inferred by providing 2025-09-07T08:19:26.5319614Z a value for epochs and steps_per_epoch. 2025-09-07T08:19:26.5319710Z Default: None 2025-09-07T08:19:26.5319911Z epochs (int): The number of epochs to train for. This is used along 2025-09-07T08:19:26.5320190Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-09-07T08:19:26.5320330Z if a value for total_steps is not provided. 2025-09-07T08:19:26.5320435Z Default: None 2025-09-07T08:19:26.5320664Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-09-07T08:19:26.5320893Z used along with epochs in order to infer the total number of steps in the 2025-09-07T08:19:26.5321057Z cycle if a value for total_steps is not provided. 2025-09-07T08:19:26.5321152Z Default: None 2025-09-07T08:19:26.5321393Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-09-07T08:19:26.5321509Z increasing the learning rate. 2025-09-07T08:19:26.5321606Z Default: 0.3 2025-09-07T08:19:26.5321742Z anneal_strategy (str): {'cos', 'linear'} 2025-09-07T08:19:26.5321991Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-09-07T08:19:26.5322103Z linear annealing. 2025-09-07T08:19:26.5322195Z Default: 'cos' 2025-09-07T08:19:26.5322392Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T08:19:26.5322590Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T08:19:26.5322687Z Default: True 2025-09-07T08:19:26.5322911Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T08:19:26.5323113Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T08:19:26.5323302Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T08:19:26.5323454Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T08:19:26.5323575Z Default: 0.85 2025-09-07T08:19:26.5323798Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T08:19:26.5323933Z for each parameter group. Functionally, 2025-09-07T08:19:26.5324219Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T08:19:26.5324362Z Note that momentum is cycled inversely 2025-09-07T08:19:26.5324572Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T08:19:26.5324699Z and learning rate is 'base_lr' 2025-09-07T08:19:26.5324793Z Default: 0.95 2025-09-07T08:19:26.5324977Z div_factor (float): Determines the initial learning rate via 2025-09-07T08:19:26.5325101Z initial_lr = max_lr/div_factor 2025-09-07T08:19:26.5325194Z Default: 25 2025-09-07T08:19:26.5325411Z final_div_factor (float): Determines the minimum learning rate via 2025-09-07T08:19:26.5325536Z min_lr = initial_lr/final_div_factor 2025-09-07T08:19:26.5325631Z Default: 1e4 2025-09-07T08:19:26.5325920Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-09-07T08:19:26.5326175Z learning rate according to 'final_div_factor' instead of modifying the second 2025-09-07T08:19:26.5326428Z phase (the first two phases will be symmetrical about the step indicated by 2025-09-07T08:19:26.5326522Z 'pct_start'). 2025-09-07T08:19:26.5326741Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T08:19:26.5326969Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T08:19:26.5327184Z batch instead of after each epoch, this number represents the total 2025-09-07T08:19:26.5327417Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T08:19:26.5327608Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T08:19:26.5327743Z Default: -1 2025-09-07T08:19:26.5327826Z 2025-09-07T08:19:26.5327911Z Example: 2025-09-07T08:19:26.5328023Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5328175Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T08:19:26.5328408Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-09-07T08:19:26.5328580Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-09-07T08:19:26.5328794Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-09-07T08:19:26.5328890Z ... ) 2025-09-07T08:19:26.5328992Z >>> for epoch in range(10): 2025-09-07T08:19:26.5329106Z >>> for batch in data_loader: 2025-09-07T08:19:26.5329216Z >>> train_batch(...) 2025-09-07T08:19:26.5329320Z >>> optimizer.step() 2025-09-07T08:19:26.5329432Z >>> scheduler.step() 2025-09-07T08:19:26.5329515Z 2025-09-07T08:19:26.5329683Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-09-07T08:19:26.5329774Z 2025-09-07T08:19:26.5330066Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-09-07T08:19:26.5330205Z https://arxiv.org/abs/1708.07120 2025-09-07T08:19:26.5330286Z 2025-09-07T08:19:26.5330543Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5330634Z 2025-09-07T08:19:26.5330727Z warnings.warn(msg) 2025-09-07T08:19:26.5330823Z 2025-09-07T08:19:26.5331029Z --- Parse Warning: 125 / 146 --- 2025-09-07T08:19:26.5331987Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/optimizer.py line=868. 2025-09-07T08:19:26.5332302Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5332407Z Load the optimizer state. 2025-09-07T08:19:26.5332498Z 2025-09-07T08:19:26.5332580Z Args: 2025-09-07T08:19:26.5332777Z state_dict (dict): optimizer state. Should be an object returned 2025-09-07T08:19:26.5332910Z from a call to :meth:`state_dict`. 2025-09-07T08:19:26.5332986Z 2025-09-07T08:19:26.5333089Z .. warning:: 2025-09-07T08:19:26.5333425Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-09-07T08:19:26.5333638Z as calling it beforehand will overwrite the loaded learning rates. 2025-09-07T08:19:26.5333730Z 2025-09-07T08:19:26.5333817Z .. note:: 2025-09-07T08:19:26.5334117Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-09-07T08:19:26.5334298Z in :meth:`state_dict`) will not affect the loading process. 2025-09-07T08:19:26.5334647Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-09-07T08:19:26.5334813Z differ from those initialized in the optimizer), 2025-09-07T08:19:26.5335119Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-09-07T08:19:26.5335225Z accordingly. 2025-09-07T08:19:26.5335517Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-09-07T08:19:26.5335839Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-09-07T08:19:26.5336019Z the optimizer ``param_names`` will remain unchanged. 2025-09-07T08:19:26.5336099Z 2025-09-07T08:19:26.5336197Z Example: 2025-09-07T08:19:26.5336299Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5336455Z >>> model = torch.nn.Linear(10, 10) 2025-09-07T08:19:26.5336634Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-09-07T08:19:26.5336790Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-09-07T08:19:26.5336891Z ... optim, 2025-09-07T08:19:26.5336992Z ... start_factor=0.1, 2025-09-07T08:19:26.5337087Z ... end_factor=1, 2025-09-07T08:19:26.5337200Z ... total_iters=20, 2025-09-07T08:19:26.5337281Z ... ) 2025-09-07T08:19:26.5337486Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-09-07T08:19:26.5337578Z ... optim, 2025-09-07T08:19:26.5337671Z ... T_max=80, 2025-09-07T08:19:26.5337775Z ... eta_min=3e-5, 2025-09-07T08:19:26.5337858Z ... ) 2025-09-07T08:19:26.5338013Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-09-07T08:19:26.5338111Z ... optim, 2025-09-07T08:19:26.5338244Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T08:19:26.5338342Z ... milestones=[20], 2025-09-07T08:19:26.5338435Z ... ) 2025-09-07T08:19:26.5338580Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-09-07T08:19:26.5338796Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-09-07T08:19:26.5338962Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-09-07T08:19:26.5339040Z 2025-09-07T08:19:26.5339132Z 2025-09-07T08:19:26.5339382Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5339498Z 2025-09-07T08:19:26.5339594Z warnings.warn(msg) 2025-09-07T08:19:26.5339698Z 2025-09-07T08:19:26.5339903Z --- Parse Warning: 126 / 146 --- 2025-09-07T08:19:26.5340792Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=120. 2025-09-07T08:19:26.5341065Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5341426Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-09-07T08:19:26.5341505Z 2025-09-07T08:19:26.5341757Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-09-07T08:19:26.5341969Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-09-07T08:19:26.5342199Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-09-07T08:19:26.5342283Z (UAI 2018). 2025-09-07T08:19:26.5342365Z 2025-09-07T08:19:26.5342589Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-09-07T08:19:26.5342828Z but using exponential weights instead of equal weights across iterations. 2025-09-07T08:19:26.5342942Z 2025-09-07T08:19:26.5343177Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-09-07T08:19:26.5343399Z on the device :attr:`device` and allows to compute running averages of the 2025-09-07T08:19:26.5343519Z parameters of the :attr:`model`. 2025-09-07T08:19:26.5343596Z 2025-09-07T08:19:26.5343687Z Args: 2025-09-07T08:19:26.5343839Z model (torch.nn.Module): model to use with SWA/EMA 2025-09-07T08:19:26.5344071Z device (torch.device, optional): if provided, the averaged model will be 2025-09-07T08:19:26.5344195Z stored on the :attr:`device` 2025-09-07T08:19:26.5344401Z avg_fn (function, optional): the averaging function used to update 2025-09-07T08:19:26.5344614Z parameters; the function must take in the current value of the 2025-09-07T08:19:26.5344858Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-09-07T08:19:26.5345054Z parameter, and the number of models already averaged; if None, 2025-09-07T08:19:26.5345225Z an equally weighted average is used (default: None) 2025-09-07T08:19:26.5345451Z multi_avg_fn (function, optional): the averaging function used to update 2025-09-07T08:19:26.5345696Z parameters inplace; the function must take in the current values of the 2025-09-07T08:19:26.5345962Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-09-07T08:19:26.5346191Z parameters as a list, and the number of models already averaged; if None, 2025-09-07T08:19:26.5346361Z an equally weighted average is used (default: None) 2025-09-07T08:19:26.5346565Z use_buffers (bool): if ``True``, it will compute running averages for 2025-09-07T08:19:26.5346795Z both the parameters and the buffers of the model. (default: ``False``) 2025-09-07T08:19:26.5346875Z 2025-09-07T08:19:26.5346971Z Example: 2025-09-07T08:19:26.5347099Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.5347225Z >>> loader, optimizer, model, loss_fn = ... 2025-09-07T08:19:26.5347406Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-09-07T08:19:26.5347628Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-09-07T08:19:26.5347750Z >>> T_max=300) 2025-09-07T08:19:26.5347844Z >>> swa_start = 160 2025-09-07T08:19:26.5347983Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-09-07T08:19:26.5348116Z >>> for i in range(300): 2025-09-07T08:19:26.5348230Z >>> for input, target in loader: 2025-09-07T08:19:26.5348375Z >>> optimizer.zero_grad() 2025-09-07T08:19:26.5348512Z >>> loss_fn(model(input), target).backward() 2025-09-07T08:19:26.5348618Z >>> optimizer.step() 2025-09-07T08:19:26.5348727Z >>> if i > swa_start: 2025-09-07T08:19:26.5348855Z >>> swa_model.update_parameters(model) 2025-09-07T08:19:26.5348964Z >>> swa_scheduler.step() 2025-09-07T08:19:26.5349063Z >>> else: 2025-09-07T08:19:26.5349165Z >>> scheduler.step() 2025-09-07T08:19:26.5349258Z >>> 2025-09-07T08:19:26.5349409Z >>> # Update bn statistics for the swa_model at the end 2025-09-07T08:19:26.5349570Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-09-07T08:19:26.5349662Z 2025-09-07T08:19:26.5349962Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-09-07T08:19:26.5350170Z If no averaging function is provided, the default is to compute 2025-09-07T08:19:26.5350317Z equally-weighted average of the weights (SWA). 2025-09-07T08:19:26.5350454Z 2025-09-07T08:19:26.5350549Z Example: 2025-09-07T08:19:26.5350676Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T08:19:26.5350893Z >>> # Compute exponential moving averages of the weights and buffers 2025-09-07T08:19:26.5351060Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-09-07T08:19:26.5351275Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-09-07T08:19:26.5351363Z 2025-09-07T08:19:26.5351447Z .. note:: 2025-09-07T08:19:26.5351678Z When using SWA/EMA with models containing Batch Normalization you may 2025-09-07T08:19:26.5351881Z need to update the activation statistics for Batch Normalization. 2025-09-07T08:19:26.5352115Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-09-07T08:19:26.5352373Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-09-07T08:19:26.5352615Z statistics in a post-training step by passing data through the model. The 2025-09-07T08:19:26.5352864Z second does it during the parameter update phase by averaging all buffers. 2025-09-07T08:19:26.5353108Z Empirical evidence has shown that updating the statistics in normalization 2025-09-07T08:19:26.5353342Z layers increases accuracy, but you may wish to empirically test which 2025-09-07T08:19:26.5353498Z approach yields the best results in your problem. 2025-09-07T08:19:26.5353574Z 2025-09-07T08:19:26.5353670Z .. note:: 2025-09-07T08:19:26.5353924Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-09-07T08:19:26.5354013Z 2025-09-07T08:19:26.5354099Z .. note:: 2025-09-07T08:19:26.5354297Z When :meth:`update_parameters` is called for the first time (i.e. 2025-09-07T08:19:26.5354492Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-09-07T08:19:26.5354693Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-09-07T08:19:26.5354880Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-09-07T08:19:26.5355003Z to update the parameters. 2025-09-07T08:19:26.5355078Z 2025-09-07T08:19:26.5355307Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T08:19:26.5355424Z https://arxiv.org/abs/1803.05407 2025-09-07T08:19:26.5355661Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-09-07T08:19:26.5355754Z Average: 2025-09-07T08:19:26.5355893Z https://arxiv.org/abs/1806.05594 2025-09-07T08:19:26.5356099Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-09-07T08:19:26.5356237Z https://arxiv.org/abs/1904.11943 2025-09-07T08:19:26.5356460Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-09-07T08:19:26.5356569Z Generalizes Well: 2025-09-07T08:19:26.5356684Z https://arxiv.org/abs/2001.02312 2025-09-07T08:19:26.5356791Z .. _Polyak averaging: 2025-09-07T08:19:26.5356956Z https://paperswithcode.com/method/polyak-averaging 2025-09-07T08:19:26.5357036Z 2025-09-07T08:19:26.5357295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5357371Z 2025-09-07T08:19:26.5357477Z warnings.warn(msg) 2025-09-07T08:19:26.5357553Z 2025-09-07T08:19:26.5357749Z --- Parse Warning: 127 / 146 --- 2025-09-07T08:19:26.5358607Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=375. 2025-09-07T08:19:26.5358872Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5359123Z Anneals the learning rate in each parameter group to a fixed value. 2025-09-07T08:19:26.5359199Z 2025-09-07T08:19:26.5359428Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-09-07T08:19:26.5359648Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-09-07T08:19:26.5359727Z 2025-09-07T08:19:26.5359818Z Args: 2025-09-07T08:19:26.5359986Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-09-07T08:19:26.5360189Z swa_lrs (float or list): the learning rate value for all param groups 2025-09-07T08:19:26.5360332Z together or separately for each group. 2025-09-07T08:19:26.5360534Z annealing_epochs (int): number of epochs in the annealing phase 2025-09-07T08:19:26.5360640Z (default: 10) 2025-09-07T08:19:26.5360849Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-09-07T08:19:26.5361080Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-09-07T08:19:26.5361189Z (default: "cos") 2025-09-07T08:19:26.5361365Z last_epoch (int): the index of the last epoch (default: -1) 2025-09-07T08:19:26.5361449Z 2025-09-07T08:19:26.5361626Z The :class:`SWALR` scheduler can be used together with other 2025-09-07T08:19:26.5361841Z schedulers to switch to a constant learning rate late in the training 2025-09-07T08:19:26.5361946Z as in the example below. 2025-09-07T08:19:26.5362023Z 2025-09-07T08:19:26.5362115Z Example: 2025-09-07T08:19:26.5362245Z >>> # xdoctest: +SKIP("Undefined variables") 2025-09-07T08:19:26.5362361Z >>> loader, optimizer, model = ... 2025-09-07T08:19:26.5362478Z >>> lr_lambda = lambda epoch: 0.9 2025-09-07T08:19:26.5362698Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-09-07T08:19:26.5362807Z >>> lr_lambda=lr_lambda) 2025-09-07T08:19:26.5362977Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-09-07T08:19:26.5363154Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-09-07T08:19:26.5363253Z >>> swa_start = 160 2025-09-07T08:19:26.5363346Z >>> for i in range(300): 2025-09-07T08:19:26.5363459Z >>> for input, target in loader: 2025-09-07T08:19:26.5363573Z >>> optimizer.zero_grad() 2025-09-07T08:19:26.5363700Z >>> loss_fn(model(input), target).backward() 2025-09-07T08:19:26.5363810Z >>> optimizer.step() 2025-09-07T08:19:26.5363905Z >>> if i > swa_start: 2025-09-07T08:19:26.5364037Z >>> swa_scheduler.step() 2025-09-07T08:19:26.5364231Z >>> else: 2025-09-07T08:19:26.5364332Z >>> scheduler.step() 2025-09-07T08:19:26.5364420Z 2025-09-07T08:19:26.5364642Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T08:19:26.5364762Z https://arxiv.org/abs/1803.05407 2025-09-07T08:19:26.5364850Z 2025-09-07T08:19:26.5365099Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5365183Z 2025-09-07T08:19:26.5365276Z warnings.warn(msg) 2025-09-07T08:19:26.5365353Z 2025-09-07T08:19:26.5365559Z --- Parse Warning: 128 / 146 --- 2025-09-07T08:19:26.5366471Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1331. 2025-09-07T08:19:26.5366746Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5366894Z Asserts that ``actual`` and ``expected`` are close. 2025-09-07T08:19:26.5366972Z 2025-09-07T08:19:26.5367344Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-09-07T08:19:26.5367466Z 2025-09-07T08:19:26.5367560Z .. math:: 2025-09-07T08:19:26.5367637Z 2025-09-07T08:19:26.5368001Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-09-07T08:19:26.5368086Z 2025-09-07T08:19:26.5368434Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-09-07T08:19:26.5368648Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-09-07T08:19:26.5368724Z 2025-09-07T08:19:26.5368924Z In addition, they are only considered close if they have the same 2025-09-07T08:19:26.5369010Z 2025-09-07T08:19:26.5369204Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-09-07T08:19:26.5369369Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-09-07T08:19:26.5369512Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-09-07T08:19:26.5369633Z - stride (if ``check_stride`` is ``True``). 2025-09-07T08:19:26.5369718Z 2025-09-07T08:19:26.5370013Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-09-07T08:19:26.5370099Z 2025-09-07T08:19:26.5370457Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-09-07T08:19:26.5370834Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-09-07T08:19:26.5371072Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-09-07T08:19:26.5371460Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-09-07T08:19:26.5371546Z 2025-09-07T08:19:26.5371829Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-09-07T08:19:26.5372197Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-09-07T08:19:26.5372290Z definition above. 2025-09-07T08:19:26.5372368Z 2025-09-07T08:19:26.5372680Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-09-07T08:19:26.5373054Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-09-07T08:19:26.5373603Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-09-07T08:19:26.5374039Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-09-07T08:19:26.5374304Z their elements are considered close according to the above definition. 2025-09-07T08:19:26.5374395Z 2025-09-07T08:19:26.5374480Z .. note:: 2025-09-07T08:19:26.5374570Z 2025-09-07T08:19:26.5374902Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-09-07T08:19:26.5375223Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-09-07T08:19:26.5375513Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-09-07T08:19:26.5375590Z 2025-09-07T08:19:26.5375686Z Args: 2025-09-07T08:19:26.5375787Z actual (Any): Actual input. 2025-09-07T08:19:26.5375901Z expected (Any): Expected input. 2025-09-07T08:19:26.5376269Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-09-07T08:19:26.5376429Z are allowed. Otherwise type equality is required. 2025-09-07T08:19:26.5376802Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-09-07T08:19:26.5377092Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T08:19:26.5377448Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-09-07T08:19:26.5377715Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T08:19:26.5377958Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-09-07T08:19:26.5378251Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-09-07T08:19:26.5378495Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-09-07T08:19:26.5378772Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-09-07T08:19:26.5379121Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-09-07T08:19:26.5379461Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-09-07T08:19:26.5379630Z :func:`torch.promote_types`) before being compared. 2025-09-07T08:19:26.5379978Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-09-07T08:19:26.5380317Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-09-07T08:19:26.5380404Z compared. 2025-09-07T08:19:26.5380777Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-09-07T08:19:26.5381128Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-09-07T08:19:26.5381480Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-09-07T08:19:26.5381605Z should return the new message. 2025-09-07T08:19:26.5381681Z 2025-09-07T08:19:26.5381772Z Raises: 2025-09-07T08:19:26.5381999Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-09-07T08:19:26.5382161Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-09-07T08:19:26.5382495Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-09-07T08:19:26.5382878Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-09-07T08:19:26.5383012Z different types. 2025-09-07T08:19:26.5383372Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-09-07T08:19:26.5383743Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-09-07T08:19:26.5384054Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-09-07T08:19:26.5384348Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-09-07T08:19:26.5384473Z :attr:`~torch.Tensor.layout`. 2025-09-07T08:19:26.5384692Z AssertionError: If only one of corresponding tensors is quantized. 2025-09-07T08:19:26.5385088Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-09-07T08:19:26.5385378Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-09-07T08:19:26.5385494Z :attr:`~torch.Tensor.device`. 2025-09-07T08:19:26.5385836Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-09-07T08:19:26.5386216Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-09-07T08:19:26.5386587Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-09-07T08:19:26.5386665Z 2025-09-07T08:19:26.5387032Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-09-07T08:19:26.5387186Z ``dtype``'s, the maximum of both tolerances is used. 2025-09-07T08:19:26.5387267Z 2025-09-07T08:19:26.5387404Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5387534Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-09-07T08:19:26.5387648Z +===========================+============+==========+ 2025-09-07T08:19:26.5387806Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-09-07T08:19:26.5387934Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5388078Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-09-07T08:19:26.5388200Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5388340Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:26.5388459Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5388590Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-09-07T08:19:26.5388718Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5388855Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-09-07T08:19:26.5388982Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5389116Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:26.5389238Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5389378Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-09-07T08:19:26.5389496Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5389635Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:26.5389751Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5389881Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:26.5390006Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5390137Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:26.5390264Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5390417Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:26.5390561Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5390700Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T08:19:26.5390821Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5390948Z | other | ``0.0`` | ``0.0`` | 2025-09-07T08:19:26.5391064Z +---------------------------+------------+----------+ 2025-09-07T08:19:26.5391143Z 2025-09-07T08:19:26.5391235Z .. note:: 2025-09-07T08:19:26.5391310Z 2025-09-07T08:19:26.5391699Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-09-07T08:19:26.5392048Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-09-07T08:19:26.5392311Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-09-07T08:19:26.5392397Z 2025-09-07T08:19:26.5392494Z >>> import functools 2025-09-07T08:19:26.5392764Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-09-07T08:19:26.5392898Z >>> assert_equal(1e-9, 1e-10) 2025-09-07T08:19:26.5393011Z Traceback (most recent call last): 2025-09-07T08:19:26.5393099Z ... 2025-09-07T08:19:26.5393223Z AssertionError: Scalars are not equal! 2025-09-07T08:19:26.5393320Z 2025-09-07T08:19:26.5393429Z Expected 1e-10 but got 1e-09. 2025-09-07T08:19:26.5393554Z Absolute difference: 9.000000000000001e-10 2025-09-07T08:19:26.5393668Z Relative difference: 9.0 2025-09-07T08:19:26.5393747Z 2025-09-07T08:19:26.5393840Z Examples: 2025-09-07T08:19:26.5393951Z >>> # tensor to tensor comparison 2025-09-07T08:19:26.5394085Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-09-07T08:19:26.5394229Z >>> actual = torch.acos(torch.cos(expected)) 2025-09-07T08:19:26.5394373Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:26.5394483Z 2025-09-07T08:19:26.5394599Z >>> # scalar to scalar comparison 2025-09-07T08:19:26.5394695Z >>> import math 2025-09-07T08:19:26.5394809Z >>> expected = math.sqrt(2.0) 2025-09-07T08:19:26.5394912Z >>> actual = 2.0 / math.sqrt(2.0) 2025-09-07T08:19:26.5395054Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:26.5395141Z 2025-09-07T08:19:26.5395264Z >>> # numpy array to numpy array comparison 2025-09-07T08:19:26.5395368Z >>> import numpy as np 2025-09-07T08:19:26.5395487Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-09-07T08:19:26.5395607Z >>> actual = np.arccos(np.cos(expected)) 2025-09-07T08:19:26.5395760Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:26.5395838Z 2025-09-07T08:19:26.5395963Z >>> # sequence to sequence comparison 2025-09-07T08:19:26.5396062Z >>> import numpy as np 2025-09-07T08:19:26.5396313Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-09-07T08:19:26.5396455Z >>> # length and their elements have to match. 2025-09-07T08:19:26.5396607Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-09-07T08:19:26.5396720Z >>> actual = tuple(expected) 2025-09-07T08:19:26.5396859Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:26.5396937Z 2025-09-07T08:19:26.5397059Z >>> # mapping to mapping comparison 2025-09-07T08:19:26.5397179Z >>> from collections import OrderedDict 2025-09-07T08:19:26.5397284Z >>> import numpy as np 2025-09-07T08:19:26.5397388Z >>> foo = torch.tensor(1.0) 2025-09-07T08:19:26.5397498Z >>> bar = 2.0 2025-09-07T08:19:26.5397638Z >>> baz = np.array(3.0) 2025-09-07T08:19:26.5397888Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-09-07T08:19:26.5398094Z >>> # have to have the same set of keys and their elements have to match. 2025-09-07T08:19:26.5398294Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-09-07T08:19:26.5398429Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-09-07T08:19:26.5398586Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:26.5398664Z 2025-09-07T08:19:26.5398795Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T08:19:26.5398901Z >>> actual = expected.clone() 2025-09-07T08:19:26.5399063Z >>> # By default, directly related instances can be compared 2025-09-07T08:19:26.5399284Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-09-07T08:19:26.5399468Z >>> # This check can be made more strict with allow_subclasses=False 2025-09-07T08:19:26.5399589Z >>> torch.testing.assert_close( 2025-09-07T08:19:26.5399789Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-09-07T08:19:26.5399894Z ... ) 2025-09-07T08:19:26.5400016Z Traceback (most recent call last): 2025-09-07T08:19:26.5400097Z ... 2025-09-07T08:19:26.5400306Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T08:19:26.5400519Z and . 2025-09-07T08:19:26.5400740Z >>> # If the inputs are not directly related, they are never considered close 2025-09-07T08:19:26.5400915Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-09-07T08:19:26.5401028Z Traceback (most recent call last): 2025-09-07T08:19:26.5401114Z ... 2025-09-07T08:19:26.5401401Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T08:19:26.5401507Z and . 2025-09-07T08:19:26.5401802Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-09-07T08:19:26.5401920Z >>> # their type if check_dtype=False. 2025-09-07T08:19:26.5402093Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-09-07T08:19:26.5402170Z 2025-09-07T08:19:26.5402270Z >>> # NaN != NaN by default. 2025-09-07T08:19:26.5402399Z >>> expected = torch.tensor(float("Nan")) 2025-09-07T08:19:26.5402507Z >>> actual = expected.clone() 2025-09-07T08:19:26.5402657Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T08:19:26.5402770Z Traceback (most recent call last): 2025-09-07T08:19:26.5402851Z ... 2025-09-07T08:19:26.5402983Z AssertionError: Scalars are not close! 2025-09-07T08:19:26.5403073Z 2025-09-07T08:19:26.5403184Z Expected nan but got nan. 2025-09-07T08:19:26.5403325Z Absolute difference: nan (up to 1e-05 allowed) 2025-09-07T08:19:26.5403471Z Relative difference: nan (up to 1.3e-06 allowed) 2025-09-07T08:19:26.5403678Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-09-07T08:19:26.5403756Z 2025-09-07T08:19:26.5403884Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T08:19:26.5404000Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-09-07T08:19:26.5404232Z >>> # The default error message can be overwritten. 2025-09-07T08:19:26.5404357Z >>> torch.testing.assert_close( 2025-09-07T08:19:26.5404535Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-09-07T08:19:26.5404626Z ... ) 2025-09-07T08:19:26.5404772Z Traceback (most recent call last): 2025-09-07T08:19:26.5404852Z ... 2025-09-07T08:19:26.5405036Z AssertionError: Argh, the tensors are not close! 2025-09-07T08:19:26.5405256Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-09-07T08:19:26.5405364Z >>> # extra information 2025-09-07T08:19:26.5405474Z >>> torch.testing.assert_close( 2025-09-07T08:19:26.5405669Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-09-07T08:19:26.5405756Z ... ) 2025-09-07T08:19:26.5405868Z Traceback (most recent call last): 2025-09-07T08:19:26.5405948Z ... 2025-09-07T08:19:26.5406058Z AssertionError: Header 2025-09-07T08:19:26.5406146Z 2025-09-07T08:19:26.5406257Z Tensor-likes are not close! 2025-09-07T08:19:26.5406340Z 2025-09-07T08:19:26.5406450Z Mismatched elements: 2 / 3 (66.7%) 2025-09-07T08:19:26.5406685Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-09-07T08:19:26.5406912Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-09-07T08:19:26.5407006Z 2025-09-07T08:19:26.5407113Z Footer 2025-09-07T08:19:26.5407191Z 2025-09-07T08:19:26.5407446Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5407522Z 2025-09-07T08:19:26.5407625Z warnings.warn(msg) 2025-09-07T08:19:26.5407700Z 2025-09-07T08:19:26.5407916Z --- Parse Warning: 129 / 146 --- 2025-09-07T08:19:26.5408852Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=134. 2025-09-07T08:19:26.5409115Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5409265Z Register a container-like type as pytree node. 2025-09-07T08:19:26.5409345Z 2025-09-07T08:19:26.5409424Z Args: 2025-09-07T08:19:26.5409642Z cls (type): A Python type to treat as an internal pytree node. 2025-09-07T08:19:26.5409912Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-09-07T08:19:26.5410170Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-09-07T08:19:26.5410459Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-09-07T08:19:26.5410575Z passed to the ``unflatten_fn``. 2025-09-07T08:19:26.5410853Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-09-07T08:19:26.5411108Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-09-07T08:19:26.5411272Z The function should return an instance of ``cls``. 2025-09-07T08:19:26.5411537Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-09-07T08:19:26.5411709Z qualified name used when serializing the tree spec. 2025-09-07T08:19:26.5412015Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-09-07T08:19:26.5412290Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-09-07T08:19:26.5412564Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-09-07T08:19:26.5412863Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-09-07T08:19:26.5413130Z how to convert the custom json dumpable representation of the context back to the 2025-09-07T08:19:26.5413409Z original context. This is used for json deserialization, which is being used in 2025-09-07T08:19:26.5413551Z :mod:`torch.export` right now. 2025-09-07T08:19:26.5413635Z 2025-09-07T08:19:26.5413720Z Example:: 2025-09-07T08:19:26.5413805Z 2025-09-07T08:19:26.5413901Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5414042Z >>> # Registry a Python type with lambda functions 2025-09-07T08:19:26.5414150Z >>> register_pytree_node( 2025-09-07T08:19:26.5414233Z ... set, 2025-09-07T08:19:26.5414362Z ... lambda s: (sorted(s), None, None), 2025-09-07T08:19:26.5414480Z ... lambda children, _: set(children), 2025-09-07T08:19:26.5414559Z ... ) 2025-09-07T08:19:26.5414648Z 2025-09-07T08:19:26.5414897Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5414983Z 2025-09-07T08:19:26.5415078Z warnings.warn(msg) 2025-09-07T08:19:26.5415153Z 2025-09-07T08:19:26.5415354Z --- Parse Warning: 130 / 146 --- 2025-09-07T08:19:26.5416328Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1226. 2025-09-07T08:19:26.5416623Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5416697Z 2025-09-07T08:19:26.5416909Z Context passed to policy function during selective checkpointing. 2025-09-07T08:19:26.5416993Z 2025-09-07T08:19:26.5417215Z This class is used to pass relevant metadata to the policy function during 2025-09-07T08:19:26.5417486Z selective checkpointing. The metadata includes whether the current invocation 2025-09-07T08:19:26.5417643Z of the policy function is during recomputation or not. 2025-09-07T08:19:26.5417718Z 2025-09-07T08:19:26.5417809Z Example: 2025-09-07T08:19:26.5417908Z >>> # xdoctest: +SKIP(stub) 2025-09-07T08:19:26.5417990Z >>> 2025-09-07T08:19:26.5418128Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T08:19:26.5418232Z >>> print(ctx.is_recompute) 2025-09-07T08:19:26.5418342Z >>> 2025-09-07T08:19:26.5418616Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T08:19:26.5418695Z >>> 2025-09-07T08:19:26.5418856Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T08:19:26.5418944Z >>> fn, x, y, 2025-09-07T08:19:26.5419053Z >>> use_reentrant=False, 2025-09-07T08:19:26.5419159Z >>> context_fn=context_fn, 2025-09-07T08:19:26.5419239Z >>> ) 2025-09-07T08:19:26.5419335Z 2025-09-07T08:19:26.5419587Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5419677Z 2025-09-07T08:19:26.5419773Z warnings.warn(msg) 2025-09-07T08:19:26.5419853Z 2025-09-07T08:19:26.5420053Z --- Parse Warning: 131 / 146 --- 2025-09-07T08:19:26.5421053Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1366. 2025-09-07T08:19:26.5421331Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5421410Z 2025-09-07T08:19:26.5421646Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-09-07T08:19:26.5421740Z 2025-09-07T08:19:26.5421953Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-09-07T08:19:26.5422126Z operations are recomputed during the backward pass. 2025-09-07T08:19:26.5422208Z 2025-09-07T08:19:26.5422291Z Args: 2025-09-07T08:19:26.5422424Z policy_fn_or_list (Callable or List): 2025-09-07T08:19:26.5422617Z - If a policy function is provided, it should accept a 2025-09-07T08:19:26.5422874Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-09-07T08:19:26.5423105Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-09-07T08:19:26.5423347Z indicating whether the execution of the op should be recomputed or not. 2025-09-07T08:19:26.5423563Z - If a list of operations is provided, it is equivalent to a policy 2025-09-07T08:19:26.5423747Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-09-07T08:19:26.5423976Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-09-07T08:19:26.5424068Z operations. 2025-09-07T08:19:26.5424284Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-09-07T08:19:26.5424511Z raised if any tensors cached by selective activation checkpoint are 2025-09-07T08:19:26.5424719Z mutated in order to ensure correctness. If set to `True`, this check 2025-09-07T08:19:26.5424822Z is disabled. 2025-09-07T08:19:26.5424907Z Returns: 2025-09-07T08:19:26.5425017Z A tuple of two context managers. 2025-09-07T08:19:26.5425107Z 2025-09-07T08:19:26.5425196Z Example: 2025-09-07T08:19:26.5425626Z >>> # xdoctest: +REQUIRES(LINUX) 2025-09-07T08:19:26.5425720Z >>> import functools 2025-09-07T08:19:26.5425800Z >>> 2025-09-07T08:19:26.5425938Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-09-07T08:19:26.5426063Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-09-07T08:19:26.5426140Z >>> 2025-09-07T08:19:26.5426245Z >>> ops_to_save = [ 2025-09-07T08:19:26.5426356Z >>> torch.ops.aten.mm.default, 2025-09-07T08:19:26.5426448Z >>> ] 2025-09-07T08:19:26.5426525Z >>> 2025-09-07T08:19:26.5426648Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T08:19:26.5426756Z >>> if op in ops_to_save: 2025-09-07T08:19:26.5426887Z >>> return CheckpointPolicy.MUST_SAVE 2025-09-07T08:19:26.5426980Z >>> else: 2025-09-07T08:19:26.5427121Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-09-07T08:19:26.5427222Z >>> 2025-09-07T08:19:26.5427506Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T08:19:26.5427585Z >>> 2025-09-07T08:19:26.5427691Z >>> # or equivalently 2025-09-07T08:19:26.5427963Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-09-07T08:19:26.5428040Z >>> 2025-09-07T08:19:26.5428137Z >>> def fn(x, y): 2025-09-07T08:19:26.5428337Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-09-07T08:19:26.5428427Z >>> 2025-09-07T08:19:26.5428565Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T08:19:26.5428656Z >>> fn, x, y, 2025-09-07T08:19:26.5428767Z >>> use_reentrant=False, 2025-09-07T08:19:26.5428867Z >>> context_fn=context_fn, 2025-09-07T08:19:26.5428949Z >>> ) 2025-09-07T08:19:26.5429040Z 2025-09-07T08:19:26.5429294Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5429386Z 2025-09-07T08:19:26.5429480Z warnings.warn(msg) 2025-09-07T08:19:26.5429557Z 2025-09-07T08:19:26.5429762Z --- Parse Warning: 132 / 146 --- 2025-09-07T08:19:26.5430675Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1158. 2025-09-07T08:19:26.5430944Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5431022Z 2025-09-07T08:19:26.5431164Z Create a :class:`setuptools.Extension` for C++. 2025-09-07T08:19:26.5431250Z 2025-09-07T08:19:26.5431529Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T08:19:26.5431781Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-09-07T08:19:26.5431860Z 2025-09-07T08:19:26.5432067Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T08:19:26.5432224Z constructor. Full list arguments can be found at 2025-09-07T08:19:26.5432548Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T08:19:26.5432638Z 2025-09-07T08:19:26.5432724Z .. warning:: 2025-09-07T08:19:26.5432951Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T08:19:26.5433167Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T08:19:26.5433361Z the user's responsibility in their library to not use APIs from 2025-09-07T08:19:26.5433599Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T08:19:26.5433816Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T08:19:26.5434027Z example, to give access to custom ops from python, the library should 2025-09-07T08:19:26.5434169Z register the ops through the dispatcher. 2025-09-07T08:19:26.5434274Z 2025-09-07T08:19:26.5434509Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T08:19:26.5434713Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T08:19:26.5434917Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T08:19:26.5435141Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T08:19:26.5435350Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T08:19:26.5435574Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T08:19:26.5435679Z CPython version of choice. 2025-09-07T08:19:26.5435759Z 2025-09-07T08:19:26.5435853Z Example: 2025-09-07T08:19:26.5435950Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5436107Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:26.5436244Z >>> from setuptools import setup 2025-09-07T08:19:26.5436463Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-09-07T08:19:26.5436558Z >>> setup( 2025-09-07T08:19:26.5436654Z ... name='extension', 2025-09-07T08:19:26.5436759Z ... ext_modules=[ 2025-09-07T08:19:26.5436854Z ... CppExtension( 2025-09-07T08:19:26.5436956Z ... name='extension', 2025-09-07T08:19:26.5437087Z ... sources=['extension.cpp'], 2025-09-07T08:19:26.5437205Z ... extra_compile_args=['-g'], 2025-09-07T08:19:26.5437365Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-09-07T08:19:26.5437445Z ... ], 2025-09-07T08:19:26.5437535Z ... cmdclass={ 2025-09-07T08:19:26.5437658Z ... 'build_ext': BuildExtension 2025-09-07T08:19:26.5437739Z ... }) 2025-09-07T08:19:26.5437827Z 2025-09-07T08:19:26.5438079Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5438156Z 2025-09-07T08:19:26.5438266Z warnings.warn(msg) 2025-09-07T08:19:26.5438344Z 2025-09-07T08:19:26.5438531Z --- Parse Warning: 133 / 146 --- 2025-09-07T08:19:26.5439453Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1228. 2025-09-07T08:19:26.5439712Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5439799Z 2025-09-07T08:19:26.5439956Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-09-07T08:19:26.5440071Z 2025-09-07T08:19:26.5440312Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T08:19:26.5440533Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-09-07T08:19:26.5440776Z extension. This includes the CUDA include path, library path and runtime 2025-09-07T08:19:26.5440861Z library. 2025-09-07T08:19:26.5440939Z 2025-09-07T08:19:26.5441154Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T08:19:26.5441298Z constructor. Full list arguments can be found at 2025-09-07T08:19:26.5441631Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T08:19:26.5441709Z 2025-09-07T08:19:26.5441795Z .. warning:: 2025-09-07T08:19:26.5442030Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T08:19:26.5442236Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T08:19:26.5442444Z the user's responsibility in their library to not use APIs from 2025-09-07T08:19:26.5442673Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T08:19:26.5442899Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T08:19:26.5443134Z example, to give access to custom ops from python, the library should 2025-09-07T08:19:26.5443259Z register the ops through the dispatcher. 2025-09-07T08:19:26.5443350Z 2025-09-07T08:19:26.5443568Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T08:19:26.5443782Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T08:19:26.5443988Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T08:19:26.5444277Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T08:19:26.5444505Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T08:19:26.5444717Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T08:19:26.5444832Z CPython version of choice. 2025-09-07T08:19:26.5444908Z 2025-09-07T08:19:26.5445021Z Example: 2025-09-07T08:19:26.5445132Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5445279Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:26.5445392Z >>> from setuptools import setup 2025-09-07T08:19:26.5445629Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-09-07T08:19:26.5445713Z >>> setup( 2025-09-07T08:19:26.5445827Z ... name='cuda_extension', 2025-09-07T08:19:26.5445924Z ... ext_modules=[ 2025-09-07T08:19:26.5446024Z ... CUDAExtension( 2025-09-07T08:19:26.5446149Z ... name='cuda_extension', 2025-09-07T08:19:26.5446313Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T08:19:26.5446453Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T08:19:26.5446571Z ... 'nvcc': ['-O2']}, 2025-09-07T08:19:26.5446725Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-09-07T08:19:26.5446819Z ... ], 2025-09-07T08:19:26.5446906Z ... cmdclass={ 2025-09-07T08:19:26.5447031Z ... 'build_ext': BuildExtension 2025-09-07T08:19:26.5447110Z ... }) 2025-09-07T08:19:26.5447186Z 2025-09-07T08:19:26.5447297Z Compute capabilities: 2025-09-07T08:19:26.5447375Z 2025-09-07T08:19:26.5447685Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T08:19:26.5447973Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-09-07T08:19:26.5448264Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-09-07T08:19:26.5448603Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-09-07T08:19:26.5448911Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-09-07T08:19:26.5449047Z support (see below for details on PTX). 2025-09-07T08:19:26.5449123Z 2025-09-07T08:19:26.5449432Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-09-07T08:19:26.5449560Z CCs you want the extension to support: 2025-09-07T08:19:26.5449636Z 2025-09-07T08:19:26.5449836Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-09-07T08:19:26.5450068Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-09-07T08:19:26.5450146Z 2025-09-07T08:19:26.5450476Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-09-07T08:19:26.5450794Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-09-07T08:19:26.5451112Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-09-07T08:19:26.5451404Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-09-07T08:19:26.5451767Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-09-07T08:19:26.5452040Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-09-07T08:19:26.5452362Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-09-07T08:19:26.5452687Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-09-07T08:19:26.5452785Z "8.0 8.6" would be better. 2025-09-07T08:19:26.5452875Z 2025-09-07T08:19:26.5453174Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T08:19:26.5453465Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T08:19:26.5453561Z 2025-09-07T08:19:26.5453914Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-09-07T08:19:26.5454140Z To workaround the issue, move python binding logic to pure C++ file. 2025-09-07T08:19:26.5454219Z 2025-09-07T08:19:26.5454306Z Example use: 2025-09-07T08:19:26.5454418Z #include 2025-09-07T08:19:26.5454572Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-09-07T08:19:26.5454664Z 2025-09-07T08:19:26.5454751Z Instead of: 2025-09-07T08:19:26.5454852Z #include 2025-09-07T08:19:26.5455021Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-09-07T08:19:26.5455101Z 2025-09-07T08:19:26.5455372Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-09-07T08:19:26.5455895Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-09-07T08:19:26.5455975Z 2025-09-07T08:19:26.5456097Z Relocatable device code linking: 2025-09-07T08:19:26.5456177Z 2025-09-07T08:19:26.5456453Z If you want to reference device symbols across compilation units (across object files), 2025-09-07T08:19:26.5456717Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-09-07T08:19:26.5457073Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-09-07T08:19:26.5457410Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-09-07T08:19:26.5457729Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-09-07T08:19:26.5457946Z helps reduce the protentional perf degradation of `-rdc`. 2025-09-07T08:19:26.5458112Z Note that it needs to be used at both steps to be useful. 2025-09-07T08:19:26.5458214Z 2025-09-07T08:19:26.5458595Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-09-07T08:19:26.5458766Z There is also a case where `-dlink` is used without `-rdc`: 2025-09-07T08:19:26.5459032Z when an extension is linked against a static lib containing rdc-compiled objects 2025-09-07T08:19:26.5459242Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-09-07T08:19:26.5459317Z 2025-09-07T08:19:26.5459526Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-09-07T08:19:26.5459604Z 2025-09-07T08:19:26.5459697Z Example: 2025-09-07T08:19:26.5459792Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5459938Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:26.5460047Z >>> CUDAExtension( 2025-09-07T08:19:26.5460153Z ... name='cuda_extension', 2025-09-07T08:19:26.5460327Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T08:19:26.5460420Z ... dlink=True, 2025-09-07T08:19:26.5460541Z ... dlink_libraries=["dlink_lib"], 2025-09-07T08:19:26.5460698Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T08:19:26.5460821Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-09-07T08:19:26.5460908Z 2025-09-07T08:19:26.5461157Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5461235Z 2025-09-07T08:19:26.5461341Z warnings.warn(msg) 2025-09-07T08:19:26.5461418Z 2025-09-07T08:19:26.5461625Z --- Parse Warning: 134 / 146 --- 2025-09-07T08:19:26.5462554Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1420. 2025-09-07T08:19:26.5462810Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5462898Z 2025-09-07T08:19:26.5463094Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-09-07T08:19:26.5463181Z 2025-09-07T08:19:26.5463422Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T08:19:26.5463616Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-09-07T08:19:26.5463711Z extension. 2025-09-07T08:19:26.5463789Z 2025-09-07T08:19:26.5463994Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T08:19:26.5464092Z constructor. 2025-09-07T08:19:26.5464172Z 2025-09-07T08:19:26.5464268Z .. warning:: 2025-09-07T08:19:26.5464495Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T08:19:26.5464703Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T08:19:26.5464907Z the user's responsibility in their library to not use APIs from 2025-09-07T08:19:26.5465139Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T08:19:26.5465364Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T08:19:26.5465573Z example, to give access to custom ops from python, the library should 2025-09-07T08:19:26.5465699Z register the ops through the dispatcher. 2025-09-07T08:19:26.5465787Z 2025-09-07T08:19:26.5466009Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T08:19:26.5466225Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T08:19:26.5466428Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T08:19:26.5466640Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T08:19:26.5466888Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T08:19:26.5467126Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T08:19:26.5467244Z CPython version of choice. 2025-09-07T08:19:26.5467323Z 2025-09-07T08:19:26.5467403Z Example: 2025-09-07T08:19:26.5467509Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5467658Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:26.5467886Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-09-07T08:19:26.5467970Z >>> setup( 2025-09-07T08:19:26.5468068Z ... name='xpu_extension', 2025-09-07T08:19:26.5468170Z ... ext_modules=[ 2025-09-07T08:19:26.5468263Z ... SyclExtension( 2025-09-07T08:19:26.5468385Z ... name='xpu_extension', 2025-09-07T08:19:26.5468549Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-09-07T08:19:26.5468728Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-09-07T08:19:26.5468821Z ... ], 2025-09-07T08:19:26.5468909Z ... cmdclass={ 2025-09-07T08:19:26.5469032Z ... 'build_ext': BuildExtension 2025-09-07T08:19:26.5469138Z ... }) 2025-09-07T08:19:26.5469214Z 2025-09-07T08:19:26.5469522Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T08:19:26.5469770Z building process of the extension. If down the road a new card is installed the 2025-09-07T08:19:26.5470026Z extension may need to be recompiled. You can override the default behavior using 2025-09-07T08:19:26.5470328Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-09-07T08:19:26.5470412Z to support: 2025-09-07T08:19:26.5470501Z 2025-09-07T08:19:26.5470694Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-09-07T08:19:26.5470785Z 2025-09-07T08:19:26.5471080Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T08:19:26.5471395Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T08:19:26.5471488Z 2025-09-07T08:19:26.5471629Z Note: Ninja is required to build SyclExtension. 2025-09-07T08:19:26.5471718Z 2025-09-07T08:19:26.5471968Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5472044Z 2025-09-07T08:19:26.5472148Z warnings.warn(msg) 2025-09-07T08:19:26.5472222Z 2025-09-07T08:19:26.5472432Z --- Parse Warning: 135 / 146 --- 2025-09-07T08:19:26.5473442Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1597. 2025-09-07T08:19:26.5473709Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5473802Z 2025-09-07T08:19:26.5473945Z Load a PyTorch C++ extension just-in-time (JIT). 2025-09-07T08:19:26.5474034Z 2025-09-07T08:19:26.5474240Z To load an extension, a Ninja build file is emitted, which is used to 2025-09-07T08:19:26.5474442Z compile the given sources into a dynamic library. This library is 2025-09-07T08:19:26.5474674Z subsequently loaded into the current Python process as a module and 2025-09-07T08:19:26.5474800Z returned from this function, ready for use. 2025-09-07T08:19:26.5474893Z 2025-09-07T08:19:26.5475095Z By default, the directory to which the build file is emitted and the 2025-09-07T08:19:26.5475327Z resulting library compiled to is ``/torch_extensions/``, where 2025-09-07T08:19:26.5475541Z ```` is the temporary folder on the current platform and ```` 2025-09-07T08:19:26.5475818Z the name of the extension. This location can be overridden in two ways. 2025-09-07T08:19:26.5476038Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-09-07T08:19:26.5476291Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-09-07T08:19:26.5476509Z into subfolders of this directory. Second, if the ``build_directory`` 2025-09-07T08:19:26.5476751Z argument to this function is supplied, it overrides the entire path, i.e. 2025-09-07T08:19:26.5476911Z the library will be compiled into that folder directly. 2025-09-07T08:19:26.5477002Z 2025-09-07T08:19:26.5477210Z To compile the sources, the default system compiler (``c++``) is used, 2025-09-07T08:19:26.5477453Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-09-07T08:19:26.5477690Z additional arguments to the compilation process, ``extra_cflags`` or 2025-09-07T08:19:26.5477906Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-09-07T08:19:26.5478129Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-09-07T08:19:26.5478284Z ``extra_cflags`` to pass further include directories. 2025-09-07T08:19:26.5478362Z 2025-09-07T08:19:26.5478609Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-09-07T08:19:26.5478832Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-09-07T08:19:26.5479087Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-09-07T08:19:26.5479298Z passing the CUDA lib64 directory as a library directory, and linking 2025-09-07T08:19:26.5479445Z ``cudart``. You can pass additional flags to nvcc via 2025-09-07T08:19:26.5479664Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-09-07T08:19:26.5479895Z heuristics for finding the CUDA install directory are used, which usually 2025-09-07T08:19:26.5480113Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-09-07T08:19:26.5480205Z safest option. 2025-09-07T08:19:26.5480283Z 2025-09-07T08:19:26.5480528Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-09-07T08:19:26.5480758Z files (``.sycl``) along with other sources. Such files will be detected 2025-09-07T08:19:26.5480985Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-09-07T08:19:26.5481192Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-09-07T08:19:26.5481381Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-09-07T08:19:26.5481598Z SYCL compiler is expected to be found via system PATH environment 2025-09-07T08:19:26.5481678Z variable. 2025-09-07T08:19:26.5481765Z 2025-09-07T08:19:26.5481847Z Args: 2025-09-07T08:19:26.5482051Z name: The name of the extension to build. This MUST be the same as the 2025-09-07T08:19:26.5482168Z name of the pybind11 module! 2025-09-07T08:19:26.5482364Z sources: A list of relative or absolute paths to C++ source files. 2025-09-07T08:19:26.5482598Z extra_cflags: optional list of compiler flags to forward to the build. 2025-09-07T08:19:26.5482817Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-09-07T08:19:26.5482922Z when building CUDA sources. 2025-09-07T08:19:26.5483154Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-09-07T08:19:26.5483273Z compiler when building SYCL sources. 2025-09-07T08:19:26.5483500Z extra_ldflags: optional list of linker flags to forward to the build. 2025-09-07T08:19:26.5483718Z extra_include_paths: optional list of include directories to forward 2025-09-07T08:19:26.5483810Z to the build. 2025-09-07T08:19:26.5484001Z build_directory: optional path to use as build workspace. 2025-09-07T08:19:26.5484271Z verbose: If ``True``, turns on verbose logging of load steps. 2025-09-07T08:19:26.5484538Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T08:19:26.5484765Z the build. If set to ``None`` (default), this value is 2025-09-07T08:19:26.5484983Z automatically determined based on the existence of ``.cu`` or 2025-09-07T08:19:26.5485153Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-09-07T08:19:26.5485260Z and libraries to be included. 2025-09-07T08:19:26.5485494Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T08:19:26.5485650Z the build. If set to ``None`` (default), this value is 2025-09-07T08:19:26.5485873Z automatically determined based on the existence of ``.sycl`` in 2025-09-07T08:19:26.5486035Z ``sources``. Set it to `True`` to force SYCL headers and 2025-09-07T08:19:26.5486141Z libraries to be included. 2025-09-07T08:19:26.5486363Z is_python_module: If ``True`` (default), imports the produced shared 2025-09-07T08:19:26.5486552Z library as a Python module. If ``False``, behavior depends on 2025-09-07T08:19:26.5486665Z ``is_standalone``. 2025-09-07T08:19:26.5486872Z is_standalone: If ``False`` (default) loads the constructed extension 2025-09-07T08:19:26.5487094Z into the process as a plain dynamic library. If ``True``, build a 2025-09-07T08:19:26.5487212Z standalone executable. 2025-09-07T08:19:26.5487292Z 2025-09-07T08:19:26.5487393Z Returns: 2025-09-07T08:19:26.5487507Z If ``is_python_module`` is ``True``: 2025-09-07T08:19:26.5487681Z Returns the loaded PyTorch extension as a Python module. 2025-09-07T08:19:26.5487778Z 2025-09-07T08:19:26.5487983Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-09-07T08:19:26.5488209Z Returns nothing. (The shared library is loaded into the process as 2025-09-07T08:19:26.5488300Z a side effect.) 2025-09-07T08:19:26.5488379Z 2025-09-07T08:19:26.5488503Z If ``is_standalone`` is ``True``. 2025-09-07T08:19:26.5488704Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-09-07T08:19:26.5488912Z added to the PATH environment variable as a side effect.) 2025-09-07T08:19:26.5488995Z 2025-09-07T08:19:26.5489080Z Example: 2025-09-07T08:19:26.5489191Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5489327Z >>> from torch.utils.cpp_extension import load 2025-09-07T08:19:26.5489421Z >>> module = load( 2025-09-07T08:19:26.5489527Z ... name='extension', 2025-09-07T08:19:26.5489686Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T08:19:26.5489798Z ... extra_cflags=['-O2'], 2025-09-07T08:19:26.5489887Z ... verbose=True) 2025-09-07T08:19:26.5489964Z 2025-09-07T08:19:26.5490230Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5490311Z 2025-09-07T08:19:26.5490416Z warnings.warn(msg) 2025-09-07T08:19:26.5490495Z 2025-09-07T08:19:26.5490707Z --- Parse Warning: 136 / 146 --- 2025-09-07T08:19:26.5491613Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1885. 2025-09-07T08:19:26.5491876Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5491970Z 2025-09-07T08:19:26.5492177Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-09-07T08:19:26.5492256Z 2025-09-07T08:19:26.5492504Z This function behaves exactly like :func:`load`, but takes its sources as 2025-09-07T08:19:26.5492733Z strings rather than filenames. These strings are stored to files in the 2025-09-07T08:19:26.5492981Z build directory, after which the behavior of :func:`load_inline` is 2025-09-07T08:19:26.5493085Z identical to :func:`load`. 2025-09-07T08:19:26.5493186Z 2025-09-07T08:19:26.5493281Z See `the 2025-09-07T08:19:26.5493609Z tests `_ 2025-09-07T08:19:26.5493745Z for good examples of using this function. 2025-09-07T08:19:26.5493824Z 2025-09-07T08:19:26.5494056Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-09-07T08:19:26.5494306Z the necessary header includes, as well as the (pybind11) binding code. More 2025-09-07T08:19:26.5494543Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-09-07T08:19:26.5494741Z single ``.cpp`` file. This file is then prepended with ``#include 2025-09-07T08:19:26.5494839Z `` 2025-09-07T08:19:26.5494917Z 2025-09-07T08:19:26.5495154Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-09-07T08:19:26.5495388Z automatically generated for each function specified. ``functions`` can 2025-09-07T08:19:26.5495621Z either be a list of function names, or a dictionary mapping from function 2025-09-07T08:19:26.5495840Z names to docstrings. If a list is given, the name of each function is used 2025-09-07T08:19:26.5495969Z as its docstring. 2025-09-07T08:19:26.5496058Z 2025-09-07T08:19:26.5496271Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-09-07T08:19:26.5496454Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-09-07T08:19:26.5496661Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-09-07T08:19:26.5496882Z separately, but ultimately linked into a single library. Note that no 2025-09-07T08:19:26.5497121Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-09-07T08:19:26.5497333Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-09-07T08:19:26.5497555Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-09-07T08:19:26.5497667Z include its name in ``functions``). 2025-09-07T08:19:26.5497744Z 2025-09-07T08:19:26.5497998Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-09-07T08:19:26.5498215Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-09-07T08:19:26.5498422Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-09-07T08:19:26.5498636Z linked into a single library. Note that no bindings are generated for 2025-09-07T08:19:26.5498848Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-09-07T08:19:26.5499067Z create a C++ function that calls it, and either declare or define this 2025-09-07T08:19:26.5499251Z C++ function in one of the ``cpp_sources`` (and include its name 2025-09-07T08:19:26.5499354Z in ``functions``). 2025-09-07T08:19:26.5499433Z 2025-09-07T08:19:26.5499514Z 2025-09-07T08:19:26.5499604Z 2025-09-07T08:19:26.5499785Z See :func:`load` for a description of arguments omitted below. 2025-09-07T08:19:26.5499875Z 2025-09-07T08:19:26.5499955Z Args: 2025-09-07T08:19:26.5500171Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-09-07T08:19:26.5500406Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-09-07T08:19:26.5500627Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-09-07T08:19:26.5500840Z functions: A list of function names for which to generate function 2025-09-07T08:19:26.5501049Z bindings. If a dictionary is given, it should map function names to 2025-09-07T08:19:26.5501227Z docstrings (which are otherwise just the function names). 2025-09-07T08:19:26.5501457Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T08:19:26.5501635Z the build. If set to ``None`` (default), this value is 2025-09-07T08:19:26.5501851Z automatically determined based on whether ``cuda_sources`` is 2025-09-07T08:19:26.5502026Z provided. Set it to ``True`` to force CUDA headers 2025-09-07T08:19:26.5502136Z and libraries to be included. 2025-09-07T08:19:26.5502368Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T08:19:26.5502522Z the build. If set to ``None`` (default), this value is 2025-09-07T08:19:26.5502736Z automatically determined based on whether ``sycl_sources`` is 2025-09-07T08:19:26.5502886Z provided. Set it to ``True`` to force SYCL headers 2025-09-07T08:19:26.5502993Z and libraries to be included. 2025-09-07T08:19:26.5503209Z with_pytorch_error_handling: Determines whether pytorch error and 2025-09-07T08:19:26.5503406Z warning macros are handled by pytorch instead of pybind. To do 2025-09-07T08:19:26.5503633Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-09-07T08:19:26.5503830Z function. This redirection might cause issues in obscure cases 2025-09-07T08:19:26.5504010Z of cpp. This flag should be set to ``False`` when this redirect 2025-09-07T08:19:26.5504142Z causes issues. 2025-09-07T08:19:26.5504393Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-09-07T08:19:26.5504628Z ``#include `` and ``#include `` lines. 2025-09-07T08:19:26.5504785Z Use this option to improve cold start times when you 2025-09-07T08:19:26.5505054Z already include the necessary headers in your source code. Default: ``False``. 2025-09-07T08:19:26.5505132Z 2025-09-07T08:19:26.5505213Z Example: 2025-09-07T08:19:26.5505369Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T08:19:26.5505527Z >>> from torch.utils.cpp_extension import load_inline 2025-09-07T08:19:26.5505618Z >>> source = """ 2025-09-07T08:19:26.5505777Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-09-07T08:19:26.5505876Z return x.sin() + y.sin(); 2025-09-07T08:19:26.5505999Z } 2025-09-07T08:19:26.5506080Z """ 2025-09-07T08:19:26.5506217Z >>> module = load_inline(name='inline_extension', 2025-09-07T08:19:26.5506345Z ... cpp_sources=[source], 2025-09-07T08:19:26.5506460Z ... functions=['sin_add']) 2025-09-07T08:19:26.5506550Z 2025-09-07T08:19:26.5506632Z .. note:: 2025-09-07T08:19:26.5506868Z Since load_inline will just-in-time compile the source code, please ensure 2025-09-07T08:19:26.5507104Z that you have the right toolchains installed in the runtime. For example, 2025-09-07T08:19:26.5507319Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-09-07T08:19:26.5507574Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-09-07T08:19:26.5507817Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-09-07T08:19:26.5508054Z are not included when you install torch and must be additionally installed. 2025-09-07T08:19:26.5508147Z 2025-09-07T08:19:26.5508397Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-09-07T08:19:26.5508624Z the extension. This may use up too many resources on some systems. One 2025-09-07T08:19:26.5508841Z can control the number of workers by setting the `MAX_JOBS` environment 2025-09-07T08:19:26.5508955Z variable to a non-negative number. 2025-09-07T08:19:26.5509044Z 2025-09-07T08:19:26.5509295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5509385Z 2025-09-07T08:19:26.5509478Z warnings.warn(msg) 2025-09-07T08:19:26.5509557Z 2025-09-07T08:19:26.5509797Z --- Parse Warning: 137 / 146 --- 2025-09-07T08:19:26.5510803Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61. 2025-09-07T08:19:26.5511079Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5511160Z 2025-09-07T08:19:26.5511463Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-09-07T08:19:26.5511543Z 2025-09-07T08:19:26.5511835Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-09-07T08:19:26.5512096Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-09-07T08:19:26.5512326Z server like load. It can emulate multiple calling threads to a single module 2025-09-07T08:19:26.5512582Z provided. In the future we plan to enhance this component to support inter and 2025-09-07T08:19:26.5512827Z intra-op parallelism as well as multiple models running in a single process. 2025-09-07T08:19:26.5512907Z 2025-09-07T08:19:26.5513172Z Please note that even though nn.Module is supported, it might incur an overhead 2025-09-07T08:19:26.5513415Z from the need to hold GIL every time we execute Python code or pass around 2025-09-07T08:19:26.5513663Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-09-07T08:19:26.5513898Z model for inference deployment it is better to switch to using it in this 2025-09-07T08:19:26.5513983Z benchmark. 2025-09-07T08:19:26.5514070Z 2025-09-07T08:19:26.5514155Z Example:: 2025-09-07T08:19:26.5514243Z 2025-09-07T08:19:26.5514360Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T08:19:26.5514500Z >>> from torch.utils import ThroughputBenchmark 2025-09-07T08:19:26.5514638Z >>> bench = ThroughputBenchmark(my_module) 2025-09-07T08:19:26.5514798Z >>> # Pre-populate benchmark's data set with the inputs 2025-09-07T08:19:26.5514913Z >>> for input in inputs: 2025-09-07T08:19:26.5515161Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-09-07T08:19:26.5515293Z ... bench.add_input(input[0], x2=input[1]) 2025-09-07T08:19:26.5515500Z >>> # Inputs supplied above are randomly used during the execution 2025-09-07T08:19:26.5515604Z >>> stats = bench.benchmark( 2025-09-07T08:19:26.5515719Z ... num_calling_threads=4, 2025-09-07T08:19:26.5515820Z ... num_warmup_iters = 100, 2025-09-07T08:19:26.5515917Z ... num_iters = 1000, 2025-09-07T08:19:26.5516013Z ... ) 2025-09-07T08:19:26.5516192Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-09-07T08:19:26.5516366Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-09-07T08:19:26.5516461Z 2025-09-07T08:19:26.5516715Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5516811Z 2025-09-07T08:19:26.5516907Z warnings.warn(msg) 2025-09-07T08:19:26.5516985Z 2025-09-07T08:19:26.5517197Z --- Parse Warning: 138 / 146 --- 2025-09-07T08:19:26.5518149Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=18. 2025-09-07T08:19:26.5518424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5518631Z Sampler that restricts data loading to a subset of the dataset. 2025-09-07T08:19:26.5518724Z 2025-09-07T08:19:26.5518857Z It is especially useful in conjunction with 2025-09-07T08:19:26.5519103Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-09-07T08:19:26.5519408Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-09-07T08:19:26.5519661Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-09-07T08:19:26.5519805Z original dataset that is exclusive to it. 2025-09-07T08:19:26.5519889Z 2025-09-07T08:19:26.5519975Z .. note:: 2025-09-07T08:19:26.5520226Z Dataset is assumed to be of constant size and that any instance of it always 2025-09-07T08:19:26.5520366Z returns the same elements in the same order. 2025-09-07T08:19:26.5520463Z 2025-09-07T08:19:26.5520549Z Args: 2025-09-07T08:19:26.5520666Z dataset: Dataset used for sampling. 2025-09-07T08:19:26.5520897Z num_replicas (int, optional): Number of processes participating in 2025-09-07T08:19:26.5521147Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-09-07T08:19:26.5521262Z current distributed group. 2025-09-07T08:19:26.5521515Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-09-07T08:19:26.5521717Z By default, :attr:`rank` is retrieved from the current distributed 2025-09-07T08:19:26.5521820Z group. 2025-09-07T08:19:26.5522069Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-09-07T08:19:26.5522170Z indices. 2025-09-07T08:19:26.5522359Z seed (int, optional): random seed used to shuffle the sampler if 2025-09-07T08:19:26.5522555Z :attr:`shuffle=True`. This number should be identical across all 2025-09-07T08:19:26.5522733Z processes in the distributed group. Default: ``0``. 2025-09-07T08:19:26.5522943Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-09-07T08:19:26.5523150Z tail of the data to make it evenly divisible across the number of 2025-09-07T08:19:26.5523346Z replicas. If ``False``, the sampler will add extra indices to make 2025-09-07T08:19:26.5523555Z the data evenly divisible across the replicas. Default: ``False``. 2025-09-07T08:19:26.5523650Z 2025-09-07T08:19:26.5523794Z .. warning:: 2025-09-07T08:19:26.5523996Z In distributed mode, calling the :meth:`set_epoch` method at 2025-09-07T08:19:26.5524335Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-09-07T08:19:26.5524593Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-09-07T08:19:26.5524728Z the same ordering will be always used. 2025-09-07T08:19:26.5524805Z 2025-09-07T08:19:26.5524905Z Example:: 2025-09-07T08:19:26.5524981Z 2025-09-07T08:19:26.5525077Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5525308Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-09-07T08:19:26.5525480Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-09-07T08:19:26.5525610Z ... sampler=sampler) 2025-09-07T08:19:26.5525743Z >>> for epoch in range(start_epoch, n_epochs): 2025-09-07T08:19:26.5525847Z ... if is_distributed: 2025-09-07T08:19:26.5525974Z ... sampler.set_epoch(epoch) 2025-09-07T08:19:26.5526067Z ... train(loader) 2025-09-07T08:19:26.5526161Z 2025-09-07T08:19:26.5526417Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5526494Z 2025-09-07T08:19:26.5526602Z warnings.warn(msg) 2025-09-07T08:19:26.5526680Z 2025-09-07T08:19:26.5526881Z --- Parse Warning: 139 / 146 --- 2025-09-07T08:19:26.5527871Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=WeightedRandomSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=227. 2025-09-07T08:19:26.5528133Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5528440Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-09-07T08:19:26.5528519Z 2025-09-07T08:19:26.5528615Z Args: 2025-09-07T08:19:26.5528857Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-09-07T08:19:26.5528995Z num_samples (int): number of samples to draw 2025-09-07T08:19:26.5529217Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-09-07T08:19:26.5529421Z If not, they are drawn without replacement, which means that when a 2025-09-07T08:19:26.5529650Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-09-07T08:19:26.5529807Z generator (Generator): Generator used in sampling. 2025-09-07T08:19:26.5529885Z 2025-09-07T08:19:26.5529982Z Example: 2025-09-07T08:19:26.5530119Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T08:19:26.5530215Z >>> list( 2025-09-07T08:19:26.5530325Z ... WeightedRandomSampler( 2025-09-07T08:19:26.5530466Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-09-07T08:19:26.5530587Z ... ) 2025-09-07T08:19:26.5530670Z ... ) 2025-09-07T08:19:26.5530754Z [4, 4, 1, 4, 5] 2025-09-07T08:19:26.5530847Z >>> list( 2025-09-07T08:19:26.5530959Z ... WeightedRandomSampler( 2025-09-07T08:19:26.5531109Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-09-07T08:19:26.5531191Z ... ) 2025-09-07T08:19:26.5531271Z ... ) 2025-09-07T08:19:26.5531368Z [0, 1, 4, 3, 2] 2025-09-07T08:19:26.5531449Z 2025-09-07T08:19:26.5531713Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5531793Z 2025-09-07T08:19:26.5531888Z warnings.warn(msg) 2025-09-07T08:19:26.5531980Z 2025-09-07T08:19:26.5532166Z --- Parse Warning: 140 / 146 --- 2025-09-07T08:19:26.5533098Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BatchSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=300. 2025-09-07T08:19:26.5533363Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5533534Z Wraps another sampler to yield a mini-batch of indices. 2025-09-07T08:19:26.5533625Z 2025-09-07T08:19:26.5533706Z Args: 2025-09-07T08:19:26.5533944Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-09-07T08:19:26.5534064Z batch_size (int): Size of mini-batch. 2025-09-07T08:19:26.5534267Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-09-07T08:19:26.5534410Z its size would be less than ``batch_size`` 2025-09-07T08:19:26.5534488Z 2025-09-07T08:19:26.5534583Z Example: 2025-09-07T08:19:26.5534665Z >>> list( 2025-09-07T08:19:26.5534763Z ... BatchSampler( 2025-09-07T08:19:26.5534976Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-09-07T08:19:26.5535059Z ... ) 2025-09-07T08:19:26.5535149Z ... ) 2025-09-07T08:19:26.5535251Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-09-07T08:19:26.5535332Z >>> list( 2025-09-07T08:19:26.5535600Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-09-07T08:19:26.5535682Z ... ) 2025-09-07T08:19:26.5535790Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-09-07T08:19:26.5535868Z 2025-09-07T08:19:26.5536143Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5536235Z 2025-09-07T08:19:26.5536354Z warnings.warn(msg) 2025-09-07T08:19:26.5536433Z 2025-09-07T08:19:26.5536628Z --- Parse Warning: 141 / 146 --- 2025-09-07T08:19:26.5537590Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=IterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py line=56. 2025-09-07T08:19:26.5537867Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5537945Z 2025-09-07T08:19:26.5538048Z Iterable-style DataPipe. 2025-09-07T08:19:26.5538138Z 2025-09-07T08:19:26.5538390Z All DataPipes that represent an iterable of data samples should subclass this. 2025-09-07T08:19:26.5538653Z This style of DataPipes is particularly useful when data come from a stream, or 2025-09-07T08:19:26.5539012Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-09-07T08:19:26.5539317Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-09-07T08:19:26.5539394Z 2025-09-07T08:19:26.5539622Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-09-07T08:19:26.5540007Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-09-07T08:19:26.5540372Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-09-07T08:19:26.5540659Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-09-07T08:19:26.5540861Z and various state variables within the custom ``IterDataPipe``. 2025-09-07T08:19:26.5540939Z 2025-09-07T08:19:26.5541030Z Note: 2025-09-07T08:19:26.5541237Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-09-07T08:19:26.5541599Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-09-07T08:19:26.5542003Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-09-07T08:19:26.5542273Z The code example below presents details on how this constraint looks in practice. 2025-09-07T08:19:26.5542650Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-09-07T08:19:26.5542727Z 2025-09-07T08:19:26.5543021Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-09-07T08:19:26.5543381Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-09-07T08:19:26.5543704Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-09-07T08:19:26.5543808Z operations in succession. 2025-09-07T08:19:26.5543886Z 2025-09-07T08:19:26.5544037Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-09-07T08:19:26.5544175Z https://github.com/pytorch/data/issues/45 2025-09-07T08:19:26.5544251Z 2025-09-07T08:19:26.5544348Z Note: 2025-09-07T08:19:26.5544577Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-09-07T08:19:26.5544854Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-09-07T08:19:26.5545073Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-09-07T08:19:26.5545308Z different copy of the DataPipe object, so it is often desired to configure 2025-09-07T08:19:26.5545558Z each copy independently to avoid having duplicate data returned from the 2025-09-07T08:19:26.5545794Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-09-07T08:19:26.5546095Z process, returns information about the worker. It can be used in either the 2025-09-07T08:19:26.5546343Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-09-07T08:19:26.5546566Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-09-07T08:19:26.5546646Z 2025-09-07T08:19:26.5546729Z Examples: 2025-09-07T08:19:26.5546832Z General Usage: 2025-09-07T08:19:26.5546929Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5547135Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T08:19:26.5547266Z >>> dp = IterableWrapper(range(10)) 2025-09-07T08:19:26.5547452Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-09-07T08:19:26.5547562Z >>> map_dp_2 = dp.map( 2025-09-07T08:19:26.5547657Z ... lambda x: x + 1 2025-09-07T08:19:26.5547789Z ... ) # Using functional form (recommended) 2025-09-07T08:19:26.5547893Z >>> list(map_dp_1) 2025-09-07T08:19:26.5547990Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T08:19:26.5548095Z >>> list(map_dp_2) 2025-09-07T08:19:26.5548188Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T08:19:26.5548335Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-09-07T08:19:26.5548472Z >>> list(filter_dp) 2025-09-07T08:19:26.5548558Z [2, 4, 6, 8, 10] 2025-09-07T08:19:26.5548682Z Single Iterator Constraint Example: 2025-09-07T08:19:26.5548888Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T08:19:26.5549017Z >>> source_dp = IterableWrapper(range(10)) 2025-09-07T08:19:26.5549130Z >>> it1 = iter(source_dp) 2025-09-07T08:19:26.5549219Z >>> list(it1) 2025-09-07T08:19:26.5549322Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-09-07T08:19:26.5549419Z >>> it1 = iter(source_dp) 2025-09-07T08:19:26.5549511Z >>> it2 = iter( 2025-09-07T08:19:26.5549614Z ... source_dp 2025-09-07T08:19:26.5549769Z ... ) # The creation of a new iterator invalidates `it1` 2025-09-07T08:19:26.5549870Z >>> next(it2) 2025-09-07T08:19:26.5549949Z 0 2025-09-07T08:19:26.5550159Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-09-07T08:19:26.5550252Z 2025-09-07T08:19:26.5550501Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5550587Z 2025-09-07T08:19:26.5550684Z warnings.warn(msg) 2025-09-07T08:19:26.5550763Z 2025-09-07T08:19:26.5550968Z --- Parse Warning: 142 / 146 --- 2025-09-07T08:19:26.5552034Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DemultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=375. 2025-09-07T08:19:26.5552310Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5552391Z 2025-09-07T08:19:26.5552846Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-09-07T08:19:26.5552938Z 2025-09-07T08:19:26.5553123Z A list of the child DataPipes is returned from this operation. 2025-09-07T08:19:26.5553213Z 2025-09-07T08:19:26.5553293Z Args: 2025-09-07T08:19:26.5553427Z datapipe: Iterable DataPipe being filtered 2025-09-07T08:19:26.5553625Z num_instances: number of instances of the DataPipe to create 2025-09-07T08:19:26.5553991Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-09-07T08:19:26.5554323Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-09-07T08:19:26.5554670Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-09-07T08:19:26.5554847Z DataPipes while waiting for their values to be yielded. 2025-09-07T08:19:26.5555046Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-09-07T08:19:26.5555127Z 2025-09-07T08:19:26.5555223Z Examples: 2025-09-07T08:19:26.5555345Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T08:19:26.5555517Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T08:19:26.5555618Z >>> def odd_or_even(n): 2025-09-07T08:19:26.5555707Z ... return n % 2 2025-09-07T08:19:26.5555835Z >>> source_dp = IterableWrapper(range(5)) 2025-09-07T08:19:26.5556042Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-09-07T08:19:26.5556129Z >>> list(dp1) 2025-09-07T08:19:26.5556216Z [0, 2, 4] 2025-09-07T08:19:26.5556299Z >>> list(dp2) 2025-09-07T08:19:26.5556387Z [1, 3] 2025-09-07T08:19:26.5556620Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-09-07T08:19:26.5556723Z >>> def odd_or_even_no_zero(n): 2025-09-07T08:19:26.5556843Z ... return n % 2 if n != 0 else None 2025-09-07T08:19:26.5556946Z >>> dp1, dp2 = source_dp.demux( 2025-09-07T08:19:26.5557191Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-09-07T08:19:26.5557270Z ... ) 2025-09-07T08:19:26.5557352Z >>> list(dp1) 2025-09-07T08:19:26.5557444Z [2, 4] 2025-09-07T08:19:26.5557526Z >>> list(dp2) 2025-09-07T08:19:26.5557604Z [1, 3] 2025-09-07T08:19:26.5557689Z 2025-09-07T08:19:26.5557939Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5558026Z 2025-09-07T08:19:26.5558120Z warnings.warn(msg) 2025-09-07T08:19:26.5558198Z 2025-09-07T08:19:26.5558393Z --- Parse Warning: 143 / 146 --- 2025-09-07T08:19:26.5559472Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=594. 2025-09-07T08:19:26.5559747Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5559829Z 2025-09-07T08:19:26.5560134Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-09-07T08:19:26.5560218Z 2025-09-07T08:19:26.5560561Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-09-07T08:19:26.5560758Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-09-07T08:19:26.5560833Z 2025-09-07T08:19:26.5560914Z Args: 2025-09-07T08:19:26.5561326Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-09-07T08:19:26.5561404Z 2025-09-07T08:19:26.5561496Z Example: 2025-09-07T08:19:26.5561619Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T08:19:26.5561796Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T08:19:26.5561898Z >>> dp1, dp2, dp3 = ( 2025-09-07T08:19:26.5562009Z ... IterableWrapper(range(3)), 2025-09-07T08:19:26.5562133Z ... IterableWrapper(range(10, 15)), 2025-09-07T08:19:26.5562246Z ... IterableWrapper(range(20, 25)), 2025-09-07T08:19:26.5562326Z ... ) 2025-09-07T08:19:26.5562436Z >>> list(dp1.mux(dp2, dp3)) 2025-09-07T08:19:26.5562528Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-09-07T08:19:26.5562614Z 2025-09-07T08:19:26.5562864Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5562944Z 2025-09-07T08:19:26.5563043Z warnings.warn(msg) 2025-09-07T08:19:26.5563120Z 2025-09-07T08:19:26.5563342Z --- Parse Warning: 144 / 146 --- 2025-09-07T08:19:26.5564482Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZipperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=665. 2025-09-07T08:19:26.5564750Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5564841Z 2025-09-07T08:19:26.5565151Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-09-07T08:19:26.5565239Z 2025-09-07T08:19:26.5565468Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-09-07T08:19:26.5565549Z 2025-09-07T08:19:26.5565641Z Args: 2025-09-07T08:19:26.5565790Z *datapipes: Iterable DataPipes being aggregated 2025-09-07T08:19:26.5565878Z 2025-09-07T08:19:26.5565960Z Example: 2025-09-07T08:19:26.5566084Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T08:19:26.5566265Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T08:19:26.5566361Z >>> dp1, dp2, dp3 = ( 2025-09-07T08:19:26.5566470Z ... IterableWrapper(range(5)), 2025-09-07T08:19:26.5566628Z ... IterableWrapper(range(10, 15)), 2025-09-07T08:19:26.5566742Z ... IterableWrapper(range(20, 25)), 2025-09-07T08:19:26.5566828Z ... ) 2025-09-07T08:19:26.5566929Z >>> list(dp1.zip(dp2, dp3)) 2025-09-07T08:19:26.5567060Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-09-07T08:19:26.5567147Z 2025-09-07T08:19:26.5567397Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5567483Z 2025-09-07T08:19:26.5567582Z warnings.warn(msg) 2025-09-07T08:19:26.5567656Z 2025-09-07T08:19:26.5567854Z --- Parse Warning: 145 / 146 --- 2025-09-07T08:19:26.5568896Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FileOpenerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py line=18. 2025-09-07T08:19:26.5569197Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5569277Z 2025-09-07T08:19:26.5569649Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-09-07T08:19:26.5569731Z 2025-09-07T08:19:26.5569810Z Args: 2025-09-07T08:19:26.5569979Z datapipe: Iterable datapipe that provides pathnames 2025-09-07T08:19:26.5570149Z mode: An optional string that specifies the mode in which 2025-09-07T08:19:26.5570358Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-09-07T08:19:26.5570522Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-09-07T08:19:26.5570721Z encoding: An optional string that specifies the encoding of the 2025-09-07T08:19:26.5570991Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-09-07T08:19:26.5571115Z length: Nominal length of the datapipe 2025-09-07T08:19:26.5571194Z 2025-09-07T08:19:26.5571285Z Note: 2025-09-07T08:19:26.5571553Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-09-07T08:19:26.5571661Z to close them explicitly. 2025-09-07T08:19:26.5571737Z 2025-09-07T08:19:26.5571818Z Example: 2025-09-07T08:19:26.5571922Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5572052Z >>> from torchdata.datapipes.iter import ( 2025-09-07T08:19:26.5572145Z ... FileLister, 2025-09-07T08:19:26.5572233Z ... FileOpener, 2025-09-07T08:19:26.5572324Z ... StreamReader, 2025-09-07T08:19:26.5572414Z ... ) 2025-09-07T08:19:26.5572653Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-09-07T08:19:26.5572759Z >>> dp = FileOpener(dp) 2025-09-07T08:19:26.5572884Z >>> dp = StreamReader(dp) 2025-09-07T08:19:26.5572961Z >>> list(dp) 2025-09-07T08:19:26.5573067Z [('./abc.txt', 'abc')] 2025-09-07T08:19:26.5573145Z 2025-09-07T08:19:26.5573577Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5573666Z 2025-09-07T08:19:26.5573758Z warnings.warn(msg) 2025-09-07T08:19:26.5573846Z 2025-09-07T08:19:26.5574032Z --- Parse Warning: 146 / 146 --- 2025-09-07T08:19:26.5575064Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=GrouperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py line=155. 2025-09-07T08:19:26.5575339Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T08:19:26.5575416Z 2025-09-07T08:19:26.5575829Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-09-07T08:19:26.5575910Z 2025-09-07T08:19:26.5576012Z (functional name: ``groupby``). 2025-09-07T08:19:26.5576162Z 2025-09-07T08:19:26.5576543Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-09-07T08:19:26.5576845Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-09-07T08:19:26.5577150Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-09-07T08:19:26.5577482Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-09-07T08:19:26.5577564Z 2025-09-07T08:19:26.5577940Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-09-07T08:19:26.5578264Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-09-07T08:19:26.5578344Z 2025-09-07T08:19:26.5578432Z Args: 2025-09-07T08:19:26.5578593Z datapipe: Iterable datapipe to be grouped 2025-09-07T08:19:26.5578865Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-09-07T08:19:26.5582767Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-09-07T08:19:26.5582979Z resulting in `(key, [items])` otherwise returning [items] 2025-09-07T08:19:26.5583134Z buffer_size: The size of buffer for ungrouped data 2025-09-07T08:19:26.5583410Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-09-07T08:19:26.5583759Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-09-07T08:19:26.5584125Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-09-07T08:19:26.5584239Z when the buffer is full 2025-09-07T08:19:26.5584320Z 2025-09-07T08:19:26.5584404Z Example: 2025-09-07T08:19:26.5584506Z >>> import os 2025-09-07T08:19:26.5584601Z >>> # xdoctest: +SKIP 2025-09-07T08:19:26.5584785Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T08:19:26.5584883Z >>> def group_fn(file): 2025-09-07T08:19:26.5585022Z ... return os.path.basename(file).split(".")[0] 2025-09-07T08:19:26.5585145Z >>> source_dp = IterableWrapper( 2025-09-07T08:19:26.5585305Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-09-07T08:19:26.5585396Z ... ) 2025-09-07T08:19:26.5585535Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-09-07T08:19:26.5585617Z >>> list(dp0) 2025-09-07T08:19:26.5585873Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-09-07T08:19:26.5586061Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-09-07T08:19:26.5586293Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-09-07T08:19:26.5586382Z >>> list(dp1) 2025-09-07T08:19:26.5586541Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T08:19:26.5586899Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-09-07T08:19:26.5587005Z >>> dp2 = source_dp.groupby( 2025-09-07T08:19:26.5587114Z ... group_key_fn=group_fn, 2025-09-07T08:19:26.5587205Z ... buffer_size=3, 2025-09-07T08:19:26.5587295Z ... group_size=3, 2025-09-07T08:19:26.5587410Z ... guaranteed_group_size=2, 2025-09-07T08:19:26.5587490Z ... ) 2025-09-07T08:19:26.5587572Z >>> list(dp2) 2025-09-07T08:19:26.5587747Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T08:19:26.5587821Z 2025-09-07T08:19:26.5588085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T08:19:26.5588162Z 2025-09-07T08:19:26.5588256Z warnings.warn(msg) 2025-09-07T08:19:26.5588375Z 2025-09-07T08:19:26.5588520Z  2025-09-07T08:19:26.5588706Z === Found 8 run-time warnings === 2025-09-07T08:19:26.5588884Z --- Runtime Warning: 1 / 8 --- 2025-09-07T08:19:26.5589146Z example = 2025-09-07T08:19:26.5590502Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1351: 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:1974.) 2025-09-07T08:19:26.5590609Z return super().refine_names(names) 2025-09-07T08:19:26.5590695Z 2025-09-07T08:19:26.5590868Z --- Runtime Warning: 2 / 8 --- 2025-09-07T08:19:26.5591213Z example = 2025-09-07T08:19:26.5591840Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:282: UserWarning: Warning only once for all operators, other operators may also be overridden. 2025-09-07T08:19:26.5592148Z Overriding a previously registered kernel for the same operator and the same dispatch key 2025-09-07T08:19:26.5592366Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2025-09-07T08:19:26.5592658Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2025-09-07T08:19:26.5592765Z dispatch key: CPU 2025-09-07T08:19:26.5593192Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2025-09-07T08:19:26.5594199Z new kernel: registered at :1 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:225.) 2025-09-07T08:19:26.5594360Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2025-09-07T08:19:26.5594437Z 2025-09-07T08:19:26.5594624Z --- Runtime Warning: 3 / 8 --- 2025-09-07T08:19:26.5594862Z example = 2025-09-07T08:19:26.5596739Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py:117: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:182.) 2025-09-07T08:19:26.5596984Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2025-09-07T08:19:26.5597100Z 2025-09-07T08:19:26.5597276Z --- Runtime Warning: 4 / 8 --- 2025-09-07T08:19:26.5597581Z example = 2025-09-07T08:19:26.5599096Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:278: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2025-09-07T08:19:26.5599259Z new_node = root_const_gm.graph.get_attr(in_node.target) 2025-09-07T08:19:26.5599348Z 2025-09-07T08:19:26.5599523Z --- Runtime Warning: 5 / 8 --- 2025-09-07T08:19:26.5599822Z example = 2025-09-07T08:19:26.5600908Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:392: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2025-09-07T08:19:26.5601028Z warnings.warn( 2025-09-07T08:19:26.5601120Z 2025-09-07T08:19:26.5601293Z --- Runtime Warning: 6 / 8 --- 2025-09-07T08:19:26.5601634Z example = 2025-09-07T08:19:26.5602709Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:392: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2025-09-07T08:19:26.5602817Z warnings.warn( 2025-09-07T08:19:26.5602892Z 2025-09-07T08:19:26.5603067Z --- Runtime Warning: 7 / 8 --- 2025-09-07T08:19:26.5603354Z example = 2025-09-07T08:19:26.5604306Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2025-09-07T08:19:26.5604446Z WeightNorm.apply(module, name, dim) 2025-09-07T08:19:26.5604525Z 2025-09-07T08:19:26.5604701Z --- Runtime Warning: 8 / 8 --- 2025-09-07T08:19:26.5605014Z example = 2025-09-07T08:19:26.5605817Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2025-09-07T08:19:26.5605951Z WeightNorm.apply(module, name, dim) 2025-09-07T08:19:26.5606029Z 2025-09-07T08:19:26.5606328Z === 338 passed, 394 skipped, 154 warnings in 14.23 seconds === 2025-09-07T08:19:26.5606538Z Running test_autoload_disable 1/1 ... [2025-09-07 08:19:26.363174] 2025-09-07T08:19:26.6771186Z Processing /var/lib/jenkins/workspace/test/cpp_extensions 2025-09-07T08:19:29.2291100Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:19:29.2320229Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T08:19:29.2329339Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:19:38.4441087Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ done 2025-09-07T08:19:38.4690267Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp313-cp313-linux_x86_64.whl size=10046718 sha256=869820a3315d49c0e7cc04f18bc21f53bfe5d016d400c4ab819748fa3f56681d 2025-09-07T08:19:38.4691783Z Stored in directory: /tmp/pip-ephem-wheel-cache-r7c2gnt0/wheels/55/0c/23/e830c2c3ad8ea6f122c5bd80ddd259405d4a52266f20ee6037 2025-09-07T08:19:38.4710937Z Successfully built torch_test_cpp_extension 2025-09-07T08:19:38.6272529Z Installing collected packages: torch_test_cpp_extension 2025-09-07T08:19:38.7858615Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T08:19:40.7721454Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:19:40.7723027Z import pkg_resources 2025-09-07T08:19:40.8013675Z 2025-09-07T08:19:40.8014074Z Running tests... 2025-09-07T08:19:40.8014415Z ---------------------------------------------------------------------- 2025-09-07T08:19:41.2500219Z . 2025-09-07T08:19:41.2500613Z ---------------------------------------------------------------------- 2025-09-07T08:19:41.2501012Z Ran 1 test in 0.449s 2025-09-07T08:19:41.2501184Z 2025-09-07T08:19:41.2501266Z OK 2025-09-07T08:19:41.2501388Z 2025-09-07T08:19:41.2501494Z Generating XML reports... 2025-09-07T08:19:41.7368944Z Running test_autoload_enable 1/1 ... [2025-09-07 08:19:41.736537] 2025-09-07T08:19:42.0517746Z Processing /var/lib/jenkins/workspace/test/cpp_extensions 2025-09-07T08:19:44.5138226Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:19:44.5167462Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T08:19:44.5176862Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:19:53.5789821Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ done 2025-09-07T08:19:53.6041786Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp313-cp313-linux_x86_64.whl size=10046718 sha256=46f12e2e2009a8fcd2c9400c47d50cb153887b48e8d18faa7f6e27f2baf07a5d 2025-09-07T08:19:53.6044099Z Stored in directory: /tmp/pip-ephem-wheel-cache-hbaxb7xo/wheels/55/0c/23/e830c2c3ad8ea6f122c5bd80ddd259405d4a52266f20ee6037 2025-09-07T08:19:53.6063470Z Successfully built torch_test_cpp_extension 2025-09-07T08:19:53.7618583Z Installing collected packages: torch_test_cpp_extension 2025-09-07T08:19:53.9279417Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T08:19:55.9253298Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:19:55.9256240Z import pkg_resources 2025-09-07T08:19:55.9508291Z 2025-09-07T08:19:55.9508703Z Running tests... 2025-09-07T08:19:55.9509219Z ---------------------------------------------------------------------- 2025-09-07T08:19:56.3999572Z . 2025-09-07T08:19:56.4000337Z ---------------------------------------------------------------------- 2025-09-07T08:19:56.4001084Z Ran 1 test in 0.449s 2025-09-07T08:19:56.4001381Z 2025-09-07T08:19:56.4001526Z OK 2025-09-07T08:19:56.4001718Z 2025-09-07T08:19:56.4001932Z Generating XML reports... 2025-09-07T08:19:56.8989104Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-09-07 08:19:56.898584] 2025-09-07T08:19:57.2432193Z Processing /var/lib/jenkins/workspace/test/cpp_extensions 2025-09-07T08:19:59.7796963Z Preparing metadata (setup.py) ... [?25l- \ done 2025-09-07T08:19:59.7825991Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T08:19:59.7834950Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:20:06.6491334Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ | done 2025-09-07T08:20:06.6582741Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp313-cp313-linux_x86_64.whl size=3235172 sha256=c1a192a631c47ba0c3f529649756989747fbfb78e04ec2740fc4aef2498e2f41 2025-09-07T08:20:06.6584602Z Stored in directory: /tmp/pip-ephem-wheel-cache-9xy71uz2/wheels/55/0c/23/e830c2c3ad8ea6f122c5bd80ddd259405d4a52266f20ee6037 2025-09-07T08:20:06.6604562Z Successfully built torch_test_cpp_extension 2025-09-07T08:20:06.8149516Z Installing collected packages: torch_test_cpp_extension 2025-09-07T08:20:06.8870307Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T08:20:07.2492393Z Processing /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test 2025-09-07T08:20:08.7005782Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:20:08.7034760Z [?25hBuilding wheels for collected packages: no_python_abi_suffix_test 2025-09-07T08:20:08.7043855Z  DEPRECATION: Building 'no_python_abi_suffix_test' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'no_python_abi_suffix_test'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:20:10.4462000Z  Building wheel for no_python_abi_suffix_test (setup.py) ... [?25l- \ | done 2025-09-07T08:20:10.4468239Z [?25h Created wheel for no_python_abi_suffix_test: filename=no_python_abi_suffix_test-0.0.0-cp313-cp313-linux_x86_64.whl size=2971 sha256=96586940e4d9946cbbfa24c3b04d769e0e8fb234f3a7bfb78c20c0699a53e6e9 2025-09-07T08:20:10.4470433Z Stored in directory: /tmp/pip-ephem-wheel-cache-fmc9utbt/wheels/c7/be/1e/a693498df0ac11a249ac34fc70a78f5f1b2d84d49e78482cb4 2025-09-07T08:20:10.4489970Z Successfully built no_python_abi_suffix_test 2025-09-07T08:20:10.6042827Z Installing collected packages: no_python_abi_suffix_test 2025-09-07T08:20:10.6141415Z Successfully installed no_python_abi_suffix_test-0.0.0 2025-09-07T08:20:10.6699570Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:20:10.6701330Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:20:10.669871] 2025-09-07T08:20:22.8098355Z 2025-09-07T08:20:22.8099691Z 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_8f0be4eed939cde1_.log 2025-09-07T08:20:22.8107817Z Running 21 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library 2025-09-07T08:20:22.8115393Z 2025-09-07T08:20:22.8115633Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-09-07 08:20:22.810270] 2025-09-07T08:20:23.1202485Z Processing /var/lib/jenkins/workspace/test/cpp_extensions 2025-09-07T08:20:25.5882152Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:20:25.5911454Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T08:20:25.5921051Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:20:34.6200391Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ done 2025-09-07T08:20:34.6450313Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp313-cp313-linux_x86_64.whl size=10046718 sha256=35218d299b9d54b8314bca93030debd4c452f88ce8988f46a200f739e6ca8632 2025-09-07T08:20:34.6451759Z Stored in directory: /tmp/pip-ephem-wheel-cache-seniybhg/wheels/55/0c/23/e830c2c3ad8ea6f122c5bd80ddd259405d4a52266f20ee6037 2025-09-07T08:20:34.6472478Z Successfully built torch_test_cpp_extension 2025-09-07T08:20:34.8036434Z Installing collected packages: torch_test_cpp_extension 2025-09-07T08:20:34.9650118Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T08:20:35.3287437Z Processing /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test 2025-09-07T08:20:36.7728389Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T08:20:36.7757854Z [?25hBuilding wheels for collected packages: no_python_abi_suffix_test 2025-09-07T08:20:36.7767204Z  DEPRECATION: Building 'no_python_abi_suffix_test' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'no_python_abi_suffix_test'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T08:20:38.4657994Z  Building wheel for no_python_abi_suffix_test (setup.py) ... [?25l- \ | done 2025-09-07T08:20:38.4663030Z [?25h Created wheel for no_python_abi_suffix_test: filename=no_python_abi_suffix_test-0.0.0-cp313-cp313-linux_x86_64.whl size=2971 sha256=bd57911e2d467bea4ead06484ff68ceb815054e2ce5c53afda15499ed93b2f5a 2025-09-07T08:20:38.4664796Z Stored in directory: /tmp/pip-ephem-wheel-cache-l7q8dlj9/wheels/c7/be/1e/a693498df0ac11a249ac34fc70a78f5f1b2d84d49e78482cb4 2025-09-07T08:20:38.4683875Z Successfully built no_python_abi_suffix_test 2025-09-07T08:20:38.6335001Z Installing collected packages: no_python_abi_suffix_test 2025-09-07T08:20:38.6437425Z Successfully installed no_python_abi_suffix_test-0.0.0 2025-09-07T08:20:38.6983451Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:20:38.6987681Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:20:38.698422] 2025-09-07T08:20:50.7010438Z 2025-09-07T08:20:50.7011376Z 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_b429576f186b9799_.log 2025-09-07T08:20:50.7020260Z Running 21 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library 2025-09-07T08:20:50.7027933Z 2025-09-07T08:20:50.7028162Z Running dynamo/test_functions 1/1 ... [2025-09-07 08:20:50.701285] 2025-09-07T08:20:50.7028587Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:20:50.7029689Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:20:50.701649] 2025-09-07T08:20:56.9016619Z 2025-09-07T08:20:56.9017743Z dynamo/test_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_functions_1.1_ca748c47dbea4190_.log 2025-09-07T08:20:56.9018508Z 2025-09-07T08:20:56.9020703Z Running dynamo/test_repros 1/1 ... [2025-09-07 08:20:56.901905] 2025-09-07T08:20:56.9021127Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:20:56.9024602Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_repros.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:20:56.902249] 2025-09-07T08:20:59.5856404Z 2025-09-07T08:20:59.5857306Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_e16e8a89bd455077_.log 2025-09-07T08:20:59.5857963Z 2025-09-07T08:20:59.5860332Z Running dynamo/test_aot_autograd_cache 1/1 ... [2025-09-07 08:20:59.585862] 2025-09-07T08:20:59.5860837Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:20:59.5864300Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:20:59.586206] 2025-09-07T08:21:05.9024580Z 2025-09-07T08:21:05.9025688Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_890ce2e770470bd0_.log 2025-09-07T08:21:05.9026444Z 2025-09-07T08:21:05.9028476Z Running dynamo/test_subclasses 1/1 ... [2025-09-07 08:21:05.902689] 2025-09-07T08:21:05.9028966Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:05.9032985Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_subclasses.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:05.903048] 2025-09-07T08:21:12.0844271Z 2025-09-07T08:21:12.0845877Z dynamo/test_subclasses 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subclasses_1.1_3b37be54d8f8cb99_.log 2025-09-07T08:21:12.0846609Z 2025-09-07T08:21:12.0848369Z Running dynamo/test_skip_guard_eval_unsafe 1/1 ... [2025-09-07 08:21:12.084643] 2025-09-07T08:21:12.0849035Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:12.0852544Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_skip_guard_eval_unsafe.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:12.084998] 2025-09-07T08:21:14.6610653Z 2025-09-07T08:21:14.6611820Z dynamo/test_skip_guard_eval_unsafe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_guard_eval_unsafe_1.1_88982766bbaaf8e5_.log 2025-09-07T08:21:14.6612629Z 2025-09-07T08:21:14.6614215Z Running dynamo/test_nops 1/1 ... [2025-09-07 08:21:14.661266] 2025-09-07T08:21:14.6614631Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:14.6618407Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:14.661612] 2025-09-07T08:21:17.2492205Z 2025-09-07T08:21:17.2493131Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_2ccd744d51478b6f_.log 2025-09-07T08:21:17.2493764Z 2025-09-07T08:21:17.2495848Z Running test_appending_byte_serializer 1/1 ... [2025-09-07 08:21:17.249427] 2025-09-07T08:21:17.2496313Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:17.2500008Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_appending_byte_serializer.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:17.249763] 2025-09-07T08:21:19.6393236Z 2025-09-07T08:21:19.6395004Z test_appending_byte_serializer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_appending_byte_serializer_1.1_c49b3d4c3692fa47_.log 2025-09-07T08:21:19.6395773Z 2025-09-07T08:21:19.6398914Z Running dynamo/test_inline_and_install 1/1 ... [2025-09-07 08:21:19.639678] 2025-09-07T08:21:19.6399397Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:19.6403556Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_inline_and_install.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:19.640117] 2025-09-07T08:21:22.3365241Z 2025-09-07T08:21:22.3366527Z dynamo/test_inline_and_install 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_inline_and_install_1.1_81bc05e079a591e9_.log 2025-09-07T08:21:22.3367318Z 2025-09-07T08:21:22.3369045Z Running dynamo/test_dicts 1/1 ... [2025-09-07 08:21:22.336712] 2025-09-07T08:21:22.3369760Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:22.3372935Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_dicts.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:22.337034] 2025-09-07T08:21:24.9454060Z 2025-09-07T08:21:24.9454961Z dynamo/test_dicts 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dicts_1.1_18fc747aad2f30ce_.log 2025-09-07T08:21:24.9455623Z 2025-09-07T08:21:24.9458153Z Running xpu/test_fusion 1/1 ... [2025-09-07 08:21:24.945660] 2025-09-07T08:21:24.9458568Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:24.9462415Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'xpu/test_fusion.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:24.946016] 2025-09-07T08:21:27.5292789Z 2025-09-07T08:21:27.5294075Z xpu/test_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_fusion_1.1_a4d57934592f23ea_.log 2025-09-07T08:21:27.5295425Z Running 0 items in this shard: 2025-09-07T08:21:27.5295789Z 2025-09-07T08:21:27.5298730Z Running dynamo/test_nested_graph_breaks 1/1 ... [2025-09-07 08:21:27.529667] 2025-09-07T08:21:27.5299484Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:27.5304171Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nested_graph_breaks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:27.530086] 2025-09-07T08:21:30.1352266Z 2025-09-07T08:21:30.1353889Z dynamo/test_nested_graph_breaks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nested_graph_breaks_1.1_a23187fc242bebe8_.log 2025-09-07T08:21:30.1355321Z 2025-09-07T08:21:30.1358035Z Running dynamo/test_subgraphs 1/1 ... [2025-09-07 08:21:30.135577] 2025-09-07T08:21:30.1358736Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:30.1362687Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:21:30.136003] 2025-09-07T08:21:32.7477567Z 2025-09-07T08:21:32.7478698Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_e87cbdaf3410e80d_.log 2025-09-07T08:21:32.7479464Z 2025-09-07T08:21:32.7481601Z Running dynamo/test_config 1/1 ... [2025-09-07 08:21:32.747975] 2025-09-07T08:21:32.7482044Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:32.7485398Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_config.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:32.748308] 2025-09-07T08:21:35.3550137Z 2025-09-07T08:21:35.3551012Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_241703aa55ded728_.log 2025-09-07T08:21:35.3551675Z 2025-09-07T08:21:35.3554038Z Running dynamo/test_install_free_tensors 1/1 ... [2025-09-07 08:21:35.355235] 2025-09-07T08:21:35.3554521Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:35.3558081Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_install_free_tensors.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:35.355569] 2025-09-07T08:21:37.9765965Z 2025-09-07T08:21:37.9766991Z dynamo/test_install_free_tensors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_install_free_tensors_1.1_67409506956d464e_.log 2025-09-07T08:21:37.9767763Z 2025-09-07T08:21:37.9770302Z Running dynamo/test_export 1/1 ... [2025-09-07 08:21:37.976871] 2025-09-07T08:21:37.9770711Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:37.9774584Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:37.977209] 2025-09-07T08:21:40.6090186Z 2025-09-07T08:21:40.6091373Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_1fbd2cc8de8c3825_.log 2025-09-07T08:21:40.6092062Z 2025-09-07T08:21:40.6094080Z Running xpu/test_gemm 1/1 ... [2025-09-07 08:21:40.609238] 2025-09-07T08:21:40.6094494Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:40.6097921Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'xpu/test_gemm.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:40.609570] 2025-09-07T08:21:43.2698033Z 2025-09-07T08:21:43.2699100Z xpu/test_gemm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_gemm_1.1_fb70b45da557b45c_.log 2025-09-07T08:21:43.2700404Z Running 0 items in this shard: 2025-09-07T08:21:43.2700607Z 2025-09-07T08:21:43.2702497Z Running dynamo/test_guard_serialization 1/1 ... [2025-09-07 08:21:43.270064] 2025-09-07T08:21:43.2703046Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:43.2706474Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_guard_serialization.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:43.270396] 2025-09-07T08:21:49.4999969Z 2025-09-07T08:21:49.5001020Z dynamo/test_guard_serialization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_guard_serialization_1.1_7647f6e7600712f5_.log 2025-09-07T08:21:49.5001811Z 2025-09-07T08:21:49.5003903Z Running dynamo/test_misc 1/1 ... [2025-09-07 08:21:49.500222] 2025-09-07T08:21:49.5004466Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:49.5008032Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_misc.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:49.500570] 2025-09-07T08:21:53.1526407Z 2025-09-07T08:21:53.1527254Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_52f56ab333477ffe_.log 2025-09-07T08:21:53.1527890Z 2025-09-07T08:21:53.1530590Z Running dynamo/test_export_mutations 1/1 ... [2025-09-07 08:21:53.152865] 2025-09-07T08:21:53.1531062Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:53.1534216Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export_mutations.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:53.153189] 2025-09-07T08:21:55.7572859Z 2025-09-07T08:21:55.7574043Z dynamo/test_export_mutations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_mutations_1.1_eec45d02ade314ff_.log 2025-09-07T08:21:55.7574816Z 2025-09-07T08:21:55.7577322Z Running test_jiterator 1/1 ... [2025-09-07 08:21:55.757557] 2025-09-07T08:21:55.7577942Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:55.7581065Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jiterator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:21:55.757882] 2025-09-07T08:21:58.2502903Z 2025-09-07T08:21:58.2503789Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_fe0846aef34b151b_.log 2025-09-07T08:21:58.2504514Z Running 0 items in this shard: 2025-09-07T08:21:58.2504722Z 2025-09-07T08:21:58.2508262Z Running dynamo/test_profiler 1/1 ... [2025-09-07 08:21:58.250585] 2025-09-07T08:21:58.2508700Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:21:58.2511681Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:21:58.250911] 2025-09-07T08:22:00.8643174Z 2025-09-07T08:22:00.8644075Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_c71fcc5de86677bd_.log 2025-09-07T08:22:00.8644832Z 2025-09-07T08:22:00.8647082Z Running dynamo/test_base_hop 1/1 ... [2025-09-07 08:22:00.864542] 2025-09-07T08:22:00.8647517Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:00.8650964Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_hop.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:00.864872] 2025-09-07T08:22:03.4566098Z 2025-09-07T08:22:03.4567035Z dynamo/test_base_hop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_hop_1.1_8143ed7d59f97b70_.log 2025-09-07T08:22:03.4567730Z 2025-09-07T08:22:03.4569900Z Running dynamo/test_python_dispatcher 1/1 ... [2025-09-07 08:22:03.456826] 2025-09-07T08:22:03.4570522Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:03.4574050Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_python_dispatcher.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:03.457161] 2025-09-07T08:22:06.0544540Z 2025-09-07T08:22:06.0545757Z dynamo/test_python_dispatcher 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_python_dispatcher_1.1_d00c9ee5b85ede40_.log 2025-09-07T08:22:06.0546538Z 2025-09-07T08:22:06.0549321Z Running dynamo/test_higher_order_ops 1/1 ... [2025-09-07 08:22:06.054726] 2025-09-07T08:22:06.0549804Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:06.0552825Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:22:06.055066] 2025-09-07T08:22:12.9429188Z 2025-09-07T08:22:12.9430289Z 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_4c010d494ea6390a_.log 2025-09-07T08:22:12.9431053Z 2025-09-07T08:22:12.9435533Z Running dynamo/test_debug_utils 1/1 ... [2025-09-07 08:22:12.943372] 2025-09-07T08:22:12.9435985Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:12.9440460Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:12.943834] 2025-09-07T08:22:15.5517530Z 2025-09-07T08:22:15.5518829Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_e176a384799e0c95_.log 2025-09-07T08:22:15.5519527Z 2025-09-07T08:22:15.5521115Z Running dynamo/test_graph_deduplication 1/1 ... [2025-09-07 08:22:15.551943] 2025-09-07T08:22:15.5521594Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:15.5524564Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_deduplication.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:15.552241] 2025-09-07T08:22:18.1722813Z 2025-09-07T08:22:18.1723874Z dynamo/test_graph_deduplication 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_deduplication_1.1_d89ff7bb2c1cc9f8_.log 2025-09-07T08:22:18.1724946Z 2025-09-07T08:22:18.1726839Z Running dynamo/test_decorators 1/1 ... [2025-09-07 08:22:18.172504] 2025-09-07T08:22:18.1727304Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:18.1730273Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_decorators.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:18.172812] 2025-09-07T08:22:20.7803117Z 2025-09-07T08:22:20.7804100Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_96aca0eed417ccd9_.log 2025-09-07T08:22:20.7805029Z 2025-09-07T08:22:20.7807392Z Running dynamo/test_aot_compile 1/1 ... [2025-09-07 08:22:20.780507] 2025-09-07T08:22:20.7807866Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:20.7810291Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_compile.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:20.780804] 2025-09-07T08:22:23.3855165Z 2025-09-07T08:22:23.3856297Z dynamo/test_aot_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_compile_1.1_711abad84ceb45a0_.log 2025-09-07T08:22:23.3857065Z 2025-09-07T08:22:23.3858863Z Running dynamo/test_reorder_logs 1/1 ... [2025-09-07 08:22:23.385704] 2025-09-07T08:22:23.3859318Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:23.3862594Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_reorder_logs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:23.386038] 2025-09-07T08:22:25.9823449Z 2025-09-07T08:22:25.9824954Z dynamo/test_reorder_logs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reorder_logs_1.1_e8abc4415ba12c3a_.log 2025-09-07T08:22:25.9826070Z 2025-09-07T08:22:25.9827733Z Running dynamo/test_exc 1/1 ... [2025-09-07 08:22:25.982572] 2025-09-07T08:22:25.9828357Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:25.9833162Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_exc.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:25.982995] 2025-09-07T08:22:28.3822915Z 2025-09-07T08:22:28.3824238Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_c56f55161b893a04_.log 2025-09-07T08:22:28.3825395Z 2025-09-07T08:22:28.3828939Z Running dynamo/test_minifier 1/1 ... [2025-09-07 08:22:28.382636] 2025-09-07T08:22:28.3829653Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:28.3833549Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:28.383047] 2025-09-07T08:22:30.7772366Z 2025-09-07T08:22:30.7773951Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_82bd9b4494cad564_.log 2025-09-07T08:22:30.7775221Z 2025-09-07T08:22:30.7778168Z Running dynamo/test_guard_manager 1/1 ... [2025-09-07 08:22:30.777623] 2025-09-07T08:22:30.7778840Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:30.7783924Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_guard_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'] ... [2025-09-07 08:22:30.778036] 2025-09-07T08:22:33.1668508Z 2025-09-07T08:22:33.1669415Z dynamo/test_guard_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_guard_manager_1.1_5c90800307b14329_.log 2025-09-07T08:22:33.1670142Z 2025-09-07T08:22:33.1672290Z Running dynamo/test_bytecode_utils 1/1 ... [2025-09-07 08:22:33.167059] 2025-09-07T08:22:33.1672831Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:33.1676726Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:33.167437] 2025-09-07T08:22:35.7575727Z 2025-09-07T08:22:35.7577211Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_7949b93e8d17f935_.log 2025-09-07T08:22:35.7578565Z 2025-09-07T08:22:35.7581357Z Running dynamo/test_generator 1/1 ... [2025-09-07 08:22:35.757931] 2025-09-07T08:22:35.7582050Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:35.7586561Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_generator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:35.758346] 2025-09-07T08:22:38.3695024Z 2025-09-07T08:22:38.3696418Z dynamo/test_generator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_generator_1.1_47efcd35ec3b8a74_.log 2025-09-07T08:22:38.3697716Z 2025-09-07T08:22:38.3700722Z Running test_unary_ufuncs 3/3 ... [2025-09-07 08:22:38.369856] 2025-09-07T08:22:38.3701502Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:38.3705830Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_unary_ufuncs.py', '-m', 'serial', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:38.370261] 2025-09-07T08:22:45.5440530Z 2025-09-07T08:22:45.5441558Z test_unary_ufuncs 3/3 was successful, full logs can be found in artifacts with path test/test-reports/test_unary_ufuncs_3.3_a4d47f56ca32230b_.log 2025-09-07T08:22:45.5442317Z Running 0 items in this shard: 2025-09-07T08:22:45.5442526Z 2025-09-07T08:22:45.5444818Z Running test_cuda_multigpu 1/1 ... [2025-09-07 08:22:45.544308] 2025-09-07T08:22:45.5445305Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:45.5449038Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:22:45.544674] 2025-09-07T08:22:48.0884078Z 2025-09-07T08:22:48.0885449Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_017521899df6f5f7_.log 2025-09-07T08:22:48.0886635Z Running 0 items in this shard: 2025-09-07T08:22:48.0886842Z 2025-09-07T08:22:50.1514227Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:22:50.1517279Z import pkg_resources 2025-09-07T08:22:50.2025625Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:22:50.2027399Z import pkg_resources 2025-09-07T08:22:50.2040485Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:22:50.2041984Z import pkg_resources 2025-09-07T08:22:50.3587065Z Running dynamo/test_functions 1/1 ... [2025-09-07 08:22:50.358244] 2025-09-07T08:22:50.3587820Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:50.3588450Z Running dynamo/test_repros 1/1 ... [2025-09-07 08:22:50.358327] 2025-09-07T08:22:50.3589125Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:50.3589972Z Running dynamo/test_aot_autograd_cache 1/1 ... [2025-09-07 08:22:50.358394] 2025-09-07T08:22:50.3590818Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:50.3592079Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:22:50.358606] 2025-09-07T08:22:50.3594204Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_repros.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:50.358751] 2025-09-07T08:22:50.3596606Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:50.358790] 2025-09-07T08:22:53.2505572Z 2025-09-07T08:22:53.2506592Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_e5da92568044f953_.log 2025-09-07T08:22:53.2507366Z 2025-09-07T08:22:55.8919644Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:22:55.8922529Z import pkg_resources 2025-09-07T08:22:56.0097351Z Running dynamo/test_subclasses 1/1 ... [2025-09-07 08:22:56.009299] 2025-09-07T08:22:56.0098206Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:22:56.0100408Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_subclasses.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:22:56.009647] 2025-09-07T08:22:57.1759145Z 2025-09-07T08:22:57.1760668Z dynamo/test_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_functions_1.1_29be69cc53e83b94_.log 2025-09-07T08:22:57.1762234Z 2025-09-07T08:22:58.6460708Z 2025-09-07T08:22:58.6462208Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_c9e9e16d2382de14_.log 2025-09-07T08:22:58.6463429Z 2025-09-07T08:22:59.9560833Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:22:59.9563805Z import pkg_resources 2025-09-07T08:23:00.0772362Z Running dynamo/test_skip_guard_eval_unsafe 1/1 ... [2025-09-07 08:23:00.076833] 2025-09-07T08:23:00.0773783Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:00.0776060Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_skip_guard_eval_unsafe.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:00.077208] 2025-09-07T08:23:01.3593063Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:01.3594610Z import pkg_resources 2025-09-07T08:23:01.4803603Z Running dynamo/test_nops 1/1 ... [2025-09-07 08:23:01.479945] 2025-09-07T08:23:01.4804255Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:01.4805988Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:01.480316] 2025-09-07T08:23:02.8822782Z 2025-09-07T08:23:02.8824016Z dynamo/test_subclasses 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subclasses_1.1_63d402949ca846e6_.log 2025-09-07T08:23:02.8824967Z 2025-09-07T08:23:02.9960061Z 2025-09-07T08:23:02.9961744Z dynamo/test_skip_guard_eval_unsafe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_guard_eval_unsafe_1.1_311b2fda44d541db_.log 2025-09-07T08:23:02.9962745Z 2025-09-07T08:23:04.3445062Z 2025-09-07T08:23:04.3446472Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_7a60b57c2d1bed22_.log 2025-09-07T08:23:04.3447190Z 2025-09-07T08:23:05.6718672Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:05.6721580Z import pkg_resources 2025-09-07T08:23:05.6786425Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:05.6789403Z import pkg_resources 2025-09-07T08:23:05.7919940Z Running test_appending_byte_serializer 1/1 ... [2025-09-07 08:23:05.791577] 2025-09-07T08:23:05.7920773Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:05.7922863Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_appending_byte_serializer.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:05.791923] 2025-09-07T08:23:05.7978587Z Running dynamo/test_inline_and_install 1/1 ... [2025-09-07 08:23:05.797622] 2025-09-07T08:23:05.7979086Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:05.7982318Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_inline_and_install.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:05.797979] 2025-09-07T08:23:06.9359805Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:06.9362719Z import pkg_resources 2025-09-07T08:23:07.0559660Z Running dynamo/test_dicts 1/1 ... [2025-09-07 08:23:07.055562] 2025-09-07T08:23:07.0560258Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:07.0562212Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_dicts.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:07.055929] 2025-09-07T08:23:08.4493617Z 2025-09-07T08:23:08.4495302Z test_appending_byte_serializer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_appending_byte_serializer_1.1_b3db1e66bdd5055f_.log 2025-09-07T08:23:08.4496831Z 2025-09-07T08:23:08.7051839Z 2025-09-07T08:23:08.7052916Z dynamo/test_inline_and_install 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_inline_and_install_1.1_6d9d2f316b68ccd8_.log 2025-09-07T08:23:08.7053700Z 2025-09-07T08:23:09.9849711Z 2025-09-07T08:23:09.9851090Z dynamo/test_dicts 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dicts_1.1_5fab43ab7a85bc25_.log 2025-09-07T08:23:09.9852270Z 2025-09-07T08:23:11.1542814Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:11.1544355Z import pkg_resources 2025-09-07T08:23:11.2735807Z Running xpu/test_fusion 1/1 ... [2025-09-07 08:23:11.273169] 2025-09-07T08:23:11.2736408Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:11.2738771Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'xpu/test_fusion.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:11.273576] 2025-09-07T08:23:11.3746933Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:11.3748477Z import pkg_resources 2025-09-07T08:23:11.4949204Z Running dynamo/test_nested_graph_breaks 1/1 ... [2025-09-07 08:23:11.494508] 2025-09-07T08:23:11.4950082Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:11.4951966Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nested_graph_breaks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:11.494890] 2025-09-07T08:23:12.5862375Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:12.5863936Z import pkg_resources 2025-09-07T08:23:12.7067824Z Running dynamo/test_subgraphs 1/1 ... [2025-09-07 08:23:12.706382] 2025-09-07T08:23:12.7068360Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:12.7071108Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:23:12.706777] 2025-09-07T08:23:14.1202715Z 2025-09-07T08:23:14.1204427Z xpu/test_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_fusion_1.1_33de7571664d0d05_.log 2025-09-07T08:23:14.1205703Z Running 0 items in this shard: 2025-09-07T08:23:14.1206019Z 2025-09-07T08:23:14.4214895Z 2025-09-07T08:23:14.4216635Z dynamo/test_nested_graph_breaks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nested_graph_breaks_1.1_16c7b0e9a1faed41_.log 2025-09-07T08:23:14.4217749Z 2025-09-07T08:23:15.5610018Z 2025-09-07T08:23:15.5611021Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_d2ca4e9bc2ab7add_.log 2025-09-07T08:23:15.5611735Z 2025-09-07T08:23:16.8846650Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:16.8849612Z import pkg_resources 2025-09-07T08:23:17.0040375Z Running dynamo/test_config 1/1 ... [2025-09-07 08:23:17.003616] 2025-09-07T08:23:17.0041159Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:17.0043596Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_config.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:17.004025] 2025-09-07T08:23:17.2075884Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:17.2078870Z import pkg_resources 2025-09-07T08:23:17.3301987Z Running dynamo/test_install_free_tensors 1/1 ... [2025-09-07 08:23:17.329769] 2025-09-07T08:23:17.3302588Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:17.3304083Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_install_free_tensors.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:17.330134] 2025-09-07T08:23:18.2304901Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:18.2307708Z import pkg_resources 2025-09-07T08:23:18.3496525Z Running dynamo/test_export 1/1 ... [2025-09-07 08:23:18.349222] 2025-09-07T08:23:18.3497354Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:18.3499627Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:18.349548] 2025-09-07T08:23:19.9891282Z 2025-09-07T08:23:19.9892590Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_ec8a6271637cc932_.log 2025-09-07T08:23:19.9893339Z 2025-09-07T08:23:20.2627079Z 2025-09-07T08:23:20.2628558Z dynamo/test_install_free_tensors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_install_free_tensors_1.1_37d09e93002f4940_.log 2025-09-07T08:23:20.2629428Z 2025-09-07T08:23:21.2444545Z 2025-09-07T08:23:21.2446359Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_913f6f339557cddf_.log 2025-09-07T08:23:21.2447452Z 2025-09-07T08:23:22.7293652Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:22.7296480Z import pkg_resources 2025-09-07T08:23:22.8474859Z Running xpu/test_gemm 1/1 ... [2025-09-07 08:23:22.846976] 2025-09-07T08:23:22.8475644Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:22.8477659Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'xpu/test_gemm.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:22.847365] 2025-09-07T08:23:23.0954104Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:23.0956971Z import pkg_resources 2025-09-07T08:23:23.2155765Z Running dynamo/test_guard_serialization 1/1 ... [2025-09-07 08:23:23.215170] 2025-09-07T08:23:23.2156602Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:23.2159066Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_guard_serialization.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:23.215554] 2025-09-07T08:23:23.9549566Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:23.9552476Z import pkg_resources 2025-09-07T08:23:24.0740505Z Running dynamo/test_misc 1/1 ... [2025-09-07 08:23:24.073654] 2025-09-07T08:23:24.0741007Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:24.0744640Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_misc.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:24.074015] 2025-09-07T08:23:25.7359975Z 2025-09-07T08:23:25.7361360Z xpu/test_gemm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_gemm_1.1_9362b649d9dc375f_.log 2025-09-07T08:23:25.7362715Z Running 0 items in this shard: 2025-09-07T08:23:25.7363042Z 2025-09-07T08:23:28.1272243Z 2025-09-07T08:23:28.1273532Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_e6bc5471a94dd8c5_.log 2025-09-07T08:23:28.1274466Z 2025-09-07T08:23:28.4182640Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:28.4184203Z import pkg_resources 2025-09-07T08:23:28.5406462Z Running dynamo/test_export_mutations 1/1 ... [2025-09-07 08:23:28.540205] 2025-09-07T08:23:28.5407350Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:28.5409929Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export_mutations.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:28.540587] 2025-09-07T08:23:30.1968840Z 2025-09-07T08:23:30.1970476Z dynamo/test_guard_serialization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_guard_serialization_1.1_8c4e36d18af43a3a_.log 2025-09-07T08:23:30.1971961Z 2025-09-07T08:23:30.7702748Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:30.7704292Z import pkg_resources 2025-09-07T08:23:30.8904967Z Running test_jiterator 1/1 ... [2025-09-07 08:23:30.890130] 2025-09-07T08:23:30.8905406Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:30.8908147Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jiterator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:30.890520] 2025-09-07T08:23:31.4211809Z 2025-09-07T08:23:31.4213112Z dynamo/test_export_mutations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_mutations_1.1_ecbd72c49ab7c6a3_.log 2025-09-07T08:23:31.4213874Z 2025-09-07T08:23:32.8814639Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:32.8817531Z import pkg_resources 2025-09-07T08:23:33.0037537Z Running dynamo/test_profiler 1/1 ... [2025-09-07 08:23:33.003323] 2025-09-07T08:23:33.0038306Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:33.0040348Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:23:33.003691] 2025-09-07T08:23:33.7318681Z 2025-09-07T08:23:33.7319923Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_365b0980bb539b66_.log 2025-09-07T08:23:33.7320843Z Running 0 items in this shard: 2025-09-07T08:23:33.7321039Z 2025-09-07T08:23:34.0613503Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:34.0616744Z import pkg_resources 2025-09-07T08:23:34.1826522Z Running dynamo/test_base_hop 1/1 ... [2025-09-07 08:23:34.182228] 2025-09-07T08:23:34.1827712Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:34.1829890Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_hop.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:34.182632] 2025-09-07T08:23:35.9214354Z 2025-09-07T08:23:35.9215658Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_25b0c3396651af4f_.log 2025-09-07T08:23:35.9216440Z 2025-09-07T08:23:36.3608200Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:36.3611237Z import pkg_resources 2025-09-07T08:23:36.4811589Z Running dynamo/test_python_dispatcher 1/1 ... [2025-09-07 08:23:36.480703] 2025-09-07T08:23:36.4812393Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:36.4814470Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_python_dispatcher.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:36.481031] 2025-09-07T08:23:37.1617742Z 2025-09-07T08:23:37.1619086Z dynamo/test_base_hop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_hop_1.1_4a9f0b806af15244_.log 2025-09-07T08:23:37.1619775Z 2025-09-07T08:23:38.5866472Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:38.5869454Z import pkg_resources 2025-09-07T08:23:38.7058949Z Running dynamo/test_higher_order_ops 1/1 ... [2025-09-07 08:23:38.705467] 2025-09-07T08:23:38.7059817Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:38.7061903Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:23:38.705833] 2025-09-07T08:23:39.3384089Z 2025-09-07T08:23:39.3385732Z dynamo/test_python_dispatcher 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_python_dispatcher_1.1_f195030cc8f2f89b_.log 2025-09-07T08:23:39.3387148Z 2025-09-07T08:23:39.8284577Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:39.8286141Z import pkg_resources 2025-09-07T08:23:39.9477665Z Running dynamo/test_debug_utils 1/1 ... [2025-09-07 08:23:39.947394] 2025-09-07T08:23:39.9478305Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:39.9480108Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:39.947737] 2025-09-07T08:23:41.9932175Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:41.9935347Z import pkg_resources 2025-09-07T08:23:42.1128847Z Running dynamo/test_graph_deduplication 1/1 ... [2025-09-07 08:23:42.112486] 2025-09-07T08:23:42.1129663Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:42.1131843Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_deduplication.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:42.112842] 2025-09-07T08:23:42.7994191Z 2025-09-07T08:23:42.7995714Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_5e9fb8feac1c096a_.log 2025-09-07T08:23:42.7997064Z 2025-09-07T08:23:45.0945601Z 2025-09-07T08:23:45.0946808Z dynamo/test_graph_deduplication 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_deduplication_1.1_c8bbf23fc620e447_.log 2025-09-07T08:23:45.0947615Z 2025-09-07T08:23:45.4800245Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:45.4801808Z import pkg_resources 2025-09-07T08:23:45.6173687Z Running dynamo/test_decorators 1/1 ... [2025-09-07 08:23:45.616957] 2025-09-07T08:23:45.6174466Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:45.6176713Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_decorators.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:45.617368] 2025-09-07T08:23:46.5084137Z 2025-09-07T08:23:46.5086598Z 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_267298a435dc9a99_.log 2025-09-07T08:23:46.5087913Z 2025-09-07T08:23:47.7891244Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:47.7894217Z import pkg_resources 2025-09-07T08:23:47.9094799Z Running dynamo/test_aot_compile 1/1 ... [2025-09-07 08:23:47.909073] 2025-09-07T08:23:47.9095780Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:47.9097912Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_compile.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:47.909429] 2025-09-07T08:23:48.5516321Z 2025-09-07T08:23:48.5517744Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_293d07fdebac3912_.log 2025-09-07T08:23:48.5518916Z 2025-09-07T08:23:49.1036367Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:49.2239601Z import pkg_resources 2025-09-07T08:23:49.2240308Z Running dynamo/test_reorder_logs 1/1 ... [2025-09-07 08:23:49.223591] 2025-09-07T08:23:49.2241019Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:49.2243269Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_reorder_logs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:49.223957] 2025-09-07T08:23:50.7601483Z 2025-09-07T08:23:50.7602818Z dynamo/test_aot_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_compile_1.1_dd6ffe8d3499df79_.log 2025-09-07T08:23:50.7604765Z 2025-09-07T08:23:51.2001149Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:51.2004090Z import pkg_resources 2025-09-07T08:23:51.3206578Z Running dynamo/test_exc 1/1 ... [2025-09-07 08:23:51.320166] 2025-09-07T08:23:51.3207417Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:51.3209162Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_exc.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:51.320533] 2025-09-07T08:23:52.1828802Z 2025-09-07T08:23:52.1830034Z dynamo/test_reorder_logs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reorder_logs_1.1_e46329ab61a85f1c_.log 2025-09-07T08:23:52.1830844Z 2025-09-07T08:23:53.4188352Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:53.4191340Z import pkg_resources 2025-09-07T08:23:53.5390152Z Running dynamo/test_minifier 1/1 ... [2025-09-07 08:23:53.538597] 2025-09-07T08:23:53.5390919Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:53.5393333Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:53.539000] 2025-09-07T08:23:53.9906760Z 2025-09-07T08:23:53.9908023Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_56cd3fcdb8a50666_.log 2025-09-07T08:23:53.9908844Z 2025-09-07T08:23:54.8637532Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:54.8640827Z import pkg_resources 2025-09-07T08:23:55.0016331Z Running dynamo/test_guard_manager 1/1 ... [2025-09-07 08:23:55.001228] 2025-09-07T08:23:55.0017185Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:55.0019846Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_guard_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'] ... [2025-09-07 08:23:55.001584] 2025-09-07T08:23:56.1865321Z 2025-09-07T08:23:56.1866527Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_644d48bf91bf9805_.log 2025-09-07T08:23:56.1867658Z 2025-09-07T08:23:56.6314796Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:56.6318086Z import pkg_resources 2025-09-07T08:23:56.7526241Z Running dynamo/test_bytecode_utils 1/1 ... [2025-09-07 08:23:56.752204] 2025-09-07T08:23:56.7527104Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:56.7529668Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:56.752577] 2025-09-07T08:23:57.6667247Z 2025-09-07T08:23:57.6669164Z dynamo/test_guard_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_guard_manager_1.1_54e1130f06e4fba7_.log 2025-09-07T08:23:57.6671108Z 2025-09-07T08:23:58.8605380Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:23:58.8608262Z import pkg_resources 2025-09-07T08:23:58.9801816Z Running dynamo/test_generator 1/1 ... [2025-09-07 08:23:58.979759] 2025-09-07T08:23:58.9802557Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:23:58.9804743Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_generator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:23:58.980114] 2025-09-07T08:23:59.6689467Z 2025-09-07T08:23:59.6691780Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_4951c524b9e9dcc2_.log 2025-09-07T08:23:59.6692579Z 2025-09-07T08:24:00.3248628Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:24:00.3251488Z import pkg_resources 2025-09-07T08:24:00.4454492Z Running test_unary_ufuncs 3/3 ... [2025-09-07 08:24:00.445050] 2025-09-07T08:24:00.4455297Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:24:00.4457866Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_unary_ufuncs.py', '-m', 'not serial', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 08:24:00.445408] 2025-09-07T08:24:01.9202295Z 2025-09-07T08:24:01.9203487Z dynamo/test_generator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_generator_1.1_b7f693e0527b397f_.log 2025-09-07T08:24:01.9204317Z 2025-09-07T08:24:02.3244669Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:24:02.3247670Z import pkg_resources 2025-09-07T08:24:02.4459579Z Running test_cuda_multigpu 1/1 ... [2025-09-07 08:24:02.445546] 2025-09-07T08:24:02.4460418Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:24:02.4462426Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-09-07 08:24:02.445916] 2025-09-07T08:24:04.5898176Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:24:04.5900869Z import pkg_resources 2025-09-07T08:24:05.1823058Z 2025-09-07T08:24:05.1824344Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_3ac42540b2351412_.log 2025-09-07T08:24:05.1825131Z Running 0 items in this shard: 2025-09-07T08:24:05.1825340Z 2025-09-07T08:24:07.8324362Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:24:07.8325962Z import pkg_resources 2025-09-07T08:30:35.2866425Z 2025-09-07T08:30:35.2867252Z test_unary_ufuncs 3/3 was successful, full logs can be found in artifacts with path test/test-reports/test_unary_ufuncs_3.3_d124d56c5a1f45db_.log 2025-09-07T08:30:35.6161286Z Running 8092 items in this shard: test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_angle_complex_to_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_byte_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_byte_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cdouble_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cdouble_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cdouble_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cdouble_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cdouble_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cdouble_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cdouble_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cfloat_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cfloat_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cfloat_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cfloat_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_cfloat_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_char_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_char_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_char_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_float_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_float_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_float_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_half_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_int_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_int_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_int_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_long_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_long_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_short_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_short_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_short_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_short_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_short_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_short_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_short_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs_abs_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs_abs_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs_abs_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frac_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frac_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_i0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_imag_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logical_not_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logical_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logical_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_0_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_positive_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_reciprocal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_decimals_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rsqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rsqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rsqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_bessel_j1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_bessel_j1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_bessel_j1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_bessel_y0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_bessel_y0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_bessel_y1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_entr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_entr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_entr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_entr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_entr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_erfcx_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_erfcx_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_erfcx_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_erfcx_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_erfcx_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i0e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i0e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i0e_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i0e_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i0e_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i0e_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i1e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i1e_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i1e_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i1e_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_i1e_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_log_ndtr_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_log_ndtr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_log_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_log_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_log_ndtr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_i0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_i1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_i1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_k0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_k0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_k0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_k0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_k0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_k1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_modified_bessel_k1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_ndtr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_ndtr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_ndtri_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_ndtri_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_ndtri_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_polygamma_special_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_polygamma_special_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_scaled_modified_bessel_k0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_scaled_modified_bessel_k0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_scaled_modified_bessel_k1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_scaled_modified_bessel_k1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_scaled_modified_bessel_k1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_spherical_bessel_j0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_spherical_bessel_j0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_special_spherical_bessel_j0_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_round_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sgn_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sgn_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sgn_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sgn_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sgn_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sigmoid_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sigmoid_cpu_int64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sinc_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sinc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sinc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_bessel_j0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_bessel_j0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_bessel_j1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_entr_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_entr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_entr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_entr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_entr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_entr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_erfcx_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_erfcx_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_erfcx_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_i0e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_i0e_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_i1e_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_i1e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_i1e_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_i1e_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_log_ndtr_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_log_ndtr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_logit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_multigammaln_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_ndtr_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_ndtr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_ndtr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_special_ndtri_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_sqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_square_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_square_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_square_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_square_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_square_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_square_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_tan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_tan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_tan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_tan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_tanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_tanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_tanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_tanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_trunc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_trunc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_trunc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_trunc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bool_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_byte_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cdouble_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cdouble_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cdouble_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cdouble_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cdouble_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cdouble_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_ceil_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_ceil_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_ceil_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cfloat_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_imag_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sigmoid_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sigmoid_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sign_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_signbit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_signbit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinc_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_airy_ai_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_airy_ai_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_j0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_j0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_j1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_j1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_y0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_y0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_y1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_y1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_entr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_entr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_entr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_entr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_erfcx_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_erfcx_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i0e_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i0e_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i0e_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i0e_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1e_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_log_ndtr_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_log_ndtr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtri_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtri_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_spherical_bessel_j0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_spherical_bessel_j0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_byte_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_byte_cpu_float32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_asinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_asinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_asinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_asinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_atan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_atan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_atan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_atanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_atanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_ceil_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_ceil_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_ceil_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_ceil_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_ceil_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_conj_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtr_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_spherical_bessel_j0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_spherical_bessel_j0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_square_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_trunc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1e_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_log_ndtr_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_log_ndtr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_log_ndtr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_log_ndtr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_k0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_k0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_k0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_k1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtri_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtri_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_polygamma_special_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_polygamma_special_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_polygamma_special_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_spherical_bessel_j0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_square_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_square_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_square_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_square_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_square_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_square_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_asin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_special_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_common_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_edge_cases_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_edge_cases_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_edge_cases_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_log1p_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_bfloat16_cpu, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bfloat16_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bool_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bool_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_byte_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_byte_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_byte_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_byte_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_byte_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cdouble_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cdouble_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cdouble_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_float_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_float_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_half_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_int_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_int_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_long_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_short_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_short_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_acos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_acos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_acosh_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_atan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_atan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_atan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_atanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_atanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_atanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bfloat16_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bitwise_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_exp_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_expm1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_fill_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_fill_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_fill_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_floor_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_floor_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_floor_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_floor_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_floor_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_frac_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_half_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_i0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_i0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_i0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_imag_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_imag_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_int_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isfinite_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isfinite_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isfinite_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isfinite_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isfinite_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isfinite_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isfinite_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isneginf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isneginf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_lgamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_lgamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_lgamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_lgamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sgn_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sgn_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_short_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sign_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_signbit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_signbit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_signbit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_signbit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_signbit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_signbit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sin_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sin_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinc_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_y1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_entr_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_entr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_entr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_entr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_erfcx_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_erfcx_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_erfcx_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i0e_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i0e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i1e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i1e_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_i1e_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_log_ndtr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_log_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_modified_bessel_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_modified_bessel_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_modified_bessel_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_modified_bessel_i1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_modified_bessel_i1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_modified_bessel_k0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_modified_bessel_k0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_modified_bessel_k0_cpu_int8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_decimals_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_decimals_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_decimals_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rsqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rsqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rsqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rsqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_rsqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sgn_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sign_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_signbit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_signbit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_signbit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_signbit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_signbit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sin_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_airy_ai_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_airy_ai_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_airy_ai_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_airy_ai_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_bessel_j0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_bessel_j1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_bessel_j1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_bessel_j1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_bessel_y0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_bessel_y1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_bessel_y1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_bessel_y1_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_i1e_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_i1e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_i1e_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_i1e_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_log_ndtr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_log_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_log_ndtr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_log_ndtr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_log_ndtr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_modified_bessel_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_modified_bessel_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_modified_bessel_i1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_modified_bessel_i1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_modified_bessel_k0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_modified_bessel_k1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_modified_bessel_k1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_ndtr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_ndtri_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_ndtri_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_ndtri_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_polygamma_special_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_polygamma_special_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_scaled_modified_bessel_k0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_scaled_modified_bessel_k0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_scaled_modified_bessel_k1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_scaled_modified_bessel_k1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_spherical_bessel_j0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_special_spherical_bessel_j0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_sqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_square_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_square_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_square_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_tan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_tan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_tanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_tanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_tanh_cpu_int64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_polygamma_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_polygamma_polygamma_n_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_polygamma_polygamma_n_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_polygamma_polygamma_n_4_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_extremal_real_cpu_float16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_abs_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_abs_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_abs_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_acosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_asin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_asin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_asin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_asinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_asinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_asinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_asinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_atan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_atan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_atan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_atanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_atanh_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_erf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_large__refs_erfc_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_4_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_real_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_real_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_round_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_round_decimals_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_round_decimals_neg_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_round_decimals_neg_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_rsqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sgn_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sgn_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sgn_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sigmoid_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sigmoid_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sign_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_signbit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_signbit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_signbit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sinc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sinc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sinc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sinh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_airy_ai_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_airy_ai_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_airy_ai_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_j0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_j1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_y0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_y0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_y1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_y1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_y1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_y1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_bessel_y1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_entr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_entr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_entr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_entr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_erfcx_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i0e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i0e_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i1e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i1e_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i1e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i1e_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i1e_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_i1e_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_log_ndtr_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_log_ndtr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_log_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_log_ndtr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_i1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_i1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_i1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_i1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_k0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_k0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_k0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_k1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_k1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_modified_bessel_k1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_ndtr_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_ndtr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_ndtr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_polygamma_special_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_polygamma_special_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_scaled_modified_bessel_k0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_scaled_modified_bessel_k1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_special_spherical_bessel_j0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_sqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_square_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_i0_i1_vs_scipy_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_unary_out_op_mem_overlap_cpu_float64 2025-09-07T08:30:35.9379040Z 2025-09-07T08:30:35.9379239Z Running test batch 'tests to run' cost 7089.81 seconds 2025-09-07T08:30:36.7590195Z 2025-09-07T08:30:36.7590559Z real 118m14.981s 2025-09-07T08:30:36.7590862Z user 140m1.256s 2025-09-07T08:30:36.7591080Z sys 22m53.698s 2025-09-07T08:30:36.7596362Z + assert_git_not_dirty 2025-09-07T08:30:36.7608784Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-09-07T08:30:36.7609177Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-09-07T08:30:36.7628243Z ++ git status --porcelain 2025-09-07T08:30:36.7628896Z ++ grep -v '?? third_party' 2025-09-07T08:31:19.4020827Z ++ true 2025-09-07T08:31:19.4048261Z + git_status= 2025-09-07T08:31:19.4048538Z + [[ -n '' ]] 2025-09-07T08:31:19.4054520Z + [[ 1 == 1 ]] 2025-09-07T08:31:19.4054758Z + test_aten 2025-09-07T08:31:19.4061750Z + echo 'Running ATen tests with pytorch lib' 2025-09-07T08:31:19.4062705Z Running ATen tests with pytorch lib 2025-09-07T08:31:19.4063212Z + [[ -n '' ]] 2025-09-07T08:31:19.4063477Z + echo 'Running test with the build folder' 2025-09-07T08:31:19.4063851Z Running test with the build folder 2025-09-07T08:31:19.4064170Z + TEST_BASE_DIR=build/bin 2025-09-07T08:31:19.4064654Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin 2025-09-07T08:31:19.4091076Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin 2025-09-07T08:31:19.4102826Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin 2025-09-07T08:31:19.4114448Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin 2025-09-07T08:31:19.4127505Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorchbind_test.so build/bin 2025-09-07T08:31:19.4136575Z + ls build/bin 2025-09-07T08:31:19.4195789Z BackoffTest c10_typeid_test 2025-09-07T08:31:19.4196360Z CMakeFiles cmake_install.cmake 2025-09-07T08:31:19.4196925Z CTestTestfile.cmake cpu_allocator_test 2025-09-07T08:31:19.4197528Z CppSignature_test cpu_generator_test 2025-09-07T08:31:19.4198155Z Dict_test cpu_profiling_allocator_test 2025-09-07T08:31:19.4198648Z Dimname_test cpu_rng_test 2025-09-07T08:31:19.4199165Z FileStoreTest dlconvertor_test 2025-09-07T08:31:19.4199696Z HashStoreTest example_allreduce 2025-09-07T08:31:19.4200339Z IListRef_test extension_backend_test 2025-09-07T08:31:19.4200832Z KernelFunction_test half_test 2025-09-07T08:31:19.4201330Z List_test inline_container_test 2025-09-07T08:31:19.4201962Z MaybeOwned_test ivalue_test 2025-09-07T08:31:19.4202319Z NamedTensor_test kernel_function_legacy_test 2025-09-07T08:31:19.4202780Z ProcessGroupGlooTest kernel_function_test 2025-09-07T08:31:19.4203413Z StorageUtils_test kernel_lambda_legacy_test 2025-09-07T08:31:19.4203911Z TCPStoreTest kernel_lambda_test 2025-09-07T08:31:19.4204436Z apply_utils_test kernel_stackbased_test 2025-09-07T08:31:19.4204928Z atest lazy_tensor_test 2025-09-07T08:31:19.4205445Z backend_fallback_test legacy_vmap_test 2025-09-07T08:31:19.4205942Z basic libc10.so 2025-09-07T08:31:19.4206766Z broadcast_test 'libcaffe2*' 2025-09-07T08:31:19.4207119Z c10_AllocatorConfig_test 'libmkldnn*' 2025-09-07T08:31:19.4207487Z c10_ArrayRef_test 'libnccl*' 2025-09-07T08:31:19.4207874Z c10_Bitset_test libtorch.so 2025-09-07T08:31:19.4208268Z c10_CompileTimeFunctionPointer_test libtorch_cpu.so 2025-09-07T08:31:19.4208708Z c10_ConstexprCrc_test libtorch_global_deps.so 2025-09-07T08:31:19.4209137Z c10_DeadlockDetection_test libtorch_python.so 2025-09-07T08:31:19.4209917Z c10_DeviceGuard_test libtorchbind_test.so 2025-09-07T08:31:19.4210473Z c10_Device_test make_boxed_from_unboxed_functor_test 2025-09-07T08:31:19.4210904Z c10_DispatchKeySet_test math_kernel_test 2025-09-07T08:31:19.4211272Z c10_Enumerate_test memory_format_test 2025-09-07T08:31:19.4211643Z c10_Half_test memory_overlapping_test 2025-09-07T08:31:19.4212052Z c10_InlineDeviceGuard_test mobile_memory_cleanup 2025-09-07T08:31:19.4212469Z c10_InlineStreamGuard_test native_test 2025-09-07T08:31:19.4212840Z c10_IntrusiveList_test op_allowlist_test 2025-09-07T08:31:19.4213225Z c10_LeftRight_test op_registration_test 2025-09-07T08:31:19.4213622Z c10_Metaprogramming_test operator_name_test 2025-09-07T08:31:19.4214078Z c10_NetworkFlow_test operators_test 2025-09-07T08:31:19.4214442Z c10_Scalar_test packedtensoraccessor_test 2025-09-07T08:31:19.4214831Z c10_Semaphore_test parallel_benchmark 2025-09-07T08:31:19.4215197Z c10_SizesAndStrides_test pow_test 2025-09-07T08:31:19.4215532Z c10_StreamGuard_test protoc 2025-09-07T08:31:19.4215840Z c10_SymInt_test protoc-3.13.0.0 2025-09-07T08:31:19.4216184Z c10_Synchronized_test quantized_test 2025-09-07T08:31:19.4216547Z c10_ThreadLocal_test reduce_ops_test 2025-09-07T08:31:19.4216928Z c10_TypeIndex_test reportMemoryUsage_test 2025-09-07T08:31:19.4217299Z c10_TypeList_test scalar_tensor_test 2025-09-07T08:31:19.4217648Z c10_TypeTraits_test scalar_test 2025-09-07T08:31:19.4218004Z c10_accumulate_test static_runtime_bench 2025-09-07T08:31:19.4218379Z c10_bfloat16_test static_runtime_test 2025-09-07T08:31:19.4218792Z c10_bit_cast_test stride_properties_test 2025-09-07T08:31:19.4219189Z c10_complex_math_test tensor_iterator_test 2025-09-07T08:31:19.4219550Z c10_complex_test test_api 2025-09-07T08:31:19.4219858Z c10_cow_test test_cpp_rpc 2025-09-07T08:31:19.4220186Z c10_error_test test_dist_autograd 2025-09-07T08:31:19.4220507Z c10_exception_test test_jit 2025-09-07T08:31:19.4220816Z c10_flags_test test_lazy 2025-09-07T08:31:19.4221130Z c10_generic_math_test test_nativert 2025-09-07T08:31:19.4221494Z c10_intrusive_ptr_benchmark test_parallel 2025-09-07T08:31:19.4221863Z c10_intrusive_ptr_test thread_init_test 2025-09-07T08:31:19.4222223Z c10_irange_test torch_shm_manager 2025-09-07T08:31:19.4222555Z c10_lazy_test type_ptr_test 2025-09-07T08:31:19.4222865Z c10_logging_test type_test 2025-09-07T08:31:19.4223186Z c10_optional_test undefined_tensor_test 2025-09-07T08:31:19.4223621Z c10_ordered_preserving_dict_test vec_test_all_types_AVX2 2025-09-07T08:31:19.4224073Z c10_registry_test vec_test_all_types_AVX512 2025-09-07T08:31:19.4224491Z c10_small_vector_test vec_test_all_types_DEFAULT 2025-09-07T08:31:19.4224875Z c10_ssize_test verify_api_visibility 2025-09-07T08:31:19.4225233Z c10_string_util_test weakref_test 2025-09-07T08:31:19.4225568Z c10_string_view_test wrapdim_test 2025-09-07T08:31:19.4225908Z c10_tempfile_test xla_tensor_test 2025-09-07T08:31:19.4226229Z + aten/tools/run_tests.sh build/bin 2025-09-07T08:31:19.4249846Z + set -e 2025-09-07T08:31:19.4252724Z ++ dirname aten/tools/run_tests.sh 2025-09-07T08:31:19.4275279Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2025-09-07T08:31:19.4275870Z + export CPP_TESTS_DIR=build/bin 2025-09-07T08:31:19.4276162Z + CPP_TESTS_DIR=build/bin 2025-09-07T08:31:19.4276447Z + VALGRIND=ON 2025-09-07T08:31:19.4278263Z + python test/run_test.py --cpp --verbose -i cpp/basic cpp/atest cpp/scalar_test cpp/broadcast_test cpp/wrapdim_test cpp/apply_utils_test cpp/dlconvertor_test cpp/native_test cpp/scalar_tensor_test cpp/undefined_tensor_test cpp/extension_backend_test cpp/lazy_tensor_test cpp/tensor_iterator_test cpp/Dimname_test cpp/Dict_test cpp/NamedTensor_test cpp/cpu_generator_test cpp/legacy_vmap_test cpp/operators_test 2025-09-07T08:31:21.3501097Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:31:21.3502711Z import pkg_resources 2025-09-07T08:31:22.7399435Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-09-07T08:31:22.7574068Z Found test times from artifacts 2025-09-07T08:31:22.8247909Z Found test times from artifacts 2025-09-07T08:31:22.8266030Z Running all tests 2025-09-07T08:31:22.8270220Z Running parallel tests on 3 processes 2025-09-07T08:31:22.8271889Z Name: tests to run (est. time: 0.0min) 2025-09-07T08:31:22.8272405Z Serial tests (0): 2025-09-07T08:31:22.8272729Z Parallel tests (19): 2025-09-07T08:31:22.8272992Z cpp/Dict_test 1/1 2025-09-07T08:31:22.8273456Z cpp/Dimname_test 1/1 2025-09-07T08:31:22.8273735Z cpp/NamedTensor_test 1/1 2025-09-07T08:31:22.8274091Z cpp/apply_utils_test 1/1 2025-09-07T08:31:22.8274357Z cpp/atest 1/1 2025-09-07T08:31:22.8274593Z cpp/basic 1/1 2025-09-07T08:31:22.8274900Z cpp/broadcast_test 1/1 2025-09-07T08:31:22.8275185Z cpp/cpu_generator_test 1/1 2025-09-07T08:31:22.8275510Z cpp/dlconvertor_test 1/1 2025-09-07T08:31:22.8275830Z cpp/extension_backend_test 1/1 2025-09-07T08:31:22.8276138Z cpp/lazy_tensor_test 1/1 2025-09-07T08:31:22.8276481Z cpp/legacy_vmap_test 1/1 2025-09-07T08:31:22.8276867Z cpp/native_test 1/1 2025-09-07T08:31:22.8277204Z cpp/operators_test 1/1 2025-09-07T08:31:22.8277489Z cpp/scalar_tensor_test 1/1 2025-09-07T08:31:22.8277774Z cpp/scalar_test 1/1 2025-09-07T08:31:22.8278100Z cpp/tensor_iterator_test 1/1 2025-09-07T08:31:22.8278411Z cpp/undefined_tensor_test 1/1 2025-09-07T08:31:22.8278773Z cpp/wrapdim_test 1/1 2025-09-07T08:31:22.8279051Z Name: excluded (est. time: 0.0min) 2025-09-07T08:31:22.8279334Z Serial tests (0): 2025-09-07T08:31:22.8279640Z Parallel tests (0): 2025-09-07T08:31:22.8279972Z Running cpp/Dict_test 1/1 ... [2025-09-07 08:31:22.827608] 2025-09-07T08:31:22.8280430Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:22.8290686Z 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-44b9aa4b9e23ca29.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:22.828833] 2025-09-07T08:31:24.5967824Z 2025-09-07T08:31:24.5968975Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_7a0ba1db2b77159b_.log 2025-09-07T08:31:24.5969621Z 2025-09-07T08:31:25.3397496Z Uploading artifacts took 0.74 seconds 2025-09-07T08:31:25.3397982Z Running cpp/Dimname_test 1/1 ... [2025-09-07 08:31:25.339537] 2025-09-07T08:31:25.3398395Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:25.3401630Z 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-75eef7a86e700445.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:25.339927] 2025-09-07T08:31:26.7566291Z 2025-09-07T08:31:26.7567523Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_f89b7a14b7eb91ad_.log 2025-09-07T08:31:26.7568198Z 2025-09-07T08:31:26.7568422Z Running cpp/NamedTensor_test 1/1 ... [2025-09-07 08:31:26.756559] 2025-09-07T08:31:26.7568864Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:26.7571573Z 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-ce1913f314cbc8ab.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:26.756915] 2025-09-07T08:31:28.1736150Z 2025-09-07T08:31:28.1737368Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_8b00d8d390b351f9_.log 2025-09-07T08:31:28.1738072Z 2025-09-07T08:31:28.1738282Z Running cpp/apply_utils_test 1/1 ... [2025-09-07 08:31:28.173528] 2025-09-07T08:31:28.1738716Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:28.1741457Z 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-921394d8a64fa2c8.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:28.173904] 2025-09-07T08:31:29.5405933Z 2025-09-07T08:31:29.5406990Z 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_9eaa86544763dbdc_.log 2025-09-07T08:31:29.5407750Z 2025-09-07T08:31:29.5407914Z Running cpp/atest 1/1 ... [2025-09-07 08:31:29.540514] 2025-09-07T08:31:29.5408299Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:29.5411488Z 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-17b91ea6f24efc6d.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:29.540907] 2025-09-07T08:31:30.9576102Z 2025-09-07T08:31:30.9577104Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_0c0d975e726c8755_.log 2025-09-07T08:31:30.9577747Z 2025-09-07T08:31:30.9578131Z Running cpp/basic 1/1 ... [2025-09-07 08:31:30.957452] 2025-09-07T08:31:30.9578530Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:30.9580316Z 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-a6f0e1789f399bcd.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:30.957809] 2025-09-07T08:31:32.3744856Z 2025-09-07T08:31:32.3745798Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_606d98bd9b0a7bcb_.log 2025-09-07T08:31:32.3746409Z 2025-09-07T08:31:32.3746612Z Running cpp/broadcast_test 1/1 ... [2025-09-07 08:31:32.374365] 2025-09-07T08:31:32.3747037Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:32.3749995Z 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-f53b7ce746b0a8cf.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:32.374740] 2025-09-07T08:31:33.7915429Z 2025-09-07T08:31:33.7916419Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_c1e88d5ca7887548_.log 2025-09-07T08:31:33.7917185Z 2025-09-07T08:31:33.7917416Z Running cpp/cpu_generator_test 1/1 ... [2025-09-07 08:31:33.791489] 2025-09-07T08:31:33.7917858Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:33.7921161Z 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-e1fa54a66b1dad1c.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:33.791828] 2025-09-07T08:31:35.2085645Z 2025-09-07T08:31:35.2086876Z 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_457dab4a837cd4a3_.log 2025-09-07T08:31:35.2087595Z 2025-09-07T08:31:35.2087823Z Running cpp/dlconvertor_test 1/1 ... [2025-09-07 08:31:35.208409] 2025-09-07T08:31:35.2088334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:35.2090240Z 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-f6a3184f3c515309.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:35.208756] 2025-09-07T08:31:36.5755569Z 2025-09-07T08:31:36.5757017Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_65d040e63843dfba_.log 2025-09-07T08:31:36.5757720Z 2025-09-07T08:31:36.5757979Z Running cpp/extension_backend_test 1/1 ... [2025-09-07 08:31:36.575442] 2025-09-07T08:31:36.5758442Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:36.5760774Z 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-c78211af6de5d20d.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:36.575839] 2025-09-07T08:31:37.9924913Z 2025-09-07T08:31:37.9926026Z 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_3fcb587b3d94fd2a_.log 2025-09-07T08:31:37.9926796Z 2025-09-07T08:31:37.9927009Z Running cpp/lazy_tensor_test 1/1 ... [2025-09-07 08:31:37.992367] 2025-09-07T08:31:37.9927427Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:37.9929755Z 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-090aa620dd7c8077.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:37.992740] 2025-09-07T08:31:39.4093559Z 2025-09-07T08:31:39.4094818Z 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_019bda1937c4e8f8_.log 2025-09-07T08:31:39.4095538Z 2025-09-07T08:31:39.4095747Z Running cpp/legacy_vmap_test 1/1 ... [2025-09-07 08:31:39.409255] 2025-09-07T08:31:39.4096176Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:39.4098468Z 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-5b098d9686884786.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:39.409601] 2025-09-07T08:31:40.8262689Z 2025-09-07T08:31:40.8263738Z 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_2484e4e6925083f1_.log 2025-09-07T08:31:40.8264511Z 2025-09-07T08:31:40.8264759Z Running cpp/native_test 1/1 ... [2025-09-07 08:31:40.826189] 2025-09-07T08:31:40.8265167Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:40.8268016Z 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-4996d3ef4307affc.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:40.826569] 2025-09-07T08:31:42.2433008Z 2025-09-07T08:31:42.2434169Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_c016283889724bb7_.log 2025-09-07T08:31:42.2435253Z 2025-09-07T08:31:42.2435559Z Running cpp/operators_test 1/1 ... [2025-09-07 08:31:42.243184] 2025-09-07T08:31:42.2436208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:42.2439312Z 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-66e3843717803e16.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:42.243597] 2025-09-07T08:31:43.6103641Z 2025-09-07T08:31:43.6104630Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_075a10b841f50c37_.log 2025-09-07T08:31:43.6105302Z 2025-09-07T08:31:43.6105535Z Running cpp/scalar_tensor_test 1/1 ... [2025-09-07 08:31:43.610270] 2025-09-07T08:31:43.6106158Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:43.6108758Z 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-4ff516b54a1949ae.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:43.610640] 2025-09-07T08:31:44.9771975Z 2025-09-07T08:31:44.9773154Z 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_bd7d60d04f227fc5_.log 2025-09-07T08:31:44.9774190Z 2025-09-07T08:31:44.9774386Z Running cpp/scalar_test 1/1 ... [2025-09-07 08:31:44.977071] 2025-09-07T08:31:44.9774800Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:44.9777910Z 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-ccddd74a4e313d64.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:44.977526] 2025-09-07T08:31:46.3943497Z 2025-09-07T08:31:46.3944821Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_4a48d96e6236cb80_.log 2025-09-07T08:31:46.3945480Z 2025-09-07T08:31:46.3945697Z Running cpp/tensor_iterator_test 1/1 ... [2025-09-07 08:31:46.394242] 2025-09-07T08:31:46.3946153Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:46.3949018Z 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-2e53cb9b6ee38378.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:46.394646] 2025-09-07T08:31:47.8114382Z 2025-09-07T08:31:47.8115399Z 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_941c74b1d1fcc80e_.log 2025-09-07T08:31:47.8116196Z 2025-09-07T08:31:47.8116447Z Running cpp/undefined_tensor_test 1/1 ... [2025-09-07 08:31:47.811293] 2025-09-07T08:31:47.8116972Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:47.8119387Z 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-bd51303d73fee283.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:47.811667] 2025-09-07T08:31:49.2285175Z 2025-09-07T08:31:49.2286202Z 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_2ed7b7f02802b239_.log 2025-09-07T08:31:49.2286983Z 2025-09-07T08:31:49.2287183Z Running cpp/wrapdim_test 1/1 ... [2025-09-07 08:31:49.228308] 2025-09-07T08:31:49.2287724Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:49.2289380Z 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-299555881b4ef856.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:49.228647] 2025-09-07T08:31:50.5951779Z 2025-09-07T08:31:50.5952981Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_776fdb5b4d86a9c7_.log 2025-09-07T08:31:50.5953649Z 2025-09-07T08:31:52.6691397Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:31:52.6692981Z import pkg_resources 2025-09-07T08:31:52.6698854Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:31:52.6700501Z import pkg_resources 2025-09-07T08:31:52.6907075Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:31:52.6908567Z import pkg_resources 2025-09-07T08:31:52.7884681Z Running cpp/Dict_test 1/1 ... [2025-09-07 08:31:52.788115] 2025-09-07T08:31:52.7885370Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:52.7889332Z 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-d577968bedaafe83.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:52.788545] 2025-09-07T08:31:52.7891859Z Running cpp/Dimname_test 1/1 ... [2025-09-07 08:31:52.788796] 2025-09-07T08:31:52.7892522Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:52.7896579Z 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-e5cde8febd909c70.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:52.789294] 2025-09-07T08:31:52.8111441Z Running cpp/NamedTensor_test 1/1 ... [2025-09-07 08:31:52.810850] 2025-09-07T08:31:52.8112217Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:52.8117122Z 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-71769dc22dd82335.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:52.811327] 2025-09-07T08:31:56.0597936Z 2025-09-07T08:31:56.0599567Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_c02951d1093af9b7_.log 2025-09-07T08:31:56.0601101Z 2025-09-07T08:31:56.5897614Z 2025-09-07T08:31:56.5898968Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_e34c9a46141e64e6_.log 2025-09-07T08:31:56.5900066Z 2025-09-07T08:31:58.9614678Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:31:58.9617582Z import pkg_resources 2025-09-07T08:31:59.0852174Z Running cpp/apply_utils_test 1/1 ... [2025-09-07 08:31:59.084794] 2025-09-07T08:31:59.0852919Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:59.0857141Z 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-f33fb99ccfc852a8.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:59.085252] 2025-09-07T08:31:59.6385074Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:31:59.6390935Z import pkg_resources 2025-09-07T08:31:59.8392934Z Running cpp/atest 1/1 ... [2025-09-07 08:31:59.838850] 2025-09-07T08:31:59.8394124Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:31:59.8400268Z 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-e4dd8e1fde8a905d.xml', '-x', '--reruns=2'] ... [2025-09-07 08:31:59.839649] 2025-09-07T08:32:00.7255802Z 2025-09-07T08:32:00.7256993Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_48518833f179a615_.log 2025-09-07T08:32:00.7257719Z 2025-09-07T08:32:02.0210527Z 2025-09-07T08:32:02.0211960Z 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_f738457cbfd9b862_.log 2025-09-07T08:32:02.0213308Z 2025-09-07T08:32:03.7373870Z 2025-09-07T08:32:03.7375095Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_146f189e5a882989_.log 2025-09-07T08:32:03.7375910Z 2025-09-07T08:32:04.0448293Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:04.0450934Z import pkg_resources 2025-09-07T08:32:04.1668746Z Running cpp/basic 1/1 ... [2025-09-07 08:32:04.166481] 2025-09-07T08:32:04.1671434Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:04.1673865Z 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-5930892c65cfebc2.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:04.166945] 2025-09-07T08:32:05.0593519Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:05.0596388Z import pkg_resources 2025-09-07T08:32:05.1822841Z Running cpp/broadcast_test 1/1 ... [2025-09-07 08:32:05.181514] 2025-09-07T08:32:05.1823592Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:05.1826576Z 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-e7fdfc0ee0cf23cb.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:05.182040] 2025-09-07T08:32:06.7187166Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:06.7190690Z import pkg_resources 2025-09-07T08:32:06.8444341Z Running cpp/cpu_generator_test 1/1 ... [2025-09-07 08:32:06.844023] 2025-09-07T08:32:06.8445101Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:06.8449528Z 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-026ac2d55a34c3cf.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:06.844586] 2025-09-07T08:32:07.0903998Z 2025-09-07T08:32:07.0905411Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_8cbed7e5b1d731e8_.log 2025-09-07T08:32:07.0906867Z 2025-09-07T08:32:07.7095372Z 2025-09-07T08:32:07.7096996Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_3012269a9823f1a0_.log 2025-09-07T08:32:07.7098167Z 2025-09-07T08:32:10.0069250Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:10.0072648Z import pkg_resources 2025-09-07T08:32:10.1311207Z Running cpp/dlconvertor_test 1/1 ... [2025-09-07 08:32:10.130716] 2025-09-07T08:32:10.1312042Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:10.1315887Z 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-bc30de3e69756926.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:10.131226] 2025-09-07T08:32:10.4658781Z 2025-09-07T08:32:10.4660085Z 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_a13bb0174eb57f45_.log 2025-09-07T08:32:10.4662069Z 2025-09-07T08:32:10.7646459Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:10.7648944Z import pkg_resources 2025-09-07T08:32:10.8968603Z Running cpp/extension_backend_test 1/1 ... [2025-09-07 08:32:10.896457] 2025-09-07T08:32:10.8969366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:10.8974475Z 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-c47bbf4bc90985c1.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:10.896976] 2025-09-07T08:32:12.5003174Z 2025-09-07T08:32:12.5004970Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_a338be01c9df696c_.log 2025-09-07T08:32:12.5006376Z 2025-09-07T08:32:13.1158061Z 2025-09-07T08:32:13.1159230Z 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_e5bbd9abd85a9669_.log 2025-09-07T08:32:13.1160477Z 2025-09-07T08:32:13.4378363Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:13.4381009Z import pkg_resources 2025-09-07T08:32:13.5840741Z Running cpp/lazy_tensor_test 1/1 ... [2025-09-07 08:32:13.583679] 2025-09-07T08:32:13.5841688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:13.5845626Z 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-6c009382b7892bcc.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:13.584173] 2025-09-07T08:32:15.4325530Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:15.4327091Z import pkg_resources 2025-09-07T08:32:15.5522414Z 2025-09-07T08:32:15.5523764Z 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_af78f8863ac1a027_.log 2025-09-07T08:32:15.5524544Z 2025-09-07T08:32:15.5591444Z Running cpp/legacy_vmap_test 1/1 ... [2025-09-07 08:32:15.558881] 2025-09-07T08:32:15.5592274Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:15.5596430Z 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-3fd0441c8767c90b.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:15.559301] 2025-09-07T08:32:15.9071194Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:15.9074358Z import pkg_resources 2025-09-07T08:32:16.0324968Z Running cpp/native_test 1/1 ... [2025-09-07 08:32:16.032026] 2025-09-07T08:32:16.0325743Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:16.0329143Z 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-332ffff73e21512a.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:16.032519] 2025-09-07T08:32:18.5020730Z 2025-09-07T08:32:18.5022019Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_253056c56f932d6a_.log 2025-09-07T08:32:18.5023129Z 2025-09-07T08:32:18.6764786Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:18.6766338Z import pkg_resources 2025-09-07T08:32:18.8198731Z Running cpp/operators_test 1/1 ... [2025-09-07 08:32:18.819361] 2025-09-07T08:32:18.8199687Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:18.8201922Z 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-180339f14346519a.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:18.819789] 2025-09-07T08:32:20.2829199Z 2025-09-07T08:32:20.2830331Z 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_144df3b2a746bc54_.log 2025-09-07T08:32:20.2831130Z 2025-09-07T08:32:21.4897535Z 2025-09-07T08:32:21.4898804Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_b47b570c8ac0b1ee_.log 2025-09-07T08:32:21.4899550Z 2025-09-07T08:32:21.8082139Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:21.8083808Z import pkg_resources 2025-09-07T08:32:21.9303191Z Running cpp/scalar_tensor_test 1/1 ... [2025-09-07 08:32:21.929927] 2025-09-07T08:32:21.9303691Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:21.9307171Z 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-921bcfe742d4e5e5.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:21.930395] 2025-09-07T08:32:23.2724745Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:23.2727369Z import pkg_resources 2025-09-07T08:32:23.3953173Z Running cpp/scalar_test 1/1 ... [2025-09-07 08:32:23.394997] 2025-09-07T08:32:23.3953610Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:23.3958209Z 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-7a8bd2b041969926.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:23.395439] 2025-09-07T08:32:24.2150196Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:24.2153135Z import pkg_resources 2025-09-07T08:32:24.2520606Z 2025-09-07T08:32:24.2522403Z 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_86092709a460d1ea_.log 2025-09-07T08:32:24.2523995Z 2025-09-07T08:32:24.3381782Z Running cpp/tensor_iterator_test 1/1 ... [2025-09-07 08:32:24.337636] 2025-09-07T08:32:24.3383452Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:24.3385724Z 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-0ccfe5c15472a213.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:24.338103] 2025-09-07T08:32:25.8146103Z 2025-09-07T08:32:25.8147449Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_2b8d062e2dafc8cd_.log 2025-09-07T08:32:25.8149755Z 2025-09-07T08:32:27.4143925Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:27.4146853Z import pkg_resources 2025-09-07T08:32:27.5392325Z Running cpp/undefined_tensor_test 1/1 ... [2025-09-07 08:32:27.538797] 2025-09-07T08:32:27.5393192Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:27.5397556Z 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-09c274f1aa7772cd.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:27.539309] 2025-09-07T08:32:28.8579494Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:28.8582530Z import pkg_resources 2025-09-07T08:32:29.0150442Z Running cpp/wrapdim_test 1/1 ... [2025-09-07 08:32:29.014588] 2025-09-07T08:32:29.0152060Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:32:29.0162108Z 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-eb168014c03fd85e.xml', '-x', '--reruns=2'] ... [2025-09-07 08:32:29.015787] 2025-09-07T08:32:29.8111839Z 2025-09-07T08:32:29.8113891Z 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_e6c4ca9dac18c773_.log 2025-09-07T08:32:29.8120468Z 2025-09-07T08:32:31.2360908Z 2025-09-07T08:32:31.2362581Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_974d4b4e1dbe0f8e_.log 2025-09-07T08:32:31.2363569Z 2025-09-07T08:32:33.1424419Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:33.1427560Z import pkg_resources 2025-09-07T08:32:33.9707903Z 2025-09-07T08:32:33.9709400Z 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_45da1d8be6823648_.log 2025-09-07T08:32:33.9710520Z 2025-09-07T08:32:34.4800592Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:32:34.4803309Z import pkg_resources 2025-09-07T08:32:35.0579441Z Running test batch 'tests to run' cost 72.23 seconds 2025-09-07T08:32:35.5765819Z + run_if_exists tensor_interop_test 2025-09-07T08:32:35.5766445Z + local test_name=tensor_interop_test 2025-09-07T08:32:35.5767014Z + [[ -x build/bin/tensor_interop_test ]] 2025-09-07T08:32:35.5767644Z + echo 'Warning: tensor_interop_test does not exist.' 2025-09-07T08:32:35.5768277Z Warning: tensor_interop_test does not exist. 2025-09-07T08:32:35.5768813Z + run_if_exists cudnn_test 2025-09-07T08:32:35.5769254Z + local test_name=cudnn_test 2025-09-07T08:32:35.5769715Z + [[ -x build/bin/cudnn_test ]] 2025-09-07T08:32:35.5770149Z + echo 'Warning: cudnn_test does not exist.' 2025-09-07T08:32:35.5770718Z Warning: cudnn_test does not exist. 2025-09-07T08:32:35.5771250Z + run_if_exists cuda_generator_test 2025-09-07T08:32:35.5771745Z + local test_name=cuda_generator_test 2025-09-07T08:32:35.5772529Z + [[ -x build/bin/cuda_generator_test ]] 2025-09-07T08:32:35.5773136Z + echo 'Warning: cuda_generator_test does not exist.' 2025-09-07T08:32:35.5773745Z Warning: cuda_generator_test does not exist. 2025-09-07T08:32:35.5774089Z + run_if_exists apply_test 2025-09-07T08:32:35.5774365Z + local test_name=apply_test 2025-09-07T08:32:35.5774652Z + [[ -x build/bin/apply_test ]] 2025-09-07T08:32:35.5774971Z + echo 'Warning: apply_test does not exist.' 2025-09-07T08:32:35.5775315Z Warning: apply_test does not exist. 2025-09-07T08:32:35.5775612Z + run_if_exists stream_test 2025-09-07T08:32:35.5775892Z + local test_name=stream_test 2025-09-07T08:32:35.5776181Z + [[ -x build/bin/stream_test ]] 2025-09-07T08:32:35.5776500Z + echo 'Warning: stream_test does not exist.' 2025-09-07T08:32:35.5776837Z Warning: stream_test does not exist. 2025-09-07T08:32:35.5777150Z + run_if_exists cuda_half_test 2025-09-07T08:32:35.5777444Z + local test_name=cuda_half_test 2025-09-07T08:32:35.5777746Z + [[ -x build/bin/cuda_half_test ]] 2025-09-07T08:32:35.5778073Z + echo 'Warning: cuda_half_test does not exist.' 2025-09-07T08:32:35.5778440Z Warning: cuda_half_test does not exist. 2025-09-07T08:32:35.5778770Z + run_if_exists cuda_vectorized_test 2025-09-07T08:32:35.5779091Z + local test_name=cuda_vectorized_test 2025-09-07T08:32:35.5779413Z + [[ -x build/bin/cuda_vectorized_test ]] 2025-09-07T08:32:35.5779987Z + echo 'Warning: cuda_vectorized_test does not exist.' 2025-09-07T08:32:35.5780388Z Warning: cuda_vectorized_test does not exist. 2025-09-07T08:32:35.5780748Z + run_if_exists cuda_distributions_test 2025-09-07T08:32:35.5781077Z + local test_name=cuda_distributions_test 2025-09-07T08:32:35.5781432Z + [[ -x build/bin/cuda_distributions_test ]] 2025-09-07T08:32:35.5781828Z + echo 'Warning: cuda_distributions_test does not exist.' 2025-09-07T08:32:35.5782368Z Warning: cuda_distributions_test does not exist. 2025-09-07T08:32:35.5782726Z + run_if_exists cuda_optional_test 2025-09-07T08:32:35.5783048Z + local test_name=cuda_optional_test 2025-09-07T08:32:35.5783382Z + [[ -x build/bin/cuda_optional_test ]] 2025-09-07T08:32:35.5783757Z + echo 'Warning: cuda_optional_test does not exist.' 2025-09-07T08:32:35.5784130Z Warning: cuda_optional_test does not exist. 2025-09-07T08:32:35.5784483Z + run_if_exists cuda_tensor_interop_test 2025-09-07T08:32:35.5784892Z + local test_name=cuda_tensor_interop_test 2025-09-07T08:32:35.5785244Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2025-09-07T08:32:35.5785634Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2025-09-07T08:32:35.5786063Z Warning: cuda_tensor_interop_test does not exist. 2025-09-07T08:32:35.5786428Z + run_if_exists cuda_complex_test 2025-09-07T08:32:35.5786734Z + local test_name=cuda_complex_test 2025-09-07T08:32:35.5787055Z + [[ -x build/bin/cuda_complex_test ]] 2025-09-07T08:32:35.5787412Z + echo 'Warning: cuda_complex_test does not exist.' 2025-09-07T08:32:35.5787791Z Warning: cuda_complex_test does not exist. 2025-09-07T08:32:35.5788136Z + run_if_exists cuda_complex_math_test 2025-09-07T08:32:35.5788477Z + local test_name=cuda_complex_math_test 2025-09-07T08:32:35.5788855Z + [[ -x build/bin/cuda_complex_math_test ]] 2025-09-07T08:32:35.5789245Z + echo 'Warning: cuda_complex_math_test does not exist.' 2025-09-07T08:32:35.5789661Z Warning: cuda_complex_math_test does not exist. 2025-09-07T08:32:35.5790037Z + run_if_exists cuda_cub_test 2025-09-07T08:32:35.5790333Z + local test_name=cuda_cub_test 2025-09-07T08:32:35.5790626Z + [[ -x build/bin/cuda_cub_test ]] 2025-09-07T08:32:35.5790968Z + echo 'Warning: cuda_cub_test does not exist.' 2025-09-07T08:32:35.5791330Z Warning: cuda_cub_test does not exist. 2025-09-07T08:32:35.5791662Z + run_if_exists cuda_atomic_ops_test 2025-09-07T08:32:35.5791978Z + local test_name=cuda_atomic_ops_test 2025-09-07T08:32:35.5792314Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2025-09-07T08:32:35.5792693Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2025-09-07T08:32:35.5793097Z Warning: cuda_atomic_ops_test does not exist. 2025-09-07T08:32:35.5793413Z + '[' ON == ON ']' 2025-09-07T08:32:35.5794167Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2025-09-07T08:32:35.6043476Z ==29953== Memcheck, a memory error detector 2025-09-07T08:32:35.6044056Z ==29953== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2025-09-07T08:32:35.6044738Z ==29953== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2025-09-07T08:32:35.6045243Z ==29953== Command: build/bin/basic --gtest_filter=-*CUDA 2025-09-07T08:32:35.6045659Z ==29953== 2025-09-07T08:32:36.1494709Z ==29953== Warning: set address range perms: large range [0x4a4c000, 0x1a643000) (defined) 2025-09-07T08:32:36.1495437Z ==29953== Warning: set address range perms: large range [0x5c30000, 0x1740f000) (defined) 2025-09-07T08:32:59.5574128Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2025-09-07T08:32:59.5865550Z Note: Google Test filter = -*CUDA 2025-09-07T08:32:59.5916229Z [==========] Running 6 tests from 1 test suite. 2025-09-07T08:32:59.5943327Z [----------] Global test environment set-up. 2025-09-07T08:32:59.6013914Z [----------] 6 tests from BasicTest 2025-09-07T08:32:59.6038034Z [ RUN ] BasicTest.BasicTestCPU 2025-09-07T08:33:00.1517436Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-09-07T08:33:00.1518800Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-09-07T08:33:00.1519297Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-09-07T08:33:00.2027224Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-09-07T08:33:00.2027725Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-09-07T08:33:00.2028200Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-09-07T08:33:00.2741929Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-09-07T08:33:00.2742451Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-09-07T08:33:00.2742931Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-09-07T08:33:01.0616776Z 359 ms 2025-09-07T08:33:01.1486066Z 55 ms 2025-09-07T08:33:01.2218342Z 65 ms 2025-09-07T08:33:01.9070187Z [ OK ] BasicTest.BasicTestCPU (2301 ms) 2025-09-07T08:33:01.9077173Z [ RUN ] BasicTest.BasicTestHalfCPU 2025-09-07T08:33:02.0577653Z 103 ms 2025-09-07T08:33:02.1097683Z 47 ms 2025-09-07T08:33:02.1757854Z 64 ms 2025-09-07T08:33:02.2289667Z [ OK ] BasicTest.BasicTestHalfCPU (319 ms) 2025-09-07T08:33:02.2290100Z [ RUN ] BasicTest.FactoryMethodsTest 2025-09-07T08:33:02.2616442Z [ OK ] BasicTest.FactoryMethodsTest (32 ms) 2025-09-07T08:33:02.2616931Z [ RUN ] BasicTest.BasicStdTestCPU 2025-09-07T08:33:02.3777937Z Simple example: called once 2025-09-07T08:33:02.4252375Z throw: call_once will retry 2025-09-07T08:33:02.4643389Z throw: call_once will retry 2025-09-07T08:33:02.4648177Z Didn't throw, call_once will not attempt again 2025-09-07T08:33:02.4667364Z [ OK ] BasicTest.BasicStdTestCPU (205 ms) 2025-09-07T08:33:02.4667781Z [ RUN ] BasicTest.TestForBlobResizeCPU 2025-09-07T08:33:02.4839517Z [ OK ] BasicTest.TestForBlobResizeCPU (17 ms) 2025-09-07T08:33:02.4839940Z [ RUN ] BasicTest.TestForBlobStridesResizeCPU 2025-09-07T08:33:02.4871808Z [ OK ] BasicTest.TestForBlobStridesResizeCPU (3 ms) 2025-09-07T08:33:02.4893344Z [----------] 6 tests from BasicTest (2884 ms total) 2025-09-07T08:33:02.4893645Z 2025-09-07T08:33:02.4905163Z [----------] Global test environment tear-down 2025-09-07T08:33:02.4933062Z [==========] 6 tests from 1 test suite ran. (2910 ms total) 2025-09-07T08:33:02.4945380Z [ PASSED ] 6 tests. 2025-09-07T08:33:04.4177208Z ==29953== 2025-09-07T08:33:04.4180944Z ==29953== HEAP SUMMARY: 2025-09-07T08:33:04.4181364Z ==29953== in use at exit: 431,636 bytes in 6,391 blocks 2025-09-07T08:33:04.4181872Z ==29953== total heap usage: 649,977 allocs, 643,586 frees, 198,948,750 bytes allocated 2025-09-07T08:33:04.4182313Z ==29953== 2025-09-07T08:33:04.4540340Z ==29953== LEAK SUMMARY: 2025-09-07T08:33:04.4541261Z ==29953== definitely lost: 0 bytes in 0 blocks 2025-09-07T08:33:04.4541655Z ==29953== indirectly lost: 0 bytes in 0 blocks 2025-09-07T08:33:04.4542024Z ==29953== possibly lost: 69,920 bytes in 2 blocks 2025-09-07T08:33:04.4542445Z ==29953== still reachable: 361,716 bytes in 6,389 blocks 2025-09-07T08:33:04.4542847Z ==29953== suppressed: 0 bytes in 0 blocks 2025-09-07T08:33:04.4543308Z ==29953== Rerun with --leak-check=full to see details of leaked memory 2025-09-07T08:33:04.4543721Z ==29953== 2025-09-07T08:33:04.4544035Z ==29953== For lists of detected and suppressed errors, rerun with: -s 2025-09-07T08:33:04.4544576Z ==29953== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2025-09-07T08:33:04.4962406Z + [[ -x build/bin/tensor_interop_test ]] 2025-09-07T08:33:04.4964439Z + [[ -n '' ]] 2025-09-07T08:33:04.4964723Z + assert_git_not_dirty 2025-09-07T08:33:04.4965025Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-09-07T08:33:04.4965396Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-09-07T08:33:04.4972002Z ++ git status --porcelain 2025-09-07T08:33:04.4973771Z ++ grep -v '?? third_party' 2025-09-07T08:33:04.7722640Z ++ true 2025-09-07T08:33:04.7723555Z + git_status= 2025-09-07T08:33:04.7723842Z + [[ -n '' ]] 2025-09-07T08:33:04.7756056Z + sccache_epilogue 2025-09-07T08:33:04.7756620Z + echo '::group::Sccache Compilation Log' 2025-09-07T08:33:04.7757458Z ##[group]Sccache Compilation Log 2025-09-07T08:33:04.7757830Z + echo '=================== sccache compilation log ===================' 2025-09-07T08:33:04.7758261Z =================== sccache compilation log =================== 2025-09-07T08:33:04.7758904Z + python /var/lib/jenkins/workspace/.ci/pytorch/print_sccache_log.py /var/lib/jenkins/sccache_error.log 2025-09-07T08:33:04.7895502Z + echo '=========== If your build fails, please take a look at the log above for possible reasons ===========' 2025-09-07T08:33:04.7896228Z =========== If your build fails, please take a look at the log above for possible reasons =========== 2025-09-07T08:33:04.7896738Z + sccache --show-stats 2025-09-07T08:33:04.7931417Z Compile requests 1550 2025-09-07T08:33:04.7932102Z Compile requests executed 112 2025-09-07T08:33:04.7932572Z Cache hits 75 2025-09-07T08:33:04.7933145Z Cache hits (C/C++) 75 2025-09-07T08:33:04.7933632Z Cache misses 37 2025-09-07T08:33:04.7933985Z Cache misses (C/C++) 37 2025-09-07T08:33:04.7934410Z Cache hits rate 66.96 % 2025-09-07T08:33:04.7934733Z Cache hits rate (C/C++) 66.96 % 2025-09-07T08:33:04.7935219Z Cache timeouts 0 2025-09-07T08:33:04.7935827Z Cache read errors 0 2025-09-07T08:33:04.7936157Z Forced recaches 0 2025-09-07T08:33:04.7936467Z Cache write errors 0 2025-09-07T08:33:04.7936790Z Cache errors 0 2025-09-07T08:33:04.7937117Z Compilations 37 2025-09-07T08:33:04.7937529Z Compilation failures 0 2025-09-07T08:33:04.7937870Z Non-cacheable compilations 0 2025-09-07T08:33:04.7938205Z Non-cacheable calls 2 2025-09-07T08:33:04.7938542Z Non-compilation calls 1436 2025-09-07T08:33:04.7938880Z Unsupported compiler calls 0 2025-09-07T08:33:04.7939210Z Average cache write 0.036 s 2025-09-07T08:33:04.7939547Z Average compiler 12.383 s 2025-09-07T08:33:04.7939881Z Average cache read hit 0.038 s 2025-09-07T08:33:04.7940223Z Failed distributed compilations 0 2025-09-07T08:33:04.7940446Z 2025-09-07T08:33:04.7940544Z Non-cacheable reasons: 2025-09-07T08:33:04.7940814Z -E 2 2025-09-07T08:33:04.7941035Z 2025-09-07T08:33:04.7941273Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-09-07T08:33:04.7941803Z Version (client) 0.10.0 2025-09-07T08:33:04.7942123Z + sccache --stop-server 2025-09-07T08:33:04.7953801Z Stopping sccache server... 2025-09-07T08:33:04.7957182Z Compile requests 1550 2025-09-07T08:33:04.7957832Z Compile requests executed 112 2025-09-07T08:33:04.7958417Z Cache hits 75 2025-09-07T08:33:04.7958776Z Cache hits (C/C++) 75 2025-09-07T08:33:04.7959384Z Cache misses 37 2025-09-07T08:33:04.7959850Z Cache misses (C/C++) 37 2025-09-07T08:33:04.7960223Z Cache hits rate 66.96 % 2025-09-07T08:33:04.7960711Z Cache hits rate (C/C++) 66.96 % 2025-09-07T08:33:04.7961256Z Cache timeouts 0 2025-09-07T08:33:04.7961857Z Cache read errors 0 2025-09-07T08:33:04.7962483Z Forced recaches 0 2025-09-07T08:33:04.7962815Z Cache write errors 0 2025-09-07T08:33:04.7963133Z Cache errors 0 2025-09-07T08:33:04.7963452Z Compilations 37 2025-09-07T08:33:04.7963773Z Compilation failures 0 2025-09-07T08:33:04.7964105Z Non-cacheable compilations 0 2025-09-07T08:33:04.7964512Z Non-cacheable calls 2 2025-09-07T08:33:04.7964840Z Non-compilation calls 1436 2025-09-07T08:33:04.7965178Z Unsupported compiler calls 0 2025-09-07T08:33:04.7965515Z Average cache write 0.036 s 2025-09-07T08:33:04.7965836Z Average compiler 12.383 s 2025-09-07T08:33:04.7966171Z Average cache read hit 0.038 s 2025-09-07T08:33:04.7966510Z Failed distributed compilations 0 2025-09-07T08:33:04.7966733Z 2025-09-07T08:33:04.7966943Z Non-cacheable reasons: 2025-09-07T08:33:04.7967196Z -E 2 2025-09-07T08:33:04.7967415Z 2025-09-07T08:33:04.7967662Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-09-07T08:33:04.7968135Z Version (client) 0.10.0 2025-09-07T08:33:04.7968462Z + echo ::endgroup:: 2025-09-07T08:33:04.7968928Z ##[endgroup] 2025-09-07T08:33:04.7969157Z + cleanup_workspace 2025-09-07T08:33:04.7969649Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2025-09-07T08:33:04.7970509Z sudo may print the following warning message that can be ignored. The chown command will still run. 2025-09-07T08:33:04.7971149Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2025-09-07T08:33:04.7971626Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2025-09-07T08:33:04.7972167Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2025-09-07T08:33:04.7972779Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2025-09-07T08:33:04.7973425Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2025-09-07T08:33:07.6892301Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main 2025-09-07T08:33:07.6892912Z with: 2025-09-07T08:33:07.6893170Z benchmark-results-dir: test/test-reports 2025-09-07T08:33:07.6893504Z dry-run: false 2025-09-07T08:33:07.6893738Z schema-version: v3 2025-09-07T08:33:07.6894212Z github-token: *** 2025-09-07T08:33:07.6894449Z env: 2025-09-07T08:33:07.6894670Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:07.6895133Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:07.6895639Z ##[endgroup] 2025-09-07T08:33:07.6923189Z ##[group]Run set -eux 2025-09-07T08:33:07.6923496Z set -eux 2025-09-07T08:33:07.6923717Z  2025-09-07T08:33:07.6923942Z if [[ -n "" ]]; then 2025-09-07T08:33:07.6924220Z  source "" 2025-09-07T08:33:07.6924564Z fi 2025-09-07T08:33:07.6924914Z python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-09-07T08:33:07.6925404Z  2025-09-07T08:33:07.6925623Z DEVICE_NAME="" 2025-09-07T08:33:07.6925887Z DEVICE_TYPE="" 2025-09-07T08:33:07.6926141Z  2025-09-07T08:33:07.6926383Z if command -v nvidia-smi; then 2025-09-07T08:33:07.6926848Z  # NB: I'm using PyTorch here to get the device name, however, it needs to 2025-09-07T08:33:07.6927445Z  # install the correct version of PyTorch manually for now. Any PyTorch 2025-09-07T08:33:07.6928002Z  # version is fine, I just use 2.7.1 to satify PYPIDEP linter 2025-09-07T08:33:07.6928450Z  python3 -mpip install torch==2.7.1 2025-09-07T08:33:07.6928808Z elif command -v rocminfo; then 2025-09-07T08:33:07.6929253Z  # NB: Installing torch on ROCm runner with pip here causes CI to fail 2025-09-07T08:33:07.6929830Z  # with a memoryview is too large error only on MI300 runners. Is pip 2025-09-07T08:33:07.6930415Z  # version on ROCm runner there too old? As a workaround, let's use the 2025-09-07T08:33:07.6930924Z  # GPU device name coming from rocminfo instead 2025-09-07T08:33:07.6931302Z  DEVICE_NAME=rocm 2025-09-07T08:33:07.6931785Z  DEVICE_TYPE=$(rocminfo | grep "Marketing Name" | tail -n1 | awk -F':' '{print $2}' | xargs) 2025-09-07T08:33:07.6932301Z fi 2025-09-07T08:33:07.6932519Z  2025-09-07T08:33:07.6932796Z echo "DEVICE_NAME=$DEVICE_NAME" >> $GITHUB_ENV 2025-09-07T08:33:07.6933220Z echo "DEVICE_TYPE=$DEVICE_TYPE" >> $GITHUB_ENV 2025-09-07T08:33:07.7034117Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:07.7034514Z env: 2025-09-07T08:33:07.7034742Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:07.7035214Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:07.7035705Z ##[endgroup] 2025-09-07T08:33:07.7067378Z + [[ -n '' ]] 2025-09-07T08:33:07.7067741Z + python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-09-07T08:33:08.0411315Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T08:33:09.1554549Z Collecting boto3==1.35.33 2025-09-07T08:33:09.1704120Z Downloading boto3-1.35.33-py3-none-any.whl (139 kB) 2025-09-07T08:33:09.4844113Z Collecting psutil==7.0.0 2025-09-07T08:33:09.4878983Z Downloading psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (277 kB) 2025-09-07T08:33:09.5232248Z Collecting pynvml==12.0.0 2025-09-07T08:33:09.5262087Z Downloading pynvml-12.0.0-py3-none-any.whl (26 kB) 2025-09-07T08:33:09.5337544Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0) 2025-09-07T08:33:09.5718276Z Collecting s3transfer<0.11.0,>=0.10.0 2025-09-07T08:33:09.5748203Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-09-07T08:33:10.7598385Z Collecting botocore<1.36.0,>=1.35.33 2025-09-07T08:33:10.7630818Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-09-07T08:33:10.9513849Z Collecting nvidia-ml-py<13.0.0a0,>=12.0.0 2025-09-07T08:33:10.9544281Z Downloading nvidia_ml_py-12.575.51-py3-none-any.whl (47 kB) 2025-09-07T08:33:10.9640445Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.1) 2025-09-07T08:33:10.9651205Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10) 2025-09-07T08:33:11.1978498Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0) 2025-09-07T08:33:11.3387666Z Installing collected packages: botocore, s3transfer, nvidia-ml-py, pynvml, psutil, boto3 2025-09-07T08:33:11.8491833Z Attempting uninstall: nvidia-ml-py 2025-09-07T08:33:11.8493211Z Found existing installation: nvidia-ml-py 11.525.84 2025-09-07T08:33:11.8512844Z Uninstalling nvidia-ml-py-11.525.84: 2025-09-07T08:33:11.8864519Z Successfully uninstalled nvidia-ml-py-11.525.84 2025-09-07T08:33:11.9477462Z Attempting uninstall: psutil 2025-09-07T08:33:11.9478160Z Found existing installation: psutil 5.9.8 2025-09-07T08:33:11.9554122Z Uninstalling psutil-5.9.8: 2025-09-07T08:33:11.9568144Z Successfully uninstalled psutil-5.9.8 2025-09-07T08:33:12.1247710Z Successfully installed boto3-1.35.33 botocore-1.35.99 nvidia-ml-py-12.575.51 psutil-7.0.0 pynvml-12.0.0 s3transfer-0.10.4 2025-09-07T08:33:12.2268242Z + DEVICE_NAME= 2025-09-07T08:33:12.2268560Z + DEVICE_TYPE= 2025-09-07T08:33:12.2268792Z + command -v nvidia-smi 2025-09-07T08:33:12.2269130Z + command -v rocminfo 2025-09-07T08:33:12.2269453Z + echo DEVICE_NAME= 2025-09-07T08:33:12.2269695Z + echo DEVICE_TYPE= 2025-09-07T08:33:12.2300084Z ##[group]Run set -eux 2025-09-07T08:33:12.2300359Z set -eux 2025-09-07T08:33:12.2300612Z  2025-09-07T08:33:12.2300862Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-09-07T08:33:12.2301229Z  echo "Missing github-token input" 2025-09-07T08:33:12.2301570Z  exit 1 2025-09-07T08:33:12.2301786Z fi 2025-09-07T08:33:12.2307366Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:12.2307764Z env: 2025-09-07T08:33:12.2307995Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:12.2308490Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:12.2308989Z DEVICE_NAME: 2025-09-07T08:33:12.2309223Z DEVICE_TYPE: 2025-09-07T08:33:12.2309697Z GITHUB_TOKEN: *** 2025-09-07T08:33:12.2309943Z ##[endgroup] 2025-09-07T08:33:12.2333857Z + [[ -z *** ]] 2025-09-07T08:33:12.2394025Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-09-07T08:33:12.2394474Z with: 2025-09-07T08:33:12.2394845Z github-token: *** 2025-09-07T08:33:12.2395080Z env: 2025-09-07T08:33:12.2395318Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:12.2395775Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:12.2396291Z DEVICE_NAME: 2025-09-07T08:33:12.2396524Z DEVICE_TYPE: 2025-09-07T08:33:12.2396765Z ##[endgroup] 2025-09-07T08:33:12.2419944Z ##[group]Run set -eux 2025-09-07T08:33:12.2420319Z set -eux 2025-09-07T08:33:12.2420555Z  2025-09-07T08:33:12.2421039Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-09-07T08:33:12.2426694Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:12.2427082Z env: 2025-09-07T08:33:12.2427306Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:12.2427782Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:12.2428275Z DEVICE_NAME: 2025-09-07T08:33:12.2428507Z DEVICE_TYPE: 2025-09-07T08:33:12.2428893Z GITHUB_TOKEN: *** 2025-09-07T08:33:12.2429271Z ##[endgroup] 2025-09-07T08:33:12.2453529Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 17524754568 i-0dd977e7b70f3c8d7 2025-09-07T08:33:14.7733274Z setting job-id=49774041707 2025-09-07T08:33:14.7733841Z setting job-name=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T08:33:14.7853404Z ##[group]Run set -eux 2025-09-07T08:33:14.7853693Z set -eux 2025-09-07T08:33:14.7853928Z  2025-09-07T08:33:14.7854157Z if [[ -n "" ]]; then 2025-09-07T08:33:14.7854441Z  source "" 2025-09-07T08:33:14.7854671Z fi 2025-09-07T08:33:14.7854884Z  2025-09-07T08:33:14.7855279Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-09-07T08:33:14.7855817Z  --schema-version "${SCHEMA_VERSION}" \ 2025-09-07T08:33:14.7856159Z  --repo "${REPO}" \ 2025-09-07T08:33:14.7856486Z  --head-branch "${HEAD_BRANCH}" \ 2025-09-07T08:33:14.7856826Z  --head-sha "${HEAD_SHA}" \ 2025-09-07T08:33:14.7857178Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-09-07T08:33:14.7857531Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-09-07T08:33:14.7857862Z  --job-id "${JOB_ID}" \ 2025-09-07T08:33:14.7858171Z  --job-name "${JOB_NAME}" 2025-09-07T08:33:14.7863857Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:14.7864244Z env: 2025-09-07T08:33:14.7864454Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:14.7864923Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:14.7865429Z DEVICE_NAME: 2025-09-07T08:33:14.7865660Z DEVICE_TYPE: 2025-09-07T08:33:14.7865885Z SCHEMA_VERSION: v3 2025-09-07T08:33:14.7866145Z REPO: pytorch/pytorch 2025-09-07T08:33:14.7866415Z HEAD_BRANCH: refs/heads/main 2025-09-07T08:33:14.7866756Z HEAD_SHA: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T08:33:14.7867104Z WORKFLOW_RUN_ID: 17524754568 2025-09-07T08:33:14.7867380Z RUN_ATTEMPT: 1 2025-09-07T08:33:14.7867621Z JOB_ID: 49774041707 2025-09-07T08:33:14.7868034Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T08:33:14.7868496Z ##[endgroup] 2025-09-07T08:33:14.7896089Z + [[ -n '' ]] 2025-09-07T08:33:14.7898110Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/main --head-sha 93fb23d6fae7c4e82c4239a1033e522088742634 --workflow-id 17524754568 --run-attempt 1 --job-id 49774041707 --job-name 'linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge)' 2025-09-07T08:33:14.8223115Z ##[group]Run set -eux 2025-09-07T08:33:14.8223394Z set -eux 2025-09-07T08:33:14.8223613Z  2025-09-07T08:33:14.8223855Z if [[ -n "" ]]; then 2025-09-07T08:33:14.8224136Z  source "" 2025-09-07T08:33:14.8224382Z fi 2025-09-07T08:33:14.8224590Z  2025-09-07T08:33:14.8224995Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_runners_info.py" 2025-09-07T08:33:14.8230570Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:14.8231042Z env: 2025-09-07T08:33:14.8231249Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:14.8231717Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:14.8232224Z DEVICE_NAME: 2025-09-07T08:33:14.8232458Z DEVICE_TYPE: 2025-09-07T08:33:14.8232683Z ##[endgroup] 2025-09-07T08:33:14.8255969Z + [[ -n '' ]] 2025-09-07T08:33:14.8659756Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_runners_info.py 2025-09-07T08:33:14.8660735Z INFO:root:Fail to import torch to get the device name 2025-09-07T08:33:14.8762950Z ##[group]Run set -eux 2025-09-07T08:33:14.8763217Z set -eux 2025-09-07T08:33:14.8763512Z  2025-09-07T08:33:14.8763773Z # TODO (huydhn): Implement this part 2025-09-07T08:33:14.8764174Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-09-07T08:33:14.8769858Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:14.8770260Z env: 2025-09-07T08:33:14.8770486Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:14.8770955Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:14.8771454Z DEVICE_NAME: 2025-09-07T08:33:14.8771694Z DEVICE_TYPE: 2025-09-07T08:33:14.8771929Z ##[endgroup] 2025-09-07T08:33:14.8794914Z + echo 'dependencies={}' 2025-09-07T08:33:14.8823786Z ##[group]Run set -eux 2025-09-07T08:33:14.8824074Z set -eux 2025-09-07T08:33:14.8824312Z  2025-09-07T08:33:14.8824520Z if [[ -n "" ]]; then 2025-09-07T08:33:14.8824799Z  source "" 2025-09-07T08:33:14.8825052Z fi 2025-09-07T08:33:14.8825267Z  2025-09-07T08:33:14.8825532Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-09-07T08:33:14.8825984Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-09-07T08:33:14.8826486Z  # We don't want the job to fail if the directory doesn't exist 2025-09-07T08:33:14.8826894Z  exit 0 2025-09-07T08:33:14.8827108Z fi 2025-09-07T08:33:14.8827320Z  2025-09-07T08:33:14.8827557Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-09-07T08:33:14.8828043Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-09-07T08:33:14.8828619Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-09-07T08:33:14.8829050Z  --metadata "${BENCHMARK_METADATA}" \ 2025-09-07T08:33:14.8829417Z  --runners "${RUNNER_INFO}" \ 2025-09-07T08:33:14.8829784Z  --dependencies "${DEPENDENCIES}" \ 2025-09-07T08:33:14.8830124Z  --dry-run 2025-09-07T08:33:14.8830365Z else 2025-09-07T08:33:14.8830757Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-09-07T08:33:14.8831321Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-09-07T08:33:14.8831762Z  --metadata "${BENCHMARK_METADATA}" \ 2025-09-07T08:33:14.8832115Z  --runners "${RUNNER_INFO}" \ 2025-09-07T08:33:14.8832473Z  --dependencies "${DEPENDENCIES}" 2025-09-07T08:33:14.8832800Z fi 2025-09-07T08:33:14.8838797Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:14.8839186Z env: 2025-09-07T08:33:14.8839402Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:14.8839870Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:14.8840374Z DEVICE_NAME: 2025-09-07T08:33:14.8840603Z DEVICE_TYPE: 2025-09-07T08:33:14.8840865Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-09-07T08:33:14.8841188Z DRY_RUN: false 2025-09-07T08:33:14.8842521Z BENCHMARK_METADATA: {"timestamp": 1757233994, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "93fb23d6fae7c4e82c4239a1033e522088742634", "workflow_id": 17524754568, "run_attempt": 1, "job_id": 49774041707} 2025-09-07T08:33:14.8844466Z RUNNER_INFO: [{"cpu_info": "x86_64", "cpu_count": 8, "avail_mem_in_gb": 15, "extra_info": {"hostname": "ip-10-0-22-62.ec2.internal"}, "name": "", "type": ""}] 2025-09-07T08:33:14.8845125Z DEPENDENCIES: {} 2025-09-07T08:33:14.8845374Z ##[endgroup] 2025-09-07T08:33:14.8867021Z + [[ -n '' ]] 2025-09-07T08:33:14.8867276Z + [[ ! -d test/test-reports ]] 2025-09-07T08:33:14.8867573Z + [[ false == \t\r\u\e ]] 2025-09-07T08:33:14.8870518Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1757233994, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "93fb23d6fae7c4e82c4239a1033e522088742634", "workflow_id": 17524754568, "run_attempt": 1, "job_id": 49774041707}' --runners '[{"cpu_info": "x86_64", "cpu_count": 8, "avail_mem_in_gb": 15, "extra_info": {"hostname": "ip-10-0-22-62.ec2.internal"}, "name": "", "type": ""}]' --dependencies '{}' 2025-09-07T08:33:15.0625756Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'test_reductions'}, {'test_file': 'dynamo/cpython/3_13/test_sort'}, {'test_file': 'test_ci_sanity_check_fail'}, {'test_file': 'higher_order_ops/test_with_effects'}, {'test_file': 'dynamo/test_functions'}, {'test_file': 'test_openreg'}, {'test_file': 'dynamo/test_dynamic_shapes'}, {'test_file': 'dynamo/test_utils'}, {'test_file': 'dynamo/test_repros'}, {'test_file': 'test_jit_fuser_te'}, {'test_file': 'test_type_hints'}, {'test_file': 'profiler/test_profiler'}, {'test_file': 'test_numpy_interop'}, {'test_file': 'test_nestedtensor'}, {'test_file': 'test_jit'}, {'test_file': 'dynamo/test_modes'}, {'test_file': 'test_cpp_extensions_mtia_backend'}, {'test_file': 'test_content_store'}, {'test_file': 'dynamo/test_structured_trace'}, {'test_file': 'test_cpp_extensions_stream_and_event'}, {'test_file': 'functorch/test_eager_transforms'}, {'test_file': 'dynamo/test_aot_autograd_cache'}, {'test_file': 'test_python_dispatch'}, {'test_file': 'test_tensor_creation_ops'}, {'test_file': 'dynamo/test_trace_rules'}, {'test_file': 'dynamo/test_package'}, {'test_file': 'torch_np/numpy_tests/core/test_indexing'}, {'test_file': 'nn/test_parametrization'}, {'test_file': 'test_autograd'}, {'test_file': 'functorch/test_control_flow'}, {'test_file': 'test_testing'}, {'test_file': 'test_indexing'}, {'test_file': 'dynamo/test_subclasses'}, {'test_file': 'test_cpp_extensions_jit'}, {'test_file': 'test_nn'}, {'test_file': 'test_overrides'}, {'test_file': 'test_type_promotion'}, {'test_file': 'test_quantization'}, {'test_file': 'dynamo/cpython/3_13/test_generator_stop'}, {'test_file': 'dynamo/cpython/3_13/test_exception_variations'}, {'test_file': 'dynamo/cpython/3_13/test_int_literal'}, {'test_file': 'dynamo/cpython/3_13/test_with'}, {'test_file': 'dynamo/cpython/3_13/test_contextlib'}, {'test_file': 'dynamo/cpython/3_13/test_raise'}, {'test_file': 'dynamo/cpython/3_13/test_ordered_dict'}, {'test_file': 'dynamo/cpython/3_13/test_numeric_tower'}, {'test_file': 'dynamo/cpython/3_13/test_collections'}, {'test_file': 'dynamo/cpython/3_13/test_heapq'}, {'test_file': 'dynamo/cpython/3_13/test_exceptions'}, {'test_file': 'dynamo/cpython/3_13/test_itertools'}, {'test_file': 'dynamo/cpython/3_13/test_int'}, {'test_file': 'dynamo/cpython/3_13/test_baseexception'}, {'test_file': 'dynamo/cpython/3_13/test_operator'}, {'test_file': 'dynamo/cpython/3_13/test_userlist'}, {'test_file': 'dynamo/cpython/3_13/test_sys'}, {'test_file': 'dynamo/cpython/3_13/test_userdict'}, {'test_file': 'dynamo/cpython/3_13/test_generators'}, {'test_file': 'dynamo/cpython/3_13/test_math'}, {'test_file': 'dynamo/cpython/3_13/test_range'}, {'test_file': 'dynamo/cpython/3_13/test_defaultdict'}, {'test_file': 'dynamo/cpython/3_13/test_iter'}, {'test_file': 'dynamo/cpython/3_13/test_set'}, {'test_file': 'dynamo/cpython/3_13/test_bool'}, {'test_file': 'dynamo/cpython/3_13/test_cmath'}, {'test_file': 'dynamo/cpython/3_13/test_dict'}, {'test_file': 'dynamo/cpython/3_13/test_float'}, {'test_file': 'dynamo/cpython/3_13/test_list'}, {'test_file': 'dynamo/cpython/3_13/test_complex'}, {'test_file': 'dynamo/cpython/3_13/test_tuple'}, {'test_file': 'test_package'}, {'test_file': 'test_autoload'}, {'test_file': 'dynamo/test_deque_reconstruct'}, {'test_file': 'test_utils_config_module'}, {'test_file': 'test_mkl_verbose'}, {'test_file': 'dynamo/cpython/3_13/test_unittest/test_assertions'}, {'test_file': 'test_comparison_utils'}, {'test_file': 'test_license'}, {'test_file': 'dynamo/test_base_output'}, {'test_file': 'test_mkldnn_verbose'}, {'test_file': 'cpp_extensions/torch_stable_test_extension/torch_stable_test/test_torch_stable'}, {'test_file': 'test_extension_utils'}, {'test_file': 'test_rename_privateuse1_to_existing_device'}, {'test_file': 'dynamo/test_skip_guard_eval_unsafe'}, {'test_file': 'dynamo/test_buffers_override'}, {'test_file': 'test_custom_ops'}, {'test_file': 'functorch/test_ac_logging'}, {'test_file': 'dynamo/test_resume'}, {'test_file': 'test_ao_sparsity'}, {'test_file': 'test_cpp_api_parity'}, {'test_file': 'dynamo/test_nops'}, {'test_file': 'torch_np/test_nep50_examples'}, {'test_file': 'torch_np/test_binary_ufuncs'}, {'test_file': 'test_hop_infra'}, {'test_file': 'torch_np/test_unary_ufuncs'}, {'test_file': 'typing/test_python_operators'}, {'test_file': 'dynamo/test_modules'}, {'test_file': 'test_transformers'}, {'test_file': 'dynamo/test_global'}, {'test_file': 'test_foreach'}, {'test_file': 'test_appending_byte_serializer'}, {'test_file': 'test_fx_experimental'}, {'test_file': 'test_file_check'}, {'test_file': 'dynamo/test_interop'}, {'test_file': 'dynamo/test_metrics_context'}, {'test_file': 'test_functionalization'}, {'test_file': 'dynamo/test_inline_and_install'}, {'test_file': 'torch_np/test_ufuncs_basic'}, {'test_file': 'test_proxy_tensor'}, {'test_file': 'dynamo/test_skip_non_tensor'}, {'test_file': 'dynamo/test_frame_init'}, {'test_file': 'test_fx'}, {'test_file': 'torch_np/test_dtype'}, {'test_file': 'test_typing'}, {'test_file': 'test_transformers_privateuse1'}, {'test_file': 'functorch/test_aot_joint_with_descriptors'}, {'test_file': 'test_utils_filelock'}, {'test_file': 'backends/xeon/test_launch'}, {'test_file': 'dynamo/test_dicts'}, {'test_file': 'dynamo/test_sdpa'}, {'test_file': 'dynamo/test_list'}, {'test_file': 'test_flop_counter'}, {'test_file': 'xpu/test_fusion'}, {'test_file': 'dynamo/test_fx_graph_runnable'}, {'test_file': 'dynamo/test_recompiles'}, {'test_file': 'test_per_overload_api'}, {'test_file': 'test_pytree'}, {'test_file': 'dynamo/test_nested_graph_breaks'}, {'test_file': 'dynamo/test_pre_dispatch'}, {'test_file': 'dynamo/test_fx_passes_pre_grad'}, {'test_file': 'dynamo/test_subgraphs'}, {'test_file': 'profiler/test_kineto'}, {'test_file': 'test_logging'}, {'test_file': 'test_expanded_weights'}, {'test_file': 'torch_np/test_random'}, {'test_file': 'dynamo/test_reconstruct'}, {'test_file': 'test_compile_benchmark_util'}, {'test_file': 'higher_order_ops/test_invoke_subgraph'}, {'test_file': 'test_optim'}, {'test_file': 'test_namedtensor'}, {'test_file': 'dynamo/test_autograd_function'}, {'test_file': 'dynamo/test_config'}, {'test_file': 'dynamo/test_compile'}, {'test_file': 'test_openmp'}, {'test_file': 'functorch/test_ops'}, {'test_file': 'test_import_stats'}, {'test_file': 'test_binary_ufuncs'}, {'test_file': 'lazy/test_bindings'}, {'test_file': 'test_fx_passes'}, {'test_file': 'cpp_extensions/python_agnostic_extension/test/test_python_agnostic'}, {'test_file': 'torch_np/numpy_tests/core/test_scalarinherit'}, {'test_file': 'test_show_pickle'}, {'test_file': 'test_native_functions'}, {'test_file': 'test_utils'}, {'test_file': 'dynamo/test_install_free_tensors'}, {'test_file': 'dynamo/test_graph_region_tracker'}, {'test_file': 'dynamo/test_pgo'}, {'test_file': 'dynamo/test_export'}, {'test_file': 'test_hub'}, {'test_file': 'dynamo/test_view'}, {'test_file': 'test_module_tracker'}, {'test_file': 'dynamo/test_after_aot'}, {'test_file': 'test_complex'}, {'test_file': 'test_meta'}, {'test_file': 'xpu/test_gemm'}, {'test_file': 'test_tensorexpr'}, {'test_file': 'higher_order_ops/test_invoke_quant'}, {'test_file': 'test_cuda_expandable_segments'}, {'test_file': 'dynamo/test_unittest'}, {'test_file': 'dynamo/test_guard_serialization'}, {'test_file': 'functorch/test_minifier'}, {'test_file': 'test_legacy_vmap'}, {'test_file': 'dynamo/test_cudagraphs_expandable_segments'}, {'test_file': 'test_multiprocessing'}, {'test_file': 'torch_np/numpy_tests/core/test_einsum'}, {'test_file': 'dynamo/test_model_output'}, {'test_file': 'torch_np/test_basic'}, {'test_file': 'test_segment_reductions'}, {'test_file': 'test_ops_fwd_gradients'}, {'test_file': 'test_dispatch'}, {'test_file': 'test_pruning_op'}, {'test_file': 'test_tensorexpr_pybind'}, {'test_file': 'dynamo/test_misc'}, {'test_file': 'dynamo/test_sets'}, {'test_file': 'dynamo/test_backward_higher_order_ops'}, {'test_file': 'dynamo/test_export_mutations'}, {'test_file': 'xpu/test_conv'}, {'test_file': 'test_ops_jit'}, {'test_file': 'nn/test_multihead_attention'}, {'test_file': 'distributions/test_constraints'}, {'test_file': 'functorch/test_ac_knapsack'}, {'test_file': 'profiler/test_record_function'}, {'test_file': 'test_ops_gradients'}, {'test_file': 'functorch/test_vmap'}, {'test_file': 'dynamo/test_flat_apply'}, {'test_file': 'test_jiterator'}, {'test_file': 'lazy/test_step_closures'}, {'test_file': 'test_namedtuple_return_api'}, {'test_file': 'test_monitor'}, {'test_file': 'functorch/test_logging'}, {'test_file': 'test_stateless'}, {'test_file': 'torch_np/numpy_tests/core/test_numeric'}, {'test_file': 'test_weak'}, {'test_file': 'test_jit_disabled'}, {'test_file': 'dynamo/test_optimizers'}, {'test_file': 'functorch/test_ac'}, {'test_file': 'dynamo/test_profiler'}, {'test_file': 'optim/test_lrscheduler'}, {'test_file': 'test_fake_tensor'}, {'test_file': 'dynamo/test_sources'}, {'test_file': 'test_cuda_trace'}, {'test_file': 'dynamo/test_base_hop'}, {'test_file': 'dynamo/test_backends'}, {'test_file': 'dynamo/test_verify_correctness'}, {'test_file': 'dynamo/test_python_dispatcher'}, {'test_file': 'test_set_default_mobile_cpu_allocator'}, {'test_file': 'torch_np/test_indexing'}, {'test_file': 'torch_np/test_scalars_0D_arrays'}, {'test_file': 'test_numba_integration'}, {'test_file': 'dynamo/test_cudagraphs'}, {'test_file': 'dynamo/test_deviceguard'}, {'test_file': 'torch_np/numpy_tests/lib/test_function_base'}, {'test_file': 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'nn/test_init'}, {'test_file': 'torch_np/numpy_tests/lib/test_arraysetops'}, {'test_file': 'test_functional_autograd_benchmark'}, {'test_file': 'test_function_schema'}, {'test_file': 'test_cuda_multigpu'}, {'test_file': 'test_sparse'}, {'test_file': 'test_mobile_optimizer'}, {'test_file': 'torch_np/test_reductions'}, {'test_file': 'test_dlpack'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_ctors'}, {'test_file': 'profiler/test_profiler_tree'}, {'test_file': 'test_spectral_ops'}, {'test_file': 'test_prims'}, {'test_file': 'test_jit_autocast'}, {'test_file': 'profiler/test_torch_tidy'}, {'test_file': 'profiler/test_python_tracer'}, {'test_file': 'lazy/test_reuse_ir'}, {'test_file': 'distributions/test_distributions'}, {'test_file': 'doctests'}, {'test_file': 'test_autoload_disable'}, {'test_file': 'test_autoload_enable'}, {'test_file': 'test_cpp_extensions_aot_ninja'}, {'test_file': 'test_cpp_extensions_aot_no_ninja'}], 'excluded': []} from test/test-reports/td_exclusions-98eb863671460f6d5bf9.json is not a benchmark record, skipping 2025-09-07T08:33:15.0678774Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T08:33:15.0682773Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/Dict_test'}, {'test_file': 'cpp/Dimname_test'}, {'test_file': 'cpp/NamedTensor_test'}, {'test_file': 'cpp/apply_utils_test'}, {'test_file': 'cpp/atest'}, {'test_file': 'cpp/basic'}, {'test_file': 'cpp/broadcast_test'}, {'test_file': 'cpp/cpu_generator_test'}, {'test_file': 'cpp/dlconvertor_test'}, {'test_file': 'cpp/extension_backend_test'}, {'test_file': 'cpp/lazy_tensor_test'}, {'test_file': 'cpp/legacy_vmap_test'}, {'test_file': 'cpp/native_test'}, {'test_file': 'cpp/operators_test'}, {'test_file': 'cpp/scalar_tensor_test'}, {'test_file': 'cpp/scalar_test'}, {'test_file': 'cpp/tensor_iterator_test'}, {'test_file': 'cpp/undefined_tensor_test'}, {'test_file': 'cpp/wrapdim_test'}], 'excluded': []} from test/test-reports/td_exclusions-64a689b9857ab65bc931.json is not a benchmark record, skipping 2025-09-07T08:33:15.0686715Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T08:33:15.0838798Z ##[group]Run cat test/**/*_toprint.log || true 2025-09-07T08:33:15.0839229Z cat test/**/*_toprint.log || true 2025-09-07T08:33:15.0845042Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:15.0859291Z env: 2025-09-07T08:33:15.0859562Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:15.0860061Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:15.0860567Z DEVICE_NAME: 2025-09-07T08:33:15.0860804Z DEVICE_TYPE: 2025-09-07T08:33:15.0861037Z ##[endgroup] 2025-09-07T08:33:15.0946683Z cat: 'test/**/*_toprint.log': No such file or directory 2025-09-07T08:33:15.0982395Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-09-07T08:33:15.0982768Z kill "$MONITOR_SCRIPT_PID" 2025-09-07T08:33:15.0987911Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:15.0988284Z env: 2025-09-07T08:33:15.0988507Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:15.0988976Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:15.0989494Z DEVICE_NAME: 2025-09-07T08:33:15.0989714Z DEVICE_TYPE: 2025-09-07T08:33:15.0989964Z MONITOR_SCRIPT_PID: 42835 2025-09-07T08:33:15.0990243Z ##[endgroup] 2025-09-07T08:33:15.1136538Z Prepare all required actions 2025-09-07T08:33:15.1136991Z Getting action download info 2025-09-07T08:33:15.2348082Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-09-07T08:33:15.4635280Z Download action repository 'actions/upload-artifact@v4' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-09-07T08:33:15.8929148Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-09-07T08:33:15.8929521Z with: 2025-09-07T08:33:15.8929853Z file-suffix: test-dynamo_wrapped-1-3-linux.2xlarge_49774041707 2025-09-07T08:33:15.8930266Z s3-bucket: gha-artifacts 2025-09-07T08:33:15.8930530Z env: 2025-09-07T08:33:15.8930743Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:15.8931207Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:15.8931706Z DEVICE_NAME: 2025-09-07T08:33:15.8931935Z DEVICE_TYPE: 2025-09-07T08:33:15.8932162Z ##[endgroup] 2025-09-07T08:33:15.8981493Z ##[group]Run # Remove any previous test jsons if they exist 2025-09-07T08:33:15.8982043Z # Remove any previous test jsons if they exist 2025-09-07T08:33:15.8982415Z rm -f test-jsons-*.zip 2025-09-07T08:33:15.8982852Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-09-07T08:33:15.8988445Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:15.8988828Z env: 2025-09-07T08:33:15.8989035Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:15.8989503Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:15.8990009Z DEVICE_NAME: 2025-09-07T08:33:15.8990237Z DEVICE_TYPE: 2025-09-07T08:33:15.8990575Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_49774041707 2025-09-07T08:33:15.8990960Z ##[endgroup] 2025-09-07T08:33:15.9134460Z adding: test/test-reports/td_exclusions-98eb863671460f6d5bf9.json (deflated 81%) 2025-09-07T08:33:15.9135231Z adding: test/test-reports/td_exclusions-64a689b9857ab65bc931.json (deflated 73%) 2025-09-07T08:33:15.9160418Z ##[group]Run # Remove any previous test reports if they exist 2025-09-07T08:33:15.9160906Z # Remove any previous test reports if they exist 2025-09-07T08:33:15.9161305Z rm -f test-reports-*.zip 2025-09-07T08:33:15.9161793Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2025-09-07T08:33:15.9167345Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:15.9167729Z env: 2025-09-07T08:33:15.9167953Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:15.9168419Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:15.9168928Z DEVICE_NAME: 2025-09-07T08:33:15.9169145Z DEVICE_TYPE: 2025-09-07T08:33:15.9169482Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_49774041707 2025-09-07T08:33:15.9169894Z ##[endgroup] 2025-09-07T08:33:15.9286635Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-e76a468edf0af46c.xml (deflated 97%) 2025-09-07T08:33:15.9287578Z adding: test/test-reports/python-pytest/test_openreg/test_openreg-5e0b480fec9f2203.xml (deflated 86%) 2025-09-07T08:33:15.9301847Z adding: test/test-reports/python-pytest/test_tensor_creation_ops/test_tensor_creation_ops-816fe28915e76a90.xml (deflated 92%) 2025-09-07T08:33:15.9317717Z adding: test/test-reports/python-pytest/test_nn/test_nn-31caa808ff9ef03f.xml (deflated 95%) 2025-09-07T08:33:15.9333252Z adding: test/test-reports/python-pytest/test_nn/test_nn-6915922c06f5e615.xml (deflated 95%) 2025-09-07T08:33:15.9363216Z adding: test/test-reports/python-pytest/test_fx/test_fx-8a3c36208b3ecf08.xml (deflated 96%) 2025-09-07T08:33:15.9364367Z adding: test/test-reports/python-pytest/test_transformers_privateuse1/test_transformers_privateuse1-4af6be3fe8db81e8.xml (deflated 69%) 2025-09-07T08:33:15.9365447Z adding: test/test-reports/python-pytest/test_show_pickle/test_show_pickle-01d8151de424424a.xml (deflated 37%) 2025-09-07T08:33:15.9481271Z adding: test/test-reports/python-pytest/test_utils/test_utils-a08c114ae214b2eb.xml (deflated 98%) 2025-09-07T08:33:15.9482470Z adding: test/test-reports/python-pytest/test_tensorexpr/test_tensorexpr-38d4e62530633a30.xml (deflated 95%) 2025-09-07T08:33:15.9485051Z adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-f33142cdfe03fd99.xml (deflated 90%) 2025-09-07T08:33:15.9486075Z adding: test/test-reports/python-pytest/test_dispatch/test_dispatch-471aad072c8ebcc2.xml (deflated 76%) 2025-09-07T08:33:15.9487612Z adding: test/test-reports/python-pytest/test_namedtuple_return_api/test_namedtuple_return_api-894e79f29a9e325b.xml (deflated 72%) 2025-09-07T08:33:15.9488632Z adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-07446e0f94263ada.xml (deflated 56%) 2025-09-07T08:33:15.9496169Z adding: test/test-reports/python-pytest/test_fake_tensor/test_fake_tensor-2661f3ad3cc35c29.xml (deflated 93%) 2025-09-07T08:33:15.9497581Z adding: test/test-reports/python-pytest/test_autograd_fallback/test_autograd_fallback-fdd3b0f47559f685.xml (deflated 86%) 2025-09-07T08:33:15.9498938Z adding: test/test-reports/python-pytest/test_autocast/test_autocast-d3c64bd046530f0e.xml (deflated 86%) 2025-09-07T08:33:15.9513375Z 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test/test-reports/dynamo.test_reorder_logs_1.1_e8abc4415ba12c3a_.log (deflated 35%) 2025-09-07T08:33:16.0503820Z adding: test/test-reports/dynamo.test_exc_1.1_c56f55161b893a04_.log (deflated 35%) 2025-09-07T08:33:16.0504503Z adding: test/test-reports/dynamo.test_minifier_1.1_82bd9b4494cad564_.log (deflated 35%) 2025-09-07T08:33:16.0505228Z adding: test/test-reports/dynamo.test_guard_manager_1.1_5c90800307b14329_.log (deflated 35%) 2025-09-07T08:33:16.0506063Z adding: test/test-reports/dynamo.test_bytecode_utils_1.1_7949b93e8d17f935_.log (deflated 35%) 2025-09-07T08:33:16.0506801Z adding: test/test-reports/dynamo.test_generator_1.1_47efcd35ec3b8a74_.log (deflated 35%) 2025-09-07T08:33:16.0507495Z adding: test/test-reports/test_unary_ufuncs_3.3_a4d47f56ca32230b_.log (deflated 49%) 2025-09-07T08:33:16.0508171Z adding: test/test-reports/test_cuda_multigpu_1.1_017521899df6f5f7_.log (deflated 49%) 2025-09-07T08:33:16.0508835Z adding: 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test/test-reports/dynamo.test_nops_1.1_7a60b57c2d1bed22_.log (deflated 35%) 2025-09-07T08:33:16.0515476Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_2ed7b7f02802b239_.log (deflated 73%) 2025-09-07T08:33:16.0516251Z adding: test/test-reports/test_appending_byte_serializer_1.1_b3db1e66bdd5055f_.log (deflated 35%) 2025-09-07T08:33:16.0517043Z adding: test/test-reports/dynamo.test_inline_and_install_1.1_6d9d2f316b68ccd8_.log (deflated 35%) 2025-09-07T08:33:16.0517795Z adding: test/test-reports/dynamo.test_dicts_1.1_5fab43ab7a85bc25_.log (deflated 35%) 2025-09-07T08:33:16.0518481Z adding: test/test-reports/cpp.operators_test_1.1_075a10b841f50c37_.log (deflated 73%) 2025-09-07T08:33:16.0519209Z adding: test/test-reports/cpp.extension_backend_test_1.1_e5bbd9abd85a9669_.log (deflated 72%) 2025-09-07T08:33:16.0519921Z adding: test/test-reports/xpu.test_fusion_1.1_33de7571664d0d05_.log (deflated 49%) 2025-09-07T08:33:16.0520638Z adding: test/test-reports/dynamo.test_nested_graph_breaks_1.1_16c7b0e9a1faed41_.log (deflated 35%) 2025-09-07T08:33:16.0521398Z adding: test/test-reports/dynamo.test_subgraphs_1.1_d2ca4e9bc2ab7add_.log (deflated 35%) 2025-09-07T08:33:16.0522116Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_bd7d60d04f227fc5_.log (deflated 73%) 2025-09-07T08:33:16.0522808Z adding: test/test-reports/cpp.Dimname_test_1.1_c02951d1093af9b7_.log (deflated 73%) 2025-09-07T08:33:16.0523482Z adding: test/test-reports/dynamo.test_config_1.1_ec8a6271637cc932_.log (deflated 35%) 2025-09-07T08:33:16.0524216Z adding: test/test-reports/dynamo.test_install_free_tensors_1.1_37d09e93002f4940_.log (deflated 35%) 2025-09-07T08:33:16.0525044Z adding: test/test-reports/dynamo.test_export_1.1_913f6f339557cddf_.log (deflated 35%) 2025-09-07T08:33:16.0525728Z adding: test/test-reports/cpp.scalar_test_1.1_4a48d96e6236cb80_.log (deflated 73%) 2025-09-07T08:33:16.0526369Z adding: 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(deflated 35%) 2025-09-07T08:33:16.0532790Z adding: test/test-reports/cpp.wrapdim_test_1.1_776fdb5b4d86a9c7_.log (deflated 73%) 2025-09-07T08:33:16.0533443Z adding: test/test-reports/cpp.Dict_test_1.1_48518833f179a615_.log (deflated 84%) 2025-09-07T08:33:16.0534127Z adding: test/test-reports/dynamo.test_debug_utils_1.1_5e9fb8feac1c096a_.log (deflated 35%) 2025-09-07T08:33:16.0534890Z adding: test/test-reports/dynamo.test_graph_deduplication_1.1_c8bbf23fc620e447_.log (deflated 35%) 2025-09-07T08:33:16.0535676Z adding: test/test-reports/dynamo.test_higher_order_ops_1.1_267298a435dc9a99_.log (deflated 35%) 2025-09-07T08:33:16.0536418Z adding: test/test-reports/dynamo.test_decorators_1.1_293d07fdebac3912_.log (deflated 35%) 2025-09-07T08:33:16.0537148Z adding: test/test-reports/dynamo.test_aot_compile_1.1_dd6ffe8d3499df79_.log (deflated 35%) 2025-09-07T08:33:16.0537893Z adding: test/test-reports/dynamo.test_reorder_logs_1.1_e46329ab61a85f1c_.log (deflated 35%) 2025-09-07T08:33:16.0538594Z adding: test/test-reports/dynamo.test_exc_1.1_56cd3fcdb8a50666_.log (deflated 35%) 2025-09-07T08:33:16.0539276Z adding: test/test-reports/dynamo.test_minifier_1.1_644d48bf91bf9805_.log (deflated 35%) 2025-09-07T08:33:16.0539998Z adding: test/test-reports/dynamo.test_guard_manager_1.1_54e1130f06e4fba7_.log (deflated 35%) 2025-09-07T08:33:16.0540749Z adding: test/test-reports/dynamo.test_bytecode_utils_1.1_4951c524b9e9dcc2_.log (deflated 35%) 2025-09-07T08:33:16.0541474Z adding: test/test-reports/dynamo.test_generator_1.1_b7f693e0527b397f_.log (deflated 35%) 2025-09-07T08:33:16.0542196Z adding: test/test-reports/test_cuda_multigpu_1.1_3ac42540b2351412_.log (deflated 49%) 2025-09-07T08:33:16.0667329Z adding: test/test-reports/test_unary_ufuncs_3.3_d124d56c5a1f45db_.log (deflated 96%) 2025-09-07T08:33:16.0668600Z adding: test/test-reports/cpp.Dict_test_1.1_7a0ba1db2b77159b_.log (deflated 73%) 2025-09-07T08:33:16.0669843Z adding: test/test-reports/cpp.Dimname_test_1.1_f89b7a14b7eb91ad_.log (deflated 73%) 2025-09-07T08:33:16.0671165Z adding: test/test-reports/cpp.NamedTensor_test_1.1_8b00d8d390b351f9_.log (deflated 73%) 2025-09-07T08:33:16.0672468Z adding: test/test-reports/cpp.apply_utils_test_1.1_9eaa86544763dbdc_.log (deflated 73%) 2025-09-07T08:33:16.0673873Z adding: test/test-reports/cpp.atest_1.1_0c0d975e726c8755_.log (deflated 73%) 2025-09-07T08:33:16.0674893Z adding: test/test-reports/cpp.basic_1.1_606d98bd9b0a7bcb_.log (deflated 73%) 2025-09-07T08:33:16.0676128Z adding: test/test-reports/cpp.broadcast_test_1.1_c1e88d5ca7887548_.log (deflated 73%) 2025-09-07T08:33:16.0677436Z adding: test/test-reports/cpp.cpu_generator_test_1.1_457dab4a837cd4a3_.log (deflated 73%) 2025-09-07T08:33:16.0678704Z adding: test/test-reports/cpp.dlconvertor_test_1.1_65d040e63843dfba_.log (deflated 73%) 2025-09-07T08:33:16.0679444Z adding: test/test-reports/cpp.extension_backend_test_1.1_3fcb587b3d94fd2a_.log (deflated 73%) 2025-09-07T08:33:16.0680607Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_019bda1937c4e8f8_.log (deflated 73%) 2025-09-07T08:33:16.0681300Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_2484e4e6925083f1_.log (deflated 73%) 2025-09-07T08:33:16.0681981Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_144df3b2a746bc54_.log (deflated 82%) 2025-09-07T08:33:16.0682676Z adding: test/test-reports/cpp.operators_test_1.1_b47b570c8ac0b1ee_.log (deflated 73%) 2025-09-07T08:33:16.0683486Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_86092709a460d1ea_.log (deflated 74%) 2025-09-07T08:33:16.0684217Z adding: test/test-reports/cpp.scalar_test_1.1_2b8d062e2dafc8cd_.log (deflated 73%) 2025-09-07T08:33:16.0685006Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_e6c4ca9dac18c773_.log (deflated 72%) 2025-09-07T08:33:16.0685841Z adding: test/test-reports/cpp.wrapdim_test_1.1_974d4b4e1dbe0f8e_.log (deflated 72%) 2025-09-07T08:33:16.0686554Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_45da1d8be6823648_.log (deflated 88%) 2025-09-07T08:33:16.0710304Z ##[group]Run # Remove any previous debugging artifacts if they exist 2025-09-07T08:33:16.0710835Z # Remove any previous debugging artifacts if they exist 2025-09-07T08:33:16.0711248Z rm -f debug-*.zip 2025-09-07T08:33:16.0711542Z if [ -d 'test/debug' ]; then 2025-09-07T08:33:16.0711896Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-09-07T08:33:16.0712244Z fi 2025-09-07T08:33:16.0717418Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:16.0717804Z env: 2025-09-07T08:33:16.0718016Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:16.0718493Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:16.0719001Z DEVICE_NAME: 2025-09-07T08:33:16.0719237Z DEVICE_TYPE: 2025-09-07T08:33:16.0719634Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_49774041707 2025-09-07T08:33:16.0720041Z ##[endgroup] 2025-09-07T08:33:16.0797468Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-09-07T08:33:16.0797809Z with: 2025-09-07T08:33:16.0798021Z s3-bucket: gha-artifacts 2025-09-07T08:33:16.0798351Z s3-prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:33:16.0798708Z retention-days: 14 2025-09-07T08:33:16.0798967Z if-no-files-found: warn 2025-09-07T08:33:16.0799232Z path: test-jsons-*.zip 2025-09-07T08:33:16.0799495Z name: artifact 2025-09-07T08:33:16.0799732Z region: us-east-1 2025-09-07T08:33:16.0799964Z env: 2025-09-07T08:33:16.0800165Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:16.0800631Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:16.0801211Z DEVICE_NAME: 2025-09-07T08:33:16.0801440Z DEVICE_TYPE: 2025-09-07T08:33:16.0801653Z ##[endgroup] 2025-09-07T08:33:16.4340191Z NOTE: s3-prefix specified, ignoring name parameter 2025-09-07T08:33:16.4340779Z With the provided path, there will be 1 file uploaded 2025-09-07T08:33:16.4341241Z Uploading to s3 prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:33:16.4479383Z Starting upload of test-jsons-test-dynamo_wrapped-1-3-linux.2xlarge_49774041707.zip 2025-09-07T08:33:16.5809585Z Finished upload of test-jsons-test-dynamo_wrapped-1-3-linux.2xlarge_49774041707.zip 2025-09-07T08:33:16.6009388Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-09-07T08:33:16.6009958Z with: 2025-09-07T08:33:16.6010337Z s3-bucket: gha-artifacts 2025-09-07T08:33:16.6010894Z s3-prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:33:16.6011516Z retention-days: 14 2025-09-07T08:33:16.6011988Z if-no-files-found: error 2025-09-07T08:33:16.6012514Z path: test-reports-*.zip 2025-09-07T08:33:16.6012981Z name: artifact 2025-09-07T08:33:16.6013357Z region: us-east-1 2025-09-07T08:33:16.6013737Z env: 2025-09-07T08:33:16.6014099Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:16.6014895Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:16.6015757Z DEVICE_NAME: 2025-09-07T08:33:16.6016132Z DEVICE_TYPE: 2025-09-07T08:33:16.6016542Z ##[endgroup] 2025-09-07T08:33:16.9236040Z NOTE: s3-prefix specified, ignoring name parameter 2025-09-07T08:33:16.9236670Z With the provided path, there will be 1 file uploaded 2025-09-07T08:33:16.9237135Z Uploading to s3 prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:33:16.9282322Z Starting upload of test-reports-test-dynamo_wrapped-1-3-linux.2xlarge_49774041707.zip 2025-09-07T08:33:17.0774773Z Finished upload of test-reports-test-dynamo_wrapped-1-3-linux.2xlarge_49774041707.zip 2025-09-07T08:33:17.0960665Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-09-07T08:33:17.0961027Z with: 2025-09-07T08:33:17.0961242Z s3-bucket: gha-artifacts 2025-09-07T08:33:17.0961580Z s3-prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:33:17.0962088Z retention-days: 14 2025-09-07T08:33:17.0962368Z if-no-files-found: ignore 2025-09-07T08:33:17.0962638Z path: logs-*.zip 2025-09-07T08:33:17.0962878Z name: artifact 2025-09-07T08:33:17.0963116Z region: us-east-1 2025-09-07T08:33:17.0963339Z env: 2025-09-07T08:33:17.0963558Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:17.0964025Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:17.0964652Z DEVICE_NAME: 2025-09-07T08:33:17.0964886Z DEVICE_TYPE: 2025-09-07T08:33:17.0965108Z ##[endgroup] 2025-09-07T08:33:17.4179777Z NOTE: s3-prefix specified, ignoring name parameter 2025-09-07T08:33:17.4180268Z With the provided path, there will be 1 file uploaded 2025-09-07T08:33:17.4180733Z Uploading to s3 prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:33:17.4225948Z Starting upload of logs-test-dynamo_wrapped-1-3-linux.2xlarge_49774041707.zip 2025-09-07T08:33:17.6196148Z Finished upload of logs-test-dynamo_wrapped-1-3-linux.2xlarge_49774041707.zip 2025-09-07T08:33:17.6383731Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-09-07T08:33:17.6384090Z with: 2025-09-07T08:33:17.6384323Z s3-bucket: gha-artifacts 2025-09-07T08:33:17.6384655Z s3-prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:33:17.6385004Z retention-days: 14 2025-09-07T08:33:17.6385267Z if-no-files-found: ignore 2025-09-07T08:33:17.6385547Z path: debug-*.zip 2025-09-07T08:33:17.6385784Z name: artifact 2025-09-07T08:33:17.6386006Z region: us-east-1 2025-09-07T08:33:17.6386237Z env: 2025-09-07T08:33:17.6386454Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:17.6386924Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:17.6387420Z DEVICE_NAME: 2025-09-07T08:33:17.6387651Z DEVICE_TYPE: 2025-09-07T08:33:17.6388004Z ##[endgroup] 2025-09-07T08:33:17.9653815Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-09-07T08:33:17.9845713Z ##[group]Run # shellcheck disable=SC2156 2025-09-07T08:33:17.9846124Z # shellcheck disable=SC2156 2025-09-07T08:33:17.9846725Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-09-07T08:33:17.9852510Z shell: /usr/bin/bash -e {0} 2025-09-07T08:33:17.9852787Z env: 2025-09-07T08:33:17.9853012Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:17.9853480Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:17.9853971Z DEVICE_NAME: 2025-09-07T08:33:17.9854204Z DEVICE_TYPE: 2025-09-07T08:33:17.9854433Z ##[endgroup] 2025-09-07T08:33:18.2936597Z Prepare all required actions 2025-09-07T08:33:18.2937261Z Getting action download info 2025-09-07T08:33:18.4021079Z ##[group]Run ./.github/actions/upload-utilization-stats 2025-09-07T08:33:18.4021454Z with: 2025-09-07T08:33:18.4021676Z job_id: 49774041707 2025-09-07T08:33:18.4022092Z job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T08:33:18.4022590Z workflow_name: pull 2025-09-07T08:33:18.4022839Z workflow_run_id: 17524754568 2025-09-07T08:33:18.4023125Z workflow_attempt: 1 2025-09-07T08:33:18.4023367Z env: 2025-09-07T08:33:18.4023585Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:18.4024035Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:18.4024537Z DEVICE_NAME: 2025-09-07T08:33:18.4024772Z DEVICE_TYPE: 2025-09-07T08:33:18.4024998Z ##[endgroup] 2025-09-07T08:33:18.4050089Z ##[group]Run echo "workflow_id: 17524754568" 2025-09-07T08:33:18.4050510Z echo "workflow_id: 17524754568" 2025-09-07T08:33:18.4050851Z echo "workflow_attempt: 1" 2025-09-07T08:33:18.4051168Z echo "workflow_Name: pull" 2025-09-07T08:33:18.4051485Z echo "job_id: 49774041707" 2025-09-07T08:33:18.4052025Z echo "job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge)" 2025-09-07T08:33:18.4052578Z echo "artifact_prefix: " 2025-09-07T08:33:18.4052889Z python3 --version 2025-09-07T08:33:18.4059218Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:18.4059609Z env: 2025-09-07T08:33:18.4059830Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:18.4060287Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:18.4060795Z DEVICE_NAME: 2025-09-07T08:33:18.4061028Z DEVICE_TYPE: 2025-09-07T08:33:18.4061254Z ##[endgroup] 2025-09-07T08:33:18.4084921Z workflow_id: 17524754568 2025-09-07T08:33:18.4085230Z workflow_attempt: 1 2025-09-07T08:33:18.4085486Z workflow_Name: pull 2025-09-07T08:33:18.4085725Z job_id: 49774041707 2025-09-07T08:33:18.4086142Z job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2025-09-07T08:33:18.4086628Z artifact_prefix: 2025-09-07T08:33:18.4097769Z Python 3.9.23 2025-09-07T08:33:18.4138544Z ##[group]Run nick-fields/retry@v3.0.0 2025-09-07T08:33:18.4138846Z with: 2025-09-07T08:33:18.4139139Z shell: bash 2025-09-07T08:33:18.4139372Z timeout_minutes: 5 2025-09-07T08:33:18.4139622Z max_attempts: 5 2025-09-07T08:33:18.4139852Z retry_wait_seconds: 30 2025-09-07T08:33:18.4140419Z command: set -eu python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3 dataclasses_json==0.6.7 2025-09-07T08:33:18.4141032Z polling_interval_seconds: 1 2025-09-07T08:33:18.4141325Z warning_on_retry: true 2025-09-07T08:33:18.4141581Z continue_on_error: false 2025-09-07T08:33:18.4141843Z env: 2025-09-07T08:33:18.4142058Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:18.4142536Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:18.4143028Z DEVICE_NAME: 2025-09-07T08:33:18.4143263Z DEVICE_TYPE: 2025-09-07T08:33:18.4143555Z ##[endgroup] 2025-09-07T08:33:18.7473459Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T08:33:18.8367243Z Collecting python-dateutil==2.8.2 2025-09-07T08:33:18.8528979Z Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) 2025-09-07T08:33:19.8428376Z Collecting boto3==1.35.42 2025-09-07T08:33:19.8469608Z Downloading boto3-1.35.42-py3-none-any.whl (139 kB) 2025-09-07T08:33:20.3820317Z Collecting pandas==2.1.3 2025-09-07T08:33:20.3860837Z Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) 2025-09-07T08:33:20.5185631Z Requirement already satisfied: dataclasses_json==0.6.7 in /home/ec2-user/.local/lib/python3.9/site-packages (0.6.7) 2025-09-07T08:33:20.5205139Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0) 2025-09-07T08:33:20.5252127Z Requirement already satisfied: botocore<1.36.0,>=1.35.42 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (1.35.99) 2025-09-07T08:33:20.5258764Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.4) 2025-09-07T08:33:20.5263999Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.0) 2025-09-07T08:33:21.4282839Z Collecting numpy<2,>=1.22.4 2025-09-07T08:33:21.4326985Z Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB) 2025-09-07T08:33:21.6252430Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1) 2025-09-07T08:33:21.6599248Z Collecting tzdata>=2022.1 2025-09-07T08:33:21.6636866Z Downloading tzdata-2025.2-py2.py3-none-any.whl (347 kB) 2025-09-07T08:33:21.6764454Z Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (3.26.1) 2025-09-07T08:33:21.6768491Z Requirement already satisfied: typing-inspect<1,>=0.4.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (0.9.0) 2025-09-07T08:33:21.6857722Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.42->boto3==1.35.42) (1.25.10) 2025-09-07T08:33:21.6949979Z Requirement already satisfied: packaging>=17.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses_json==0.6.7) (25.0) 2025-09-07T08:33:21.7052819Z Requirement already satisfied: typing-extensions>=3.7.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (4.15.0) 2025-09-07T08:33:21.7057213Z Requirement already satisfied: mypy-extensions>=0.3.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (1.1.0) 2025-09-07T08:33:21.8699906Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3 2025-09-07T08:33:26.8382401Z Attempting uninstall: boto3 2025-09-07T08:33:26.8383347Z Found existing installation: boto3 1.35.33 2025-09-07T08:33:26.8484267Z Uninstalling boto3-1.35.33: 2025-09-07T08:33:26.8498251Z Successfully uninstalled boto3-1.35.33 2025-09-07T08:33:26.9037827Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.2 2025-09-07T08:33:27.4976985Z Command completed after 1 attempt(s). 2025-09-07T08:33:27.5029141Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-09-07T08:33:27.5029874Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-09-07T08:33:27.5030405Z  --workflow-run-id "17524754568" \ 2025-09-07T08:33:27.5030779Z  --workflow-name "pull" \ 2025-09-07T08:33:27.5031106Z  --workflow-run-attempt "1" \ 2025-09-07T08:33:27.5031443Z  --job-id "49774041707" \ 2025-09-07T08:33:27.5031963Z  --job-name "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge)" \ 2025-09-07T08:33:27.5032586Z  --local-path "" \ 2025-09-07T08:33:27.5032877Z  --artifact-prefix "" 2025-09-07T08:33:27.5038540Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:27.5038933Z env: 2025-09-07T08:33:27.5039166Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:27.5039626Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:27.5040141Z DEVICE_NAME: 2025-09-07T08:33:27.5040380Z DEVICE_TYPE: 2025-09-07T08:33:27.5040615Z ##[endgroup] 2025-09-07T08:33:29.6124287Z repo: pytorch/pytorch 2025-09-07T08:33:29.6125084Z Search for test log in s3 bucket: ossci-utilization 2025-09-07T08:33:29.6126479Z Downloading logs-test-dynamo_wrapped-1-3-linux.2xlarge_49774041707.zip 2025-09-07T08:33:29.6127849Z extracting usage_log.txt from zip file logs-test-dynamo_wrapped-1-3-linux.2xlarge_49774041707.zip 2025-09-07T08:33:29.6129016Z Converted Log Model: UtilizationMetadata: 2025-09-07T08:33:29.6131775Z UtilizationMetadata(level='metadata', workflow_id='17524754568', job_id='49774041707', workflow_name='pull', job_name='linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge)', usage_collect_interval=1.0, data_model_version=1.5, start_at=1757226600, gpu_count=0, cpu_count=8, gpu_type=None, error=None) 2025-09-07T08:33:29.6134733Z [Db Segments] detected pytest cmd: 11, generated segments: 11 2025-09-07T08:33:29.6135543Z [db model] Peek db timeseries 2025-09-07T08:33:29.6136096Z :{ 2025-09-07T08:33:29.6136519Z "created_at": 1757234009, 2025-09-07T08:33:29.6137083Z "type": "utilization", 2025-09-07T08:33:29.6137582Z "tags": [ 2025-09-07T08:33:29.6138011Z "record" 2025-09-07T08:33:29.6138454Z ], 2025-09-07T08:33:29.6138900Z "time_stamp": 1757226600, 2025-09-07T08:33:29.6139462Z "repo": "pytorch/pytorch", 2025-09-07T08:33:29.6140037Z "workflow_id": 17524754568, 2025-09-07T08:33:29.6140603Z "run_attempt": 1, 2025-09-07T08:33:29.6141099Z "job_id": 49774041707, 2025-09-07T08:33:29.6141620Z "workflow_name": "pull", 2025-09-07T08:33:29.6142520Z "job_name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, linux.2xlarge)", 2025-09-07T08:33:29.6143506Z "json_data": "{}" 2025-09-07T08:33:29.6143983Z } 2025-09-07T08:33:29.6144980Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.5/pytorch/pytorch/17524754568/1/49774041707/metadata 2025-09-07T08:33:29.6146878Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.5/pytorch/pytorch/17524754568/1/49774041707/metadata 2025-09-07T08:33:29.6148864Z Writing 1474 documents to S3 ossci-utilization/util_timeseries/v_1.5/pytorch/pytorch/17524754568/1/49774041707/time_series 2025-09-07T08:33:29.6150922Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.5/pytorch/pytorch/17524754568/1/49774041707/time_series 2025-09-07T08:33:29.7171336Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-09-07T08:33:29.7171799Z with: 2025-09-07T08:33:29.7172017Z env: 2025-09-07T08:33:29.7172227Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:29.7172706Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:29.7173495Z DEVICE_NAME: 2025-09-07T08:33:29.7173737Z DEVICE_TYPE: 2025-09-07T08:33:29.7173957Z ##[endgroup] 2025-09-07T08:33:29.7193314Z ##[group]Run set -eou pipefail 2025-09-07T08:33:29.7193756Z set -eou pipefail 2025-09-07T08:33:29.7194029Z  2025-09-07T08:33:29.7194406Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-09-07T08:33:29.7194874Z for _ in $(seq 1440); do 2025-09-07T08:33:29.7195219Z  # Break if no ssh session exists anymore 2025-09-07T08:33:29.7195583Z  if [ "$(who)" = "" ]; then 2025-09-07T08:33:29.7195893Z  break 2025-09-07T08:33:29.7196189Z  fi 2025-09-07T08:33:29.7196510Z  echo "." 2025-09-07T08:33:29.7196753Z  sleep 5 2025-09-07T08:33:29.7197000Z done 2025-09-07T08:33:29.7202474Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:29.7202871Z env: 2025-09-07T08:33:29.7203087Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:29.7203561Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:29.7204072Z DEVICE_NAME: 2025-09-07T08:33:29.7204403Z DEVICE_TYPE: 2025-09-07T08:33:29.7204626Z ##[endgroup] 2025-09-07T08:33:29.7228447Z Holding runner for 2 hours until all ssh sessions have logged out 2025-09-07T08:33:29.7304644Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T08:33:29.7305235Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T08:33:29.7305685Z # shellcheck disable=SC2046 2025-09-07T08:33:29.7306044Z docker stop $(docker ps -q) || true 2025-09-07T08:33:29.7306386Z # Prune all of the docker images 2025-09-07T08:33:29.7306726Z docker system prune -af 2025-09-07T08:33:29.7311982Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:29.7312378Z env: 2025-09-07T08:33:29.7312591Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:33:29.7313062Z DOCKER_CONTAINER_ID: 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:29.7313577Z DEVICE_NAME: 2025-09-07T08:33:29.7313809Z DEVICE_TYPE: 2025-09-07T08:33:29.7314035Z ##[endgroup] 2025-09-07T08:33:40.7000653Z 1383124d873a 2025-09-07T08:33:41.3857509Z Deleted Containers: 2025-09-07T08:33:41.3857980Z 1383124d873ae4411bc6450241dbf43a5e029ffc886bc13694a68825bd27f834 2025-09-07T08:33:41.3858325Z 2025-09-07T08:33:47.6471856Z Deleted Images: 2025-09-07T08:33:47.6472804Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T08:33:47.6474441Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image@sha256:91b756ef2bd4983cfdb00ea40fdbe492b4a29b8a15826aa577c257c337fe768e 2025-09-07T08:33:47.6475541Z deleted: sha256:d68dea278b660b539496dbad92d5230006940a736c3a0dcc39d6f72863a5aaa0 2025-09-07T08:33:47.6476295Z deleted: sha256:466a9a29a31fe6c744ee15be6cf8a8ee7bb4fb23f252e11fc5e1c4d92349f196 2025-09-07T08:33:47.6477024Z deleted: sha256:d6d613330769ed32cd2356a3b10f7f89b5c6a3330dd56e7aac31d1cfff254bef 2025-09-07T08:33:47.6477802Z deleted: sha256:c1c5ece321cdcac0e059f727e117bd108baf18bc8e6362e7d502cadc9bd6cd6b 2025-09-07T08:33:47.6478541Z deleted: sha256:40a748da4ab83430b43ee885861c8df1e280b4fb883cbbd2ecc0c4db0cd7e2b8 2025-09-07T08:33:47.6479258Z deleted: sha256:1699c000e06a248dc93621fd4755694e7709dfd8c2735e507ffe9c326c5827d7 2025-09-07T08:33:47.6479922Z deleted: sha256:856655db136c6c6b0b428e2840143642f49d20a083ed479de40587d78ab4a341 2025-09-07T08:33:47.6480901Z deleted: 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2025-09-07T08:33:47.7987823Z git version 2.47.1 2025-09-07T08:33:47.8024242Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/a7d31c24-e6c9-4b85-a050-d6171a0d71d2/.gitconfig' 2025-09-07T08:33:47.8032681Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/a7d31c24-e6c9-4b85-a050-d6171a0d71d2' before making global git config changes 2025-09-07T08:33:47.8033691Z Adding repository directory to the temporary git global config as a safe directory 2025-09-07T08:33:47.8037605Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T08:33:47.8083389Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-07T08:33:47.8121636Z [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' || :" 2025-09-07T08:33:47.8529517Z Entering 'android/libs/fbjni' 2025-09-07T08:33:47.8586448Z Entering 'third_party/FP16' 2025-09-07T08:33:47.8640506Z Entering 'third_party/FXdiv' 2025-09-07T08:33:47.8695122Z Entering 'third_party/NNPACK' 2025-09-07T08:33:47.8751390Z Entering 'third_party/NVTX' 2025-09-07T08:33:47.8806974Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T08:33:47.8862575Z Entering 'third_party/XNNPACK' 2025-09-07T08:33:47.8932974Z Entering 'third_party/aiter' 2025-09-07T08:33:47.8990927Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T08:33:47.9055444Z Entering 'third_party/benchmark' 2025-09-07T08:33:47.9109482Z Entering 'third_party/composable_kernel' 2025-09-07T08:33:47.9176648Z Entering 'third_party/cpp-httplib' 2025-09-07T08:33:47.9234079Z Entering 'third_party/cpuinfo' 2025-09-07T08:33:47.9289490Z Entering 'third_party/cudnn_frontend' 2025-09-07T08:33:47.9343504Z Entering 'third_party/cutlass' 2025-09-07T08:33:47.9408610Z Entering 'third_party/fbgemm' 2025-09-07T08:33:47.9464464Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T08:33:47.9521034Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T08:33:47.9580535Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T08:33:47.9634405Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T08:33:47.9697260Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T08:33:47.9753373Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T08:33:47.9806919Z Entering 'third_party/fbgemm/external/json' 2025-09-07T08:33:47.9862098Z Entering 'third_party/flash-attention' 2025-09-07T08:33:47.9917209Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T08:33:47.9976800Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T08:33:48.0044213Z Entering 'third_party/flatbuffers' 2025-09-07T08:33:48.0105086Z Entering 'third_party/fmt' 2025-09-07T08:33:48.0160948Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T08:33:48.0217988Z Entering 'third_party/gloo' 2025-09-07T08:33:48.0274945Z Entering 'third_party/googletest' 2025-09-07T08:33:48.0329738Z Entering 'third_party/ideep' 2025-09-07T08:33:48.0383759Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T08:33:48.0445347Z Entering 'third_party/ittapi' 2025-09-07T08:33:48.0500392Z Entering 'third_party/kineto' 2025-09-07T08:33:48.0555932Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T08:33:48.0610384Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T08:33:48.0665241Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T08:33:48.0718664Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T08:33:48.0774149Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T08:33:48.0828621Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T08:33:48.0884763Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T08:33:48.0939231Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T08:33:48.0992956Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T08:33:48.1049820Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T08:33:48.1105943Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T08:33:48.1159279Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T08:33:48.1216798Z Entering 'third_party/kleidiai' 2025-09-07T08:33:48.1272201Z Entering 'third_party/mimalloc' 2025-09-07T08:33:48.1328513Z Entering 'third_party/nlohmann' 2025-09-07T08:33:48.1387865Z Entering 'third_party/onnx' 2025-09-07T08:33:48.1459951Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T08:33:48.1517142Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T08:33:48.1578403Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T08:33:48.1633518Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T08:33:48.1686943Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T08:33:48.1740519Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T08:33:48.1796625Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T08:33:48.1849936Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T08:33:48.1903280Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T08:33:48.1956100Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T08:33:48.2011822Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T08:33:48.2066697Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T08:33:48.2141642Z Entering 'third_party/pocketfft' 2025-09-07T08:33:48.2196852Z Entering 'third_party/protobuf' 2025-09-07T08:33:48.2256382Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T08:33:48.2310809Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T08:33:48.2367493Z Entering 'third_party/psimd' 2025-09-07T08:33:48.2423635Z Entering 'third_party/pthreadpool' 2025-09-07T08:33:48.2480854Z Entering 'third_party/pybind11' 2025-09-07T08:33:48.2536033Z Entering 'third_party/python-peachpy' 2025-09-07T08:33:48.2589731Z Entering 'third_party/sleef' 2025-09-07T08:33:48.2644512Z Entering 'third_party/tensorpipe' 2025-09-07T08:33:48.2704014Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T08:33:48.2760649Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T08:33:48.2817614Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T08:33:48.2872597Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T08:33:48.2925856Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T08:33:48.3003453Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-07T08:33:48.3024228Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3034513Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-09-07T08:33:48.3064991Z [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' || :" 2025-09-07T08:33:48.3357169Z Entering 'android/libs/fbjni' 2025-09-07T08:33:48.3396282Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3431076Z Entering 'third_party/FP16' 2025-09-07T08:33:48.3467762Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3502158Z Entering 'third_party/FXdiv' 2025-09-07T08:33:48.3539748Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3572548Z Entering 'third_party/NNPACK' 2025-09-07T08:33:48.3610203Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3642945Z Entering 'third_party/NVTX' 2025-09-07T08:33:48.3680793Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3715032Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T08:33:48.3752208Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3786549Z Entering 'third_party/XNNPACK' 2025-09-07T08:33:48.3824154Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3871736Z Entering 'third_party/aiter' 2025-09-07T08:33:48.3909790Z http.https://github.com/.extraheader 2025-09-07T08:33:48.3944619Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T08:33:48.3982567Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4025187Z Entering 'third_party/benchmark' 2025-09-07T08:33:48.4061862Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4097200Z Entering 'third_party/composable_kernel' 2025-09-07T08:33:48.4134244Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4176080Z Entering 'third_party/cpp-httplib' 2025-09-07T08:33:48.4212851Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4246326Z Entering 'third_party/cpuinfo' 2025-09-07T08:33:48.4283459Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4317331Z Entering 'third_party/cudnn_frontend' 2025-09-07T08:33:48.4354175Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4389077Z Entering 'third_party/cutlass' 2025-09-07T08:33:48.4425822Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4468220Z Entering 'third_party/fbgemm' 2025-09-07T08:33:48.4506461Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4545491Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T08:33:48.4582850Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4615737Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T08:33:48.4651123Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4692359Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T08:33:48.4727930Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4762143Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T08:33:48.4799388Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4841347Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T08:33:48.4877821Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4911080Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T08:33:48.4947036Z http.https://github.com/.extraheader 2025-09-07T08:33:48.4979486Z Entering 'third_party/fbgemm/external/json' 2025-09-07T08:33:48.5017152Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5053572Z Entering 'third_party/flash-attention' 2025-09-07T08:33:48.5090495Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5124574Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T08:33:48.5159997Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5199162Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T08:33:48.5234371Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5280911Z Entering 'third_party/flatbuffers' 2025-09-07T08:33:48.5317500Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5353713Z Entering 'third_party/fmt' 2025-09-07T08:33:48.5391021Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5423813Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T08:33:48.5460190Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5493986Z Entering 'third_party/gloo' 2025-09-07T08:33:48.5535649Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5570655Z Entering 'third_party/googletest' 2025-09-07T08:33:48.5611085Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5643948Z Entering 'third_party/ideep' 2025-09-07T08:33:48.5681826Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5714286Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T08:33:48.5749705Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5791948Z Entering 'third_party/ittapi' 2025-09-07T08:33:48.5828181Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5861064Z Entering 'third_party/kineto' 2025-09-07T08:33:48.5897375Z http.https://github.com/.extraheader 2025-09-07T08:33:48.5930477Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T08:33:48.5966915Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6000968Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T08:33:48.6039001Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6072927Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T08:33:48.6110929Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6144442Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T08:33:48.6182179Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6215821Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T08:33:48.6252176Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6287299Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T08:33:48.6324084Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6359590Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T08:33:48.6395908Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6429996Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T08:33:48.6465550Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6499551Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T08:33:48.6536948Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6571071Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T08:33:48.6608686Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6643143Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T08:33:48.6679552Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6712523Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T08:33:48.6748594Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6783804Z Entering 'third_party/kleidiai' 2025-09-07T08:33:48.6822062Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6856301Z Entering 'third_party/mimalloc' 2025-09-07T08:33:48.6894613Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6928451Z Entering 'third_party/nlohmann' 2025-09-07T08:33:48.6965082Z http.https://github.com/.extraheader 2025-09-07T08:33:48.6999150Z Entering 'third_party/onnx' 2025-09-07T08:33:48.7037039Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7088205Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T08:33:48.7124696Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7159333Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T08:33:48.7196540Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7231492Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T08:33:48.7267531Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7300578Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T08:33:48.7336021Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7369290Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T08:33:48.7404854Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7437739Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T08:33:48.7473794Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7507836Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T08:33:48.7546881Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7580940Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T08:33:48.7617285Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7650616Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T08:33:48.7687375Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7720156Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T08:33:48.7757281Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7795716Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T08:33:48.7833525Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7869030Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T08:33:48.7906064Z http.https://github.com/.extraheader 2025-09-07T08:33:48.7959644Z Entering 'third_party/pocketfft' 2025-09-07T08:33:48.7997717Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8030890Z Entering 'third_party/protobuf' 2025-09-07T08:33:48.8068373Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8104756Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T08:33:48.8140930Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8173477Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T08:33:48.8209119Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8243696Z Entering 'third_party/psimd' 2025-09-07T08:33:48.8281268Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8314690Z Entering 'third_party/pthreadpool' 2025-09-07T08:33:48.8351218Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8385198Z Entering 'third_party/pybind11' 2025-09-07T08:33:48.8421554Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8455758Z Entering 'third_party/python-peachpy' 2025-09-07T08:33:48.8493537Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8527239Z Entering 'third_party/sleef' 2025-09-07T08:33:48.8566209Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8599672Z Entering 'third_party/tensorpipe' 2025-09-07T08:33:48.8637345Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8672404Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T08:33:48.8710431Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8744702Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T08:33:48.8781314Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8815963Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T08:33:48.8852430Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8886584Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T08:33:48.8922356Z http.https://github.com/.extraheader 2025-09-07T08:33:48.8955902Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T08:33:48.8993844Z http.https://github.com/.extraheader 2025-09-07T08:33:48.9145363Z A job completed hook has been configured by the self-hosted runner administrator 2025-09-07T08:33:48.9180309Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-09-07T08:33:48.9185633Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:33:48.9186040Z ##[endgroup] 2025-09-07T08:33:58.0448253Z Cleaning up orphan processes